CN117425614A - Diagnostic system - Google Patents

Abstract

The present invention provides a diagnostic system for diagnosing abnormality of a passenger conveyor that conveys passengers by circulating a plurality of steps connected in a loop, the diagnostic system comprising: a step for diagnosis, which is included in the steps and has at least 1 or more diagnostic sensors including a magnetic sensor; a magnetic distribution map generation unit configured to generate a magnetic distribution map indicating a correlation between a position in the passenger conveyor during one cycle of the step cycle for diagnosis and a detection value of the magnetic sensor; a position estimating unit that estimates a position of the passenger conveyor at which the detection value of the diagnostic sensor is detected, based on the magnetic distribution map and the detection value of the magnetic sensor during the cyclic movement; and an abnormality diagnosis unit that performs abnormality diagnosis based on the detection value of the diagnosis sensor.

Description

Diagnostic system

Technical Field

The present invention relates to a diagnostic system for a passenger conveyor.

Background

Patent document 1 describes a configuration in which a sensor is mounted in an inner space of a step for diagnosis, and measurement of the inside of an escalator is performed by circulating the step for diagnosis in the escalator. For example, the regular step is replaced with a diagnostic step at the time of regular inspection, thereby improving the efficiency of regular inspection. Since the step for diagnosis circulates in the escalator, it is necessary to make the timing of obtaining the data correspond to the position of the step for diagnosis at that time. In patent document 1, a timing at which a diagnosis step is pulled to the rear side of an escalator and reversed is acquired using an acceleration sensor, and a step position is estimated from an elapsed time from the reversal.

Prior art literature

Patent literature

Patent document 1: japanese patent laid-open No. 2006-76729

Disclosure of Invention

Technical problem to be solved by the invention

However, the technique described in patent document 1 is not applicable to, for example, an escalator or the like that reduces the operation speed and saves power when no passenger is present, because it is premised on the constant speed operation of the escalator.

Technical means for solving the technical problems

A diagnostic system according to an aspect of the present invention is a diagnostic system for diagnosing an abnormality of a passenger conveyor that conveys a passenger by circulating a plurality of steps connected in a loop, the diagnostic system including: a step for diagnosis, which is included in the steps and has at least 1 or more diagnostic sensors including a magnetic sensor; a magnetic distribution map generation unit configured to generate a magnetic distribution map (magnetic map) indicating a correlation between a position in the passenger conveyor during one cycle of the step cycle for diagnosis and a detection value of the magnetic sensor; a position estimating unit configured to estimate a position of the passenger conveyor at which the detection value of the diagnostic sensor is detected, based on the magnetic distribution map and the detection value of the magnetic sensor during the cyclic movement; and an abnormality diagnosis unit that performs abnormality diagnosis based on the detection value of the diagnosis sensor.

ADVANTAGEOUS EFFECTS OF INVENTION

According to the present invention, even in a situation where the operation speed is changed, the detection position of the detection value of the diagnostic sensor can be estimated with high accuracy.

Drawings

Fig. 1 is a schematic view showing a schematic structure of an escalator.

Fig. 2 is a perspective view showing a schematic configuration of the step for diagnosis.

FIG. 3 is a functional block diagram of a diagnostic system.

Fig. 4 is a diagram illustrating an example of the noise reduction process.

Fig. 5 is a diagram illustrating an example of positioning of the diagnostic step.

Fig. 6 is a diagram showing an example of time-series (time-series) magnetic detection data after the noise reduction process.

Fig. 7 is a diagram illustrating a second method of position correspondence.

Fig. 8 is a diagram illustrating an example of the self-position estimation process.

Fig. 9 is a flowchart showing an example of the abnormality detection operation.

Fig. 10 is a diagram illustrating an example of abnormality occurrence determination and abnormality precursor determination.

Fig. 11 is a block diagram showing a modification 1 of the structure of the diagnostic system.

Fig. 12 is a block diagram showing modification 2 of the structure of the diagnostic system.

Fig. 13 is a block diagram showing modification 3 of the structure of the diagnostic system.

Fig. 14 is a block diagram showing a modification 4 of the structure of the diagnostic system.

Fig. 15 is a block diagram showing a modification 5 of the structure of the diagnostic system.

Fig. 16 is a block diagram showing modification 6 of the structure of the diagnostic system.

Fig. 17 is a block diagram showing modification 7 of the structure of the diagnostic system.

Detailed Description

Hereinafter, embodiments of the present invention will be described with reference to the drawings. The embodiments are examples for illustrating the present invention, and are omitted and simplified as appropriate for clarity of illustration. The invention can also be implemented in other various ways. The constituent elements may be single or plural, as long as they are not particularly limited. The positions, sizes, shapes, ranges, etc. of the respective constituent elements shown in the drawings may not be actual positions, sizes, shapes, ranges, etc. in order to facilitate understanding of the present invention. Therefore, the present invention is not limited to the positions, sizes, shapes, ranges, and the like described in the drawings. When there are a plurality of constituent elements having the same or similar functions, the same reference numerals may be given different suffixes. In addition, when it is not necessary to distinguish between the plurality of components, a description of the suffix may be omitted.

Fig. 1 is a diagram showing an example of a passenger conveyor as a diagnosis target of the diagnosis system of the present invention. In the present embodiment, the passenger conveyor is an escalator, and fig. 1 is a schematic diagram showing a schematic configuration of an escalator 100. The escalator 100 includes steps 1, 1a, a chain 2, a housing frame 3, a final gear 4, a drive motor 6, a lower final gear 9, handrails 8, guide rails 11a, 11b, a control device 12, an upper landing floor 13a, a lower landing floor 13b, and the like. The terminal gear 4, the drive motor 6, the lower terminal gear 9, the control device 12, and the like are provided in the housing frame 3.

In the escalator 100, a plurality of steps 1 and 1 step 1a for diagnosis are connected in a loop by a loop-shaped chain 2. The escalator 100 conveys passengers by circulating a plurality of steps 1 and diagnostic steps 1a connected in a loop. In fig. 1, hatching is applied to the diagnostic step 1 a. The upper landing floor 13a is a steel plate constituting the floor of the upper landing of the escalator 100. The lower entrance floor 13b is a steel plate that forms the floor of the lower entrance of the escalator 100.

In the case frame 3 below the upper entrance floor 13a, a terminal gear 4 engaged with the chain 2 is provided. When the chain 2 is driven by the final gear 4, the steps 1 and 1a connected to the chain 2 are circulated between the upper entrance floor 13a and the lower entrance floor 13 b. In addition, the handrail 8 circulates in synchronization with the chain 2. The final gear 4 is driven by a drive motor 6 having a drive gear 5. The drive motor 6 is controlled by a control device 12. A drive chain 7 is provided between the drive gear 5 and the final gear 4. In the case frame 3 below the lower entrance floor 13b, a lower final gear 9 is provided. The chain 2 meshes with the lower final gear 9.

