CN104444750A - Abnormality diagnostic system for passenger