The side portions of the steps 1, 1a are formed in a substantially fan shape. In each step 1, 1a, a front guide roller 10a and a rear guide roller 10b are provided. The front guide roller 10a and the rear guide roller 10b are provided in a pair on the left and right sides of each step 1, 1a, i.e., in the forward and backward directions of the paper surface. In the housing frame 3, a guide rail 11a on which the front guide roller 10a runs and a guide rail 11b on which the rear guide roller 10b runs are provided. The guide rails 11a, 11b are provided in a pair in the upper and lower directions in the housing frame 3.

When the steps 1, 1a move from the lower final gear 9 to the final gear 4, the front guide roller 10a and the rear guide roller 10b travel on the upper guide rails 11a, 11b. The steps 1, 1a reaching the final gear 4 move along the final gear 4. When the steps 1, 1a move along the final gear 4 from the upper side of the drawing of the final gear 4 to the lower side of the drawing, the posture of the steps 1, 1a is reversed. The front guide roller 10a and the rear guide roller 10b of the reversed steps 1, 1a are transferred to the lower guide rails 11a, 11b. When the steps 1, 1a move from the final gear 4 to the lower final gear 9, the front guide roller 10a and the rear guide roller 10b run on the lower guide rails 11a, 11b. The steps 1, 1a reaching the lower final gear 9 move along the lower final gear 9, and the posture is reversed again. The front guide roller 10a and the rear guide roller 10b of the reversed steps 1, 1a are transferred to the upper guide rails 11a, 11b.

Fig. 2 is a perspective view showing a schematic structure of the diagnostic step 1 a. The diagnostic step 1a has a tread portion (tread portion) 21 on which a passenger rides, a kick plate portion 22 continuous with the tread portion 21, and a pair of side portions 23. In each side surface portion 23, a front guide roller 10a and a rear guide roller 10b are provided. In the diagnostic step 1a, a sensor terminal 24 is provided. In the sensor terminal 24, a sensor unit 25, a control unit 26, and a wireless communication unit 27 are provided. The step 1 is also similar in structure to the step 1a for diagnosis, but is different from the step 1a for diagnosis in that the sensor terminal 24 is not included.

Fig. 3 is a functional block diagram showing a functional configuration of the diagnostic system according to the present embodiment. The diagnostic system 1000 includes: a position estimating device 51 provided in the step 1a for diagnosis and the control device 12 of the escalator 100, a monitoring center 50 for performing diagnosis processing remotely, and an abnormality diagnosing device 52 and a communication device 53. The data collection device 30 transmits and receives data to and from the monitoring center 50 via the network 40.

As described above, the sensor terminals 24 are provided in the diagnostic step 1 a. The sensor terminal 24 includes a sensor unit 25, a control unit 26, and a wireless communication unit 27. The sensor unit 25 includes a magnetic sensor 251, a sound sensor 252, and an acceleration sensor 253 as diagnostic sensors. In the control device 12 provided in the housing frame 3 of the escalator 100, a data collection device 30 including a data storage portion 301 and a communication portion 302 is provided. The magnetic detection data of the magnetic sensor 251, the sound detection data of the sound sensor 252, and the acceleration detection data of the acceleration sensor 253 are transmitted to the data collection device 30 through the wireless communication section 27. The wireless communication unit 27 uses short-range wireless with low power consumption and low cost. The battery is used as a power source for the sensor terminal 24, and battery replacement is performed if necessary, for example, at the time of periodic inspection.

As a method of data collection, for example, time series detection data during one rotation of the diagnostic step 1a is temporarily stored in a memory (not shown) included in the control unit 26. Then, at the time when the multi-turn amount of time series detection data is stored in the memory, the multi-turn amount of time series detection data is transmitted through the wireless communication unit 27. The time series detection data of the multi-turn amount is hereinafter referred to as a time series detection data group. The data transmission by the wireless communication unit 27 is repeated at predetermined time intervals. The data collection device 30 stores the time series detection data group received through the communication section 302 in the data storage section 301.

The plurality of time series detection data sets stored in the data storage unit 301 are transmitted to the monitoring center 50 at predetermined intervals. The transmission is performed in accordance with a transmission instruction from the monitoring center 50. For example, every 24 hours, a transmission instruction of the time series detection data group is output from the monitoring center 50. When the data collection device 30 receives the transmission instruction from the monitoring center 50, it transmits the time series detection data group of 24 hours stored in the data storage unit 301 to the monitoring center 50. The plurality of time series detection data sets transmitted from the communication unit 302 are received by the communication device 53 of the monitoring center 50 via the network 40.

The monitoring center 50 includes a position estimating device 51, an abnormality diagnosing device 52, and a communicating device 53. The position estimation device 51 includes a magnetic profile generation unit 511, a self-position estimation unit 512, a noise reduction unit 513, and a storage unit 514. The noise reduction unit 513 performs a process of reducing noise included in time series detection data (magnetic detection data, sound detection data, and acceleration detection data) used when generating the magnetic distribution map. Details of the noise reduction processing are described later.

The magnetic distribution map generating unit 511 generates a magnetic distribution map based on the time-series magnetic detection data after the noise reduction processing. Specifically, for the time-series magnetic detection data of the amount of 1 turn, a magnetic distribution map is generated by associating (relating) the detection value at each time with the position in the escalator 100. The magnetic distribution map generation process is described later. The magnetic pattern generation process is performed as an initial operation when the escalator 100 is provided with the diagnostic steps 1 a. The generated magnetic distribution map is stored in the storage unit 514.

The self-position estimating unit 512 associates each detection value of the time-series sound detection data and the time-series acceleration detection data for abnormality diagnosis with each position of the escalator 100 based on the time-series magnetic detection data and the magnetic distribution map, as described later. By performing the self-position estimation process, it is possible to know at which position of the escalator 100 the sound detection data and the acceleration detection data detected simultaneously with the specific data of the magnetic detection data are detected. Details of the self-position estimation processing by the self-position estimation unit 512 will be described later.

The abnormality diagnostic device 52 performs abnormality diagnosis of the escalator 100 based on the magnetic distribution map and the detection data of the diagnostic sensor. The abnormality diagnostic device 52 includes an abnormality estimation unit 521, a noise reduction unit 522, a storage unit 523, and a reporting unit 524. The abnormality estimation unit 521 performs abnormality estimation processing described later based on the sound detection data after the self-position estimation processing performed by the self-position estimation unit 512. The noise reduction unit 522 performs noise reduction processing on the time series detection data set for abnormality diagnosis. The reporting unit 524 performs reporting operation when the abnormality estimation unit 521 determines that the vehicle is abnormal. Upon receiving the abnormality report based on the report operation, the operator performs the inspection operation of the escalator 100. The storage unit 523 stores judgment reference data for abnormality diagnosis, a time series detection data set acquired from the data collection device 30, and the like.

(noise reduction treatment)

Fig. 4 is a diagram illustrating an example of the noise reduction processing of the noise reduction units 513 and 522. Here, the time-series magnetic detection data included in the time-series detection data group will be described as an example. Fig. 4 shows time-series magnetic detection data in the case where the diagnostic step 1a is rotated at a constant speed. In fig. 4, the vertical axis represents the detection value, and the horizontal axis represents time. The time-series magnetic detection data D1 is the data of the 1 st turn, the time-series magnetic detection data D2 is the data of the 2 nd turn, and the time-series magnetic detection data D3 is the data of the 3 rd turn. The time-series magnetic detection data D2 is shown shifted by Δ along the vertical axis from the time-series magnetic detection data D1. Likewise, the time-series magnetic detection data D3 is shown shifted by Δ along the vertical axis from the time-series magnetic detection data D2. T is the time required for a cyclic movement of 1 turn, i.e. the period.

In the example shown in fig. 4, noise n1 is generated in the time-series magnetic detection data D1, noise n2 is generated in the time-series magnetic detection data D2, and noise n3 is generated in the time-series magnetic detection data D3. The noise reduction units 513 and 522 average the detection values of the same time of the time-series magnetic detection data D1 to D3 for a plurality of turns, for example, to reduce the detection values of the portions where the respective noises n1 to n3 are generated. In the case of performing the averaging process using the time-series magnetic detection data of the amount of 10 turns, if noise is generated in only the amount of 1 turn, the detection value of the noise portion is reduced to 1/10 by the averaging process. The averaged time-series magnetic detection data is used as the noise-reduction-processed time-series magnetic detection data. The noise reduction processing of the time-series sound detection data and the time-series acceleration detection data is also performed in the same manner as in the case of the time-series magnetic detection data.

(magnetic distribution map generation processing)

The magnetic pattern generation unit 511 generates a magnetic pattern M (magnetic detection value, position) by associating the magnetic detection value at each time with the position in the escalator 100 with respect to the time-series magnetic detection data of 1 turn after the noise reduction processing by the noise reduction unit 513. As a method of processing the above-mentioned materials, there are various methods, and the following description will be given of 3 kinds of processing methods.

The first method is described. First, the movement start position of the step 1a for diagnosis is positioned at a predetermined position. Then, the diagnostic step 1a is rotated at a constant speed more than once from this position, and a plurality of time-series magnetic detection data shown in fig. 4 are acquired. Here, the above-described noise reduction process will be described taking as an example a case where the number of turns is equal to or greater than a plurality of turns. However, if the noise reduction processing capable of reducing noise with the detection data of the amount of 1 turn is adopted, the number of turns (cyclic movement) may be 1.

The movement start position of the diagnostic step 1a is positioned to a predetermined position, for example, as follows. The operator manually operates the control device 12 to adjust the position of the step for diagnosis 1a while visually observing the step for diagnosis 1 a. By performing such adjustment, as shown in fig. 5, the diagnostic step 1a is positioned at a position exposed (rotated out) from the lower entrance floor 13b (hereinafter, this position will be referred to as a reference position a).

The plurality of acquired time-series magnetic detection data are transmitted to the monitoring center 50, and the noise reduction unit 513 of the position estimation device 51 performs the noise reduction process described above. As a result, the time-series magnetic detection data D after the noise reduction processing shown in fig. 6 is obtained. The horizontal axis represents the elapsed time from the step 1a for diagnosis passing through the reference position a. The origin O (t=0) corresponds to the reference position a shown in fig. 5. The time-series magnetic detection data D indicates the magnetic field around the diagnostic step 1a, that is, the state of magnetization of the components of the escalator 100 during one rotation of the diagnostic step 1 a. The step 1a for diagnosis, which is moved cyclically, reaches the reference position a every cycle T, and the detection value Da is detected by the magnetic sensor 251. At each time t= tb, tc, te, tf in fig. 6, the position of the diagnostic step 1a is B, C, E, F in fig. 5. Returning to reference position a at t=ta.

The magnetic pattern generation unit 511 generates a magnetic pattern in which the time-series magnetic detection data D corresponds to each position of the escalator 100, based on the moving speed of the step 1a for diagnosis and the elapsed time from the reference position a. That is, the magnetic distribution map M of 1 turn of the diagnostic step 1a is represented by a set of data (Da, a), … …, (Db, B), … …, (Dc, C), … …, (De, E), … …, (Df, F), … …, (Da, a), and the like. That is, the magnetic distribution map M can be expressed as M (magnetic detection value, position).

The second method is explained. In the second method, a magnetic distribution map M (magnetic detection value, position) is generated using the detection value of the acceleration sensor 253 provided in the sensor portion 25. Fig. 7 is a diagram showing time-series magnetic detection data D1 and time-series acceleration detection data D10. Reference numerals a to F correspond to positions a to F of the escalator 100 shown in fig. 5. In the example shown in fig. 5, when the posture of the step 1a for diagnosis is observed, the second posture from the position E to the position F is inverted from the first posture from the position a to the position B. While the step 1a for diagnosis is moving from the position B to the position E, the posture of the step 1a for diagnosis gradually changes from the first posture to the second posture. On the other hand, while the step 1a for diagnosis is moving from the position F to the position a, the posture of the step 1a for diagnosis gradually changes from the second posture to the first posture.

The detection value of the acceleration sensor 253 provided in the step for diagnosis 1a changes according to the movement of the step for diagnosis 1 a. The acceleration detected by the acceleration sensor 253 includes acceleration due to gravity and acceleration due to vibration of the acceleration sensor 253. The outline shape of the line representing the time-series acceleration detection data D10 is determined by the acceleration due to gravity. The acceleration caused by the vibration of the acceleration sensor 253 is expressed as a minute vibration on the line.

In the time-series acceleration detection data D10 shown in fig. 7, the detection value is +d in the first posture and-D in the second posture. During the movement of the diagnostic step 1a from position B to position E, the detection value gradually changes from +d to-d, and the detection value changes from positive to negative at position P1. In addition, the detection value gradually changes from-d to +d while the diagnostic step 1a moves from the position F to the position a, and the detection value is inverted from negative to positive at the position P2. The time point (time) at which the detection value is inverted can be used as a reference position at the time of generation of the magnetic distribution map. For example, when the inversion position P1 is set as the reference position, the cycle from the inversion position P1 to the next inversion position P1 is 1 cycle.

The magnetic pattern generation unit 511 generates a magnetic pattern in which the time-series magnetic detection data D corresponds to each position of the escalator 100, based on the moving speed of the step 1a for diagnosis and the elapsed time from the reference position P1. That is, the magnetic distribution map M (magnetic detection value, position) of 1 turn of the diagnostic step 1a is expressed as a set of data (magnetic detection value, position) such as data sets (Dp, P1), … …, (De, E), … …, (Df, F), … …, (Da, a), … …, (Db, B), … …, (Dp, P1). In this way, the second method uses the inversion position P1 appearing in the detection value of the acceleration sensor 253 as a reference position when the magnetic distribution map is generated. As a result, automation and high accuracy of the magnetic distribution map generation operation can be achieved.

In addition, instead of the detection value of the acceleration sensor 253, the detection value of the sound sensor 252 may be used to set the reference position of the escalator 100. The time when the maximum detection value is detected can be set as the reference position as long as the position (portion) where the maximum sound is detected during one cycle of the diagnostic step 1a is known in advance. For example, assuming that the diagnostic step 1a is at the position C of fig. 5, the motor sound of the drive motor 6 is detected as the maximum detection value. In this case, the position C at which the detection value Dc of fig. 6 is detected is set as the reference position. Then, a magnetic distribution map M (magnetic detection value, position) is generated based on the moving speed of the step 1a for diagnosis and the elapsed time from the reference position C. The magnetic distribution map M (magnetic detection value, position) of 1 turn of the diagnostic step 1a is represented by a set of data (magnetic detection value, position) such as data sets (Dc, C), … …, (De, E), … …, (Df, F), … …, (Da, a), … …, (Db, B), … …, (Dc, C).

A third method is described. In the third method, the position of the diagnostic step 1a when the magnetization of the magnetized member among the members of the escalator 100 is detected is set as the reference position. For example, assuming that the diagnostic step 1a is at the position C of fig. 5, the magnetic field of the drive motor 6 is detected as the maximum detection value. In this case, a position C at which the detection value Dc of fig. 6 is detected is set as a reference position, and a magnetic distribution map M (magnetic detection value, position) is generated based on the moving speed of the step 1a for diagnosis and the elapsed time from the reference position C. The magnetic distribution map M (magnetic detection value, position) of 1 turn of the diagnostic step 1a is represented by a set of data (magnetic detection value, position) such as data sets (Dc, C), … …, (De, E), … …, (Df, F), … …, (Da, a), … …, (Db, B), … …, (Dc, C). By setting the position of the diagnostic step 1a at the time of detecting the magnetization of the magnetized member as the reference position in this way, it is not necessary to provide a sensor (acceleration sensor 253) for determining the reference position as in the second method. Therefore, the cost can be reduced.

(self position estimation processing)

Fig. 8 is a diagram illustrating an example of the self-position estimation process. In fig. 8, a line denoted by reference numeral M represents a magnetic distribution map M (magnetic detection value, position), and is a line of the same shape as the time-series magnetic detection data D shown in fig. 6. The line denoted by reference numeral S is time-series sound detection data of the sound sensor 252. In fig. 8, the vertical axis represents the detection values of the magnetic sensor 251 and the sound sensor 252, and the horizontal axis represents the position in the escalator 100.

Here, consider a case where Db is detected as the detection value of the magnetic sensor 251. When fitting (fixing) the detection value to the magnetic distribution map M (magnetic detection value, position), a position B is obtained. Since the magnetic distribution map M (magnetic detection value, position) of fig. 8 has a value Db in a place other than the position B, the position of the detection value Db is determined to be the position B by referring to the detection values before and after the detection value Db when fitting is performed. That is, the position of the step 1a for diagnosis when the detection value Db of the magnetic sensor 251 is detected is the position B.

The detection value Sb of the sound sensor 252 detected at the same time as the detection value Db is estimated as sound detection data detected when the diagnostic step 1a is at the position B. That is, the self-position estimation is to estimate the position of the step 1a for diagnosis in the escalator 100 when the detection value Sb is detected. By performing such a self-position estimation process, it is known where each detection value of the time-series sound detection data S is detected in the escalator 100.

Fig. 9 is a flowchart showing an example of the abnormality detection operation of the abnormality diagnosis device 52. The monitoring center 50 acquires a plurality of time-series detection data sets from the data collection device 30 by transmitting a data transmission instruction to the data collection device 30. Then, abnormality diagnosis processing is sequentially performed on the plurality of acquired time-series detection data sets. The flowchart shown in fig. 9 shows processing based on a data transmission instruction once, and is repeatedly executed at predetermined time intervals.

In step S90, a data transmission command is transmitted to the data collection device 30, and a plurality of time series detection data sets stored in the data storage unit 301 are collected. The time series detection data sets include a plurality of types of time series detection data sets corresponding to the respective sensors provided in the sensor section 25. In the example shown in fig. 3, since the magnetic sensor 251, the acoustic sensor 252, and the acceleration sensor 253 are provided in the sensor section 25, the time-series magnetic detection data set, the time-series acoustic detection data set, and the time-series acceleration detection data set are included in the time-series detection data set. In step S91, the noise reduction unit 522 performs noise reduction processing on the first time series detection data group of the plurality of time series detection data groups.

In step S92, based on the time series sound detection data and the time series acceleration detection data after the noise reduction processing, the abnormality estimating unit 521 determines whether or not an abnormality has occurred. In step S92, when it is determined that an abnormality has occurred (YES: yes), the process proceeds to step S94, and when it is determined that an abnormality has not occurred (NO: no), the process proceeds to step S93. In step S93, the abnormality estimating unit 521 determines whether or not an abnormality warning exists based on the time-series sound detection data and the time-series acceleration detection data after the noise reduction processing. In step S93, if it is determined that an abnormality warning exists (YES: yes), the process proceeds to step S94, and if it is determined that an abnormality warning does not exist (NO: no), the process proceeds to step S97.

Fig. 10 is a diagram illustrating an example of the abnormality occurrence determination in step S92 and the abnormality prediction determination in step S93. In the abnormality occurrence judgment and the abnormality precursor judgment, the time-series sound detection data and the time-series acceleration detection data after the noise reduction processing are compared with the judgment reference data to perform the judgment. Wherein the judgment reference data is stored in the storage unit 523. In the initial operation of generating the magnetic distribution map M (magnetic detection value, position) after the diagnostic step 1a is set, a time series detection data set is acquired from the data collection device 30. The storage unit 523 stores, as the judgment reference data, the time series detection data obtained by performing the noise reduction process on the time series detection data group. The time-series detection data includes time-series magnetic detection data, time-series sound detection data, and time-series acceleration detection data.

Fig. 10 is a diagram showing time series sound detection data S11 and S12 obtained by the noise reduction processing, and time series sound detection data S0 as judgment reference data. The horizontal axis is time t. The line S1 is a first determination line obtained by multiplying each value of the determination reference data S0 by α1. The line S2 is a second determination line obtained by multiplying each value of the determination reference data S0 by α2 (> α1). The second determination line S2 is used for the abnormality occurrence determination. The first judgment line S1 is used for the abnormality prediction judgment.

In the example shown in fig. 10, the line of the time series sound detection data S11 exceeds the first judgment line S1 in the vicinity of the time tc. In this case, the abnormality estimating unit 521 determines that an abnormality is observed in a certain component of the escalator 100, that is, that an abnormality is present. The time series sound detection data S12 is a line when a time has elapsed since the time series sound detection data S11 was acquired. The line of the time-series sound detection data S12 exceeds the second judgment line S2 in the vicinity of t=tc. In this case, the abnormality estimating unit 521 determines that an abnormality has occurred in a certain component of the escalator 100, that is, that an abnormality has occurred.

In fig. 10, the description has been made of the case where the abnormality occurrence or the abnormality sign is determined based on the time-series change of the time-series sound detection data, but the abnormality occurrence or the abnormality sign is determined using the time-series acceleration detection data similarly. When it is determined that an abnormality has occurred in at least one of the time-series sound detection data and the time-series acceleration detection data, it is determined that an abnormality has occurred in step S92. Similarly, when it is determined that at least one of the time series sound detection data and the time series acceleration detection data has an abnormality sign, it is determined in step S93 that an abnormality sign exists.

Returning to the flowchart of fig. 9, in step S94, a process is performed to estimate which position of the escalator 100 the abnormality has occurred or the position where the abnormality sign has occurred is. In the noise reduction processing of step S91 described above, time-series magnetic detection data, time-series sound detection data, and time-series acceleration detection data after the noise reduction processing are obtained. In fig. 10, the detection value at t=tc of the time-series sound detection data S11, S12 is Sc1 in the case of the time-series sound detection data S11, and Sc2 in the case of the time-series sound detection data S12. In addition, the detection value of the time-series magnetic detection data at t=tc is Dc as shown in fig. 6.

The abnormality diagnostic device 52 obtains the position where the detected value of the time-series magnetic detection data is Dc by causing the self-position estimating unit 512 to perform the self-position estimating process. In the self-position estimation process, the detected value Dc is fitted to the magnetic distribution map M (magnetic detected value, position) to obtain the position C (see fig. 8). As a result, it is known that the detected value Sc1 detected at the same time as the detected value Dc is a detected value obtained when the step 1a for diagnosis is located at the position C. That is, it is known that abnormality occurs in the component disposed in the vicinity of the position C. By performing the same processing also on the detected value Sc2, it is known that an abnormality has occurred in the member disposed in the vicinity of the position C.

In step S94, an estimation of the occurrence of an abnormality sign or the position of the abnormality is performed. Further, in step S95, an estimation of the occurrence of the abnormality sign or the abnormal component is performed. As shown in fig. 5, near a position C of the escalator 100, a chain 2, a final gear 4, a drive gear 5, a drive motor 6, and the like are provided. The abnormality estimation unit 521 estimates the abnormal position and also estimates the abnormal component. By performing not only the estimation of the occurrence of the abnormality but also the estimation of the abnormal component, it is possible to cope with the occurrence of the abnormality promptly and appropriately.

When an abnormality occurs in the chain 2, the final gear 4, the drive gear 5, and the drive motor 6, a characteristic abnormal sound or abnormal vibration is generated in each component. The time series sound detection data and the time series acceleration detection data include not only the magnitude of sound and vibration, but also the frequency of sound and vibration. In addition, the storage unit 523 stores in advance fault determination data in which the frequency of the abnormal sound and the frequency of the abnormal vibration are listed for each component and each fault. The abnormality estimating unit 521 estimates the component or the failure content in which an abnormality or an abnormality sign has occurred, based on the frequency of the sound and the frequency of the vibration at the position C.

In step S96, the report unit 524 reports the occurrence of an abnormality or an abnormality sign. The report information includes at least one of occurrence of an abnormality or an abnormality sign, a position where the abnormality or the abnormality sign has occurred, a component, and a failure content. If the process of step S96 ends, the process proceeds to step S97. In step S97, it is determined whether the noise reduction process and the abnormality occurrence or abnormality prediction determination process are completed for all of the plurality of time series detection data sets. In step S94, when it is judged that the operation is not completed (NO: NO), the routine returns to step S91, and when it is judged that the operation is completed (YES: yes), a series of diagnostic processes are completed.

As described above, in the diagnostic system of the present embodiment, when the diagnostic step 1a including the diagnostic sensor of the magnetic sensor 251 is mounted, the diagnostic step 1a is circulated to generate the magnetic distribution map M (magnetic detection value, position). The magnetic distribution map M (magnetic detection value, position) is a map showing the magnetic state of each position in the escalator 100. Therefore, the detection position of the detection value of the diagnostic sensor obtained during the operation of the escalator can be obtained by comparing the magnetic detection value detected at the same time as the detection value with the magnetic distribution map M (magnetic detection value, position). That is, even in a situation where the operation speed is changed, the detection position of the detection value of the diagnostic sensor can be estimated with high accuracy, and diagnosis can be performed at any time.

Further, in the present embodiment, as shown in fig. 3, data detected by the sensor unit 25 in the diagnostic step 1a is transmitted to the remote monitoring center 50 provided with the position estimating device 51 and the abnormality diagnosing device 52 by communication. Therefore, the condition of the escalator 100 can be monitored remotely at any time. Further, the monitoring center 50 can easily monitor a plurality of escalators at any time.

In addition, as described above, when estimating the position of the diagnostic step 1a, the detection position is estimated by comparing the detection value of the magnetic sensor 251 with the magnetic distribution map M (magnetic detection value, position). Therefore, in order to accurately estimate the position, it is preferable that the output of the magnetic sensor 251 is stable. In the present embodiment, the offset (offset) and drift (drift) of the magnetic sensor 251 are corrected by the magnetization of the member provided in the escalator 100.

For example, since the magnetization of the drive motor 6 is stable, the detection value of the magnetic sensor 251 at the position C near the drive motor 6 is taken as the reference value for correction. The detection value of the position C is used as a reference, that is, the detection value of the other position is corrected so that the detection value of the position C is always constant. For example, when the detected value of the position C is β times larger than the initial value, the detected values of all the positions are multiplied by (1/β) to perform correction. As a result, the influence of the output variation of the magnetic sensor 251 can be removed, and deterioration of the self-position estimation accuracy can be prevented. The correction operation may be performed by any one of the control unit 26, the data collection device 30, the position estimation device 51, and the abnormality diagnosis device 52 provided in the sensor terminal 24.

In the above embodiment, the self-position estimation process is performed by associating the sound detection data with the position in the escalator 100 based on the detection data of the magnetic sensor 251 and the generated magnetic distribution map M (magnetic detection value, position). Further, the self-position estimation process may be performed by using the inversion timing of the detection data of the acceleration sensor 253 at the same time. The reverse rotation timing is generated when the diagnostic step 1a moves to a specific position of the escalator 100. Therefore, by using the inversion timing, the self-position estimation accuracy can be improved, and the abnormality diagnosis can be performed with high accuracy. Further, a diagnostic sensor other than the acceleration sensor 253 may be used. For example, a height sensor or the like may be additionally used as a diagnostic sensor, and the detection data thereof may be used.

The configuration of the diagnostic system 1000 is not limited to the configuration shown in the block diagram of fig. 3.

(modification of diagnostic System configuration 1)

Fig. 11 is a block diagram showing a modification 1 of the structure of the diagnostic system 1000. In the diagnostic system 1000 of fig. 3, a plurality of time series detection data sets stored in the data storage unit 301 are transmitted to the communication device 53 of the monitoring center 50 via the network 40 by the communication unit 302. On the other hand, in the diagnostic system 1000A shown in fig. 11, the communication unit 302 of the control device 12 and the communication device 53 of the monitoring center 50 are omitted. In the diagnostic system 1000A, a system is employed in which a plurality of time series detection data sets stored in the data storage unit 301 are collected by an operator at the time of periodic inspection, and transmitted to the monitoring center 50. For data collection, portable storage media such as a USB flash drive (USB flash drive) and a portable hard disk drive are used. Since the communication section 302 and the communication device 53 are not required, cost reduction with respect to the communication system can be achieved.

(modification of diagnostic System configuration 2)

Fig. 12 is a block diagram showing modification 2 of the structure of the diagnostic system 1000. In the diagnostic system 1000B according to modification 2, the data storage unit 301 and the communication unit 302 that were previously provided in the control device 12 are provided in the diagnostic step 1 a. The plurality of time series detection data sets stored in the data storage 301 are transmitted from the communication unit 302 provided in the diagnostic step 1a to the communication device 53 of the monitoring center 50 via the network 40.

(modification 3 of the diagnostic System configuration)

Fig. 13 is a block diagram showing modification 3 of the structure of the diagnostic system 1000. The diagnostic system 1000C of modification 3 omits the communication unit 302 and the communication device 53 provided in the diagnostic system 1000B of fig. 12. For a plurality of time series detection data sets stored in the data storage unit 301, data collection is performed by an operator at the time of periodic inspection, and the data is transmitted to the monitoring center 50. The position estimating device 51 and the abnormality diagnosing device 52 of the monitoring center 50 generate and diagnose the magnetic distribution map M (magnetic detection value, position) based on the collected time series detection data set.

(modification of diagnostic System configuration 4)

Fig. 14 is a block diagram showing a modification 4 of the configuration of the diagnostic system 1000. In the diagnostic system 1000D according to modification 4, the position estimation device 51 and the abnormality diagnostic device 52 provided in the monitoring center 50 in the diagnostic system 1000 of fig. 3 are provided in the control device 12 of the escalator 200. However, the report section 524 provided in the abnormality diagnosis device 52 of fig. 3 is provided in the monitoring center 50.

In the diagnostic system 1000D, generation and storage of the magnetic distribution map M (magnetic detection value, position), and diagnostic processing other than the processing of step S96 among the diagnostic processing shown in fig. 9 are performed by the position estimating device 51 and the abnormality diagnostic device 52 provided in the control device 12. When an abnormality or an abnormality sign occurs as a result of the diagnosis process, report information is transmitted from the communication unit 302 to the communication device 53 of the monitoring center 50 via the network 40. Report information is presented by the reporting unit 524. Of course, the monitoring center 50 can acquire the time series detection data set stored in the data storage unit 301 of the data collection device 30 by transmitting a transmission instruction of the time series detection data set to the control device 12.

(modification 5 of the diagnostic System configuration)

Fig. 15 is a block diagram showing a modification 5 of the structure of the diagnostic system 1000. In the diagnostic system 1000E according to modification 5, in the diagnostic system 1000D shown in fig. 14, the communication unit 302 of the data collection device 30 is replaced with a wireless communication unit 302A for short-range communication, the communication unit 53 of the monitoring center 50 is omitted, and the information processing device 54 such as a personal computer is disposed in place of the report unit 524 of the monitoring center 50. The wireless communication unit 302A receives the detection data of the sensors 251 to 253 from the wireless communication unit 27 provided in the step 1a for diagnosis.

The results of the diagnosis processing by the position estimation device 51 and the abnormality diagnosis device 52 provided in the control device 12, the generated magnetic distribution map M (magnetic detection value, position), the time series detection data set stored in the data storage unit 301, and the like are collected by the operator at the time of the periodic inspection, and transmitted to the monitoring center 50. In the monitoring center 50, the diagnosis processing result and the magnetic distribution map M (magnetic detection value, position) can be checked by the information processing device 54. Further, the information processing device 54 can perform confirmation, analysis, and the like of the collected time series detection data set.

In the configuration shown in fig. 15, the report portion 524 is not provided in the abnormality diagnosis device 52, but the report portion 524 may be provided in the abnormality diagnosis device 52. By providing the report unit 524, the operator can check the abnormal state at the time of the regular inspection, and can cope with the abnormality on site. Of course, even if the report section 524 is not provided, the diagnosis result stored in the storage section 523 can be read by a personal computer or the like to confirm the diagnosis result.

(modification 6 of the diagnostic System configuration)

Fig. 16 is a block diagram showing modification 6 of the structure of the diagnostic system 1000. In the diagnostic system 1000F according to modification 6, the data storage 301, the communication unit 302, the position estimation device 51, and the abnormality diagnosis device 52 provided in the control device 12 are provided in the diagnostic step 1a in the diagnostic system 1000D shown in fig. 14, and the wireless communication unit 27 provided in the diagnostic step 1a is omitted.

In the diagnostic system 1000F, the data storage unit 301, the position estimation unit 51, and the abnormality diagnosis unit 52 provided in the step 1a for diagnosis are all configured to store the detection data detected by the sensor unit 25, generate a magnetic distribution map M (magnetic detection value, position) based on the detection data, and perform diagnosis based on the detection data and the magnetic distribution map M (magnetic detection value, position). The diagnosis result is transmitted from the communication unit 302 to the communication device 53 of the monitoring center 50 via the network 40. The monitoring center 50 reports the received diagnosis result to the reporting unit 524. Of course, the monitoring center 50 can acquire the time series detection data set stored in the data storage unit 301 by transmitting a transmission instruction of the time series detection data set to the control device 12.

(modification 7 of the diagnostic System configuration)

Fig. 17 is a block diagram showing modification 7 of the structure of the diagnostic system 1000. In the diagnostic system 1000G according to modification 7, in the diagnostic system 1000F shown in fig. 16, the communication unit 302 provided in the step 1a for diagnosis is omitted, the communication device 53 of the monitoring center 50 is omitted, and the information processing device 54 such as a personal computer is disposed in place of the report unit 524 of the monitoring center 50.

The position estimation device 51 and the abnormality diagnosis device 52 provided in the diagnosis step 1a collect data from the operator at the time of periodic inspection, and send the collected data to the monitoring center 50, as well as the generated magnetic distribution map M (magnetic detection value, position) and the time series detection data set stored in the data storage unit 301. In the monitoring center 50, the diagnosis processing result and the magnetic distribution map M (magnetic detection value, position) can be checked by the information processing device 54. Further, the information processing device 54 can perform confirmation, analysis, and the like of the collected time series detection data set. Further, as in the case of modification 5, the operator can confirm the diagnosis result by reading the diagnosis result stored in the storage unit 523 with a personal computer or the like at the time of the regular inspection, and can cope with the abnormality on site. The reporting unit 524 may be provided.

In the block diagrams shown in fig. 3 and 11 to 17, the functional units in the configuration, for example, the functional units in the position estimation device 51 and the abnormality diagnosis device 52, may be realized by programs executed by a combination of a microcomputer, a processor, and a computing device similar to them, a ROM, a RAM, a flash memory, a hard disk, an SSD, a memory card, an optical disk, and a storage device similar to them, a bus, a network, and a communication device similar to them, and peripheral devices, in addition to a circuit, an electronic circuit, a logic circuit, and an integrated circuit incorporating them, and the present invention is applicable to any implementation. In addition, in the embodiment, 2 or more programs may be implemented as 1 program, and 1 program may be implemented as 2 or more programs.

According to the embodiments of the present invention described above, the following operational effects can be obtained.

(C1) As shown in fig. 1 and 3, a diagnostic system 1000 is a diagnostic system 1000 for diagnosing an abnormality of an escalator 100, the escalator 100 being a passenger conveyor for conveying passengers by circulating a plurality of steps 1, 1a connected in a loop, the diagnostic system 1000 including: a step 1a for diagnosis, which is provided in a plurality of steps 1, 1a and has at least 1 or more diagnostic sensors including a magnetic sensor 251; a magnetic distribution map generation unit 511 for generating a magnetic distribution map M (magnetic detection value, position) that is a correlation between the position in the escalator 100 and the detection value of the magnetic sensor 251 during one cycle of the diagnostic step 1 a; a self-position estimating unit 512 that estimates the position of the escalator 100 at which the detection value of the sound sensor 252 is detected, based on the magnetic distribution map M (magnetic detection value, position) and the detection value of the magnetic sensor 251 at the time of the circulating movement; and an abnormality estimation unit 521 that performs abnormality diagnosis based on the detection value of the acoustic sensor 252.

As shown in fig. 8, a magnetic distribution map M (magnetic detection value, position) shows a correlation between the position in the escalator 100 and the detection value of the magnetic sensor 251 during one cycle of the diagnostic step 1 a. Based on the magnetic distribution map M (magnetic detection value, position) and the detection value of the magnetic sensor 251 at the time of the circulating movement, the position of the detection value of the sound sensor 252 in the escalator 100 is estimated. Therefore, even in a situation where the operation speed changes during operation of the escalator, the detection position of the detection value of the diagnostic sensor can be estimated with high accuracy, and diagnosis can be performed at any time.

(C2) As shown in fig. 5 and 7, the diagnostic step 1a may include an acceleration sensor 253 and a sound sensor 252 as position specifying sensors capable of specifying the position of the circulating diagnostic step 1a in the escalator 100, and the magnetic pattern generating unit 511 may generate the magnetic pattern M (magnetic detection value, position) using the position specified by the position specifying sensors (for example, the reverse position P1) as a reference position. By using the position determined by the position determining sensor as the reference position at the time of magnetic distribution map generation, automation and high accuracy of the magnetic distribution map generation operation can be achieved.

(C3) The diagnostic step 1a may include an acceleration sensor 253 as a position determination sensor; the magnetic distribution map generation unit 511 estimates the time at which the posture of the diagnostic step 1a is inverted based on the sensor output of the acceleration sensor 253, and generates a magnetic distribution map M (magnetic detection value, position) using the inversion position P1 of the diagnostic step 1a at this time as a reference position. The acceleration sensor 253 can detect the inversion position P1 easily and with high accuracy.

(C4) As shown in fig. 5 and 6, the escalator 100 may include a magnetized member (e.g., a drive motor 6), and the magnetic pattern generation unit 511 may generate a magnetic pattern M (magnetic detection value, position) using the position C of the diagnostic step 1a when the magnetic sensor 251 detects the magnetization of the magnetized member as a reference position. By setting the position of the diagnostic step 1a at the time of detecting the magnetization of the magnetized member as the reference position, it is not necessary to additionally provide a sensor (acceleration sensor 253) for specifying the reference position. Therefore, cost reduction can be achieved.

(C5) As shown in fig. 5 and 7, the self-position estimating unit 512 may be configured to estimate the position at which the detection value of the sound sensor 252, which is the diagnostic sensor, is detected based on the magnetic distribution map M (magnetic detection value, position) and the detection value (for example, the inversion position P1) of the magnetic sensor 251 and the acceleration sensor 253, which is the position determining sensor, during the cyclic movement. The reverse position P1 detected by the acceleration sensor 253 is generated when the diagnostic step 1a moves to a specific position of the escalator 100. Therefore, by using the reverse position P1 also for self-position estimation, the self-position estimation accuracy can be improved, and abnormality diagnosis can be performed with high accuracy.

(C6) Further, a magnetized member (e.g., drive motor 6) may be provided in escalator 100, and the output error of magnetic sensor 251 may be corrected based on the magnetization of the magnetized member. For example, the control unit 26 and the data collection device 30 perform correction processing. As a result, the influence of the output variation of the magnetic sensor 251 can be removed, and deterioration of the self-position estimation accuracy can be prevented.

(C7) As in the process of step S95 in fig. 9, the abnormality estimating unit 521 estimates the abnormal member based on the position estimated by the self-position estimating unit 512 and the detection data of the diagnostic sensor (for example, the acoustic sensor 252). By estimating not only the occurrence of an abnormality but also the abnormal component, it is possible to cope with the occurrence of an abnormality promptly and appropriately.

(C8) As shown in fig. 3 and 12, the diagnostic system further includes: a communication unit 302 provided in the case frame 3 or the diagnostic step 1a of the escalator 100 in which the drive motor 6 for circulating the plurality of steps 1, 1a is disposed; and a monitoring center 50 that transmits and receives data by communication with the communication unit 302, and that transmits detection data of the diagnostic sensor to the monitoring center 50 through the communication unit 302 as a remote monitoring unit provided with the self-position estimating unit 512 and the abnormality diagnostic device 52. This enables the escalator 100 to be monitored remotely at any time.

(C9) Further, as shown in fig. 3, the communication unit 302 may be provided in the housing frame 3 of the escalator 100, and the diagnostic step 1a may further include a wireless communication unit 27, and the wireless communication unit 27 may transmit the detection data of the diagnostic sensor to the communication unit 302 provided in the housing frame 3 by wireless communication, and the communication unit 302 may transmit the detection data transmitted from the diagnostic step 1a to the monitoring center 50. The wireless communication unit 27 can use a short-range wireless device with low power consumption, and can use a small-capacity power source such as a battery as the power source arranged in the diagnostic step 1 a.

(C10) As shown in fig. 14 and 16, the self-position estimating unit 512 and the abnormality estimating unit 521 are provided in the case frame 3 or the diagnostic step 1a of the escalator 100 in which the drive motor 6 for circularly moving the steps 1 and 1a is disposed, and further include: a communication unit 302 provided in the case frame 3 or the diagnostic step 1a; and a monitoring center 50 that transmits and receives data to and from the communication unit 302 by communication, and transmits the diagnosis result of the abnormality estimation unit 521 to the monitoring center 50 via the communication unit 302.

In the case of such a configuration, the monitoring center 50 may be provided with a device capable of receiving the inspection result, and therefore, for example, the diagnosis result can be received via the internet through an information terminal such as a personal computer or a mobile phone. Therefore, even if a large-scale monitoring center is not provided, it is possible to realize a system for the operator to receive the diagnosis result with the information terminal and to check the escalator 100 for the presence of an abnormality.

(C11) As shown in fig. 11, 13, 15, and 17, the case frame 3 or the diagnostic step 1a of the escalator 100 in which the drive motor 6 for circularly moving the steps 1 and 1a is disposed further includes a data storage unit 301 for storing detection data of the sensor unit 25. In this case, the operator collects the detection data stored in the data storage unit 301, and the self-position estimating unit 512 and the abnormality estimating unit 521 perform data analysis of the detection data, so that it is not necessary to provide a communication device.

(C12) As shown in fig. 15 and 17, the self-position estimating unit 512 and the abnormality estimating unit 521 are provided in the case frame 3 or the step 1a for diagnosis of the escalator 100 in which the drive motor 6 for circularly moving the steps 1 and 1a is disposed, and the case frame 3 or the step 1a for diagnosis in which the self-position estimating unit 512 and the abnormality estimating unit 521 are disposed further includes a storage unit 523 for storing the diagnosis result of the abnormality estimating unit 521. In this case, the operator can check the diagnosis result by reading the diagnosis result stored in the storage unit 523 with a personal computer or the like at the time of the regular inspection, and can cope with the abnormality on site.

The embodiments and the modifications described above are merely examples, and the present invention is not limited to these examples as long as the features of the invention are not impaired. For example, the present invention can also be applied to passenger conveyors other than escalators. The various embodiments and modifications have been described above, but the present invention is not limited to these. Other modes conceivable within the technical idea of the present invention are also included in the scope of the present invention.

Description of the reference numerals

A step for diagnosis of 1a … …, a step for diagnosis of … …, a chain of 2 … …, a 3 … … housing frame, a 4 … … final gear, a 6 … … drive motor, a 9 … … lower final gear, a 12 … … control device, a 24 … … sensor terminal, a 25 … … sensor portion, a 26 … … control device, a 27 … … wireless communication portion, a 30 … … data collection device, a 50 … … monitoring center, a 51 … … position estimation device, a 52 … … abnormality diagnosis device, a 53 … … communication device, a 54 … … information processing device, a 251 … … magnetic sensor, a 252 … … sound sensor, a 253 … … acceleration sensor, a 301 … … data storage portion, a 302 … … communication portion, a 511 … … magnetic profile generation portion, a 512 … … self position estimation portion, 513, a 522 … … noise reduction portion, a 514, a 523 … … storage portion, a 521 … … abnormality estimation portion, a 524 … … reporting portion, and a 1000, 1000A 1000G … … diagnosis system.

Claims (12)

1. A diagnostic system for diagnosing an abnormality of a passenger conveyor that conveys passengers by circulating a plurality of steps that are linked in a loop, the diagnostic system comprising:

a step for diagnosis, which is included in the steps and has at least 1 or more diagnostic sensors including a magnetic sensor;

a magnetic distribution map generation unit configured to generate a magnetic distribution map indicating a correlation between a position in the passenger conveyor during one cycle of the step cycle for diagnosis and a detection value of the magnetic sensor;

a position estimating unit that estimates a position of the passenger conveyor at which the detection value of the diagnostic sensor is detected, based on the magnetic distribution map and the detection value of the magnetic sensor during the cyclic movement; and

and an abnormality diagnosis unit that performs abnormality diagnosis based on the detection value of the diagnosis sensor.

2. The diagnostic system of claim 1, wherein:

the step for diagnosis includes, as the sensor for diagnosis, a position determination sensor capable of determining a position of the step for diagnosis in the passenger conveyor, which is moved circularly,

The magnetic distribution map generating unit generates the magnetic distribution map using the position determined by the position determining sensor as a reference position.

3. The diagnostic system of claim 2, wherein:

the step for diagnosis includes an acceleration sensor as the position determining sensor,

the magnetic distribution map generation unit estimates a time when the posture of the step for diagnosis is reversed based on the sensor output of the acceleration sensor, and generates the magnetic distribution map using the position of the step for diagnosis at the time as a reference position.

4. The diagnostic system of claim 1, wherein:

the passenger conveyor comprises magnetized components,

the magnetic distribution map generating unit generates the magnetic distribution map using a position of the diagnostic step at which the magnetic sensor detects magnetization of the magnetization member as a reference position.

5. The diagnostic system of claim 2, wherein:

the position estimating unit estimates a position at which the detection value of the diagnostic sensor is detected, based on the magnetic distribution map and the detection values of the magnetic sensor and the position determining sensor during the cyclic movement.

6. The diagnostic system of claim 1, wherein:

a magnetized component is arranged in the passenger conveyor,

the diagnostic system further includes a correction unit that corrects an output error of the magnetic sensor based on magnetization of the magnetization member.

7. The diagnostic system of claim 1, wherein:

the abnormality diagnosis unit estimates an abnormal component based on the position estimated by the position estimation unit and the detection data of the diagnosis sensor.

8. The diagnostic system of claim 1, further comprising:

a communication device provided in a fixed portion of the passenger conveyor or the diagnostic steps, the passenger conveyor being provided with a driving device that cyclically moves a plurality of the steps; and

a remote monitoring unit which transmits and receives data by communicating with the communication device, the remote monitoring unit being provided with the position estimating unit and the abnormality diagnosing unit,

and transmitting detection data of the diagnostic sensor to the remote monitoring unit via the communication device.

9. The diagnostic system of claim 8, wherein:

the communication device is arranged at the fixed part of the passenger conveyor,

The step for diagnosis further includes a wireless communication device that transmits detection data of the sensor for diagnosis to the communication device provided in the fixed portion by wireless communication,

the communication device transmits the detection data transmitted from the diagnostic step to the remote monitoring unit.

10. The diagnostic system of claim 1, wherein:

the position estimating unit and the abnormality diagnosing unit are provided in a stationary unit of the passenger conveyor or the step for diagnosis, in which a driving device for circulating a plurality of steps is provided,

the diagnostic system further comprises:

a communication device provided in the stationary part or the diagnostic step of the passenger conveyor; and

a remote monitoring unit which transmits and receives data to and from the communication device by communication,

and transmitting the diagnosis result of the abnormality diagnosis section to the remote monitoring section through the communication device.

11. The diagnostic system of claim 1, wherein:

the passenger conveyor fixing portion or the diagnostic step, on which a driving device for circularly moving the plurality of steps is disposed, further includes: and a data storage unit for storing detection data of the diagnostic sensor.

12. The diagnostic system of claim 1, wherein:

the position estimating unit and the abnormality diagnosing unit are provided in a stationary unit of the passenger conveyor or the step for diagnosis, in which a driving device for circulating a plurality of steps is provided,

the position estimating unit, the fixing unit of the abnormality diagnosing unit, or the step for diagnosis, and a diagnosis result storing unit that stores a diagnosis result of the abnormality diagnosing unit are provided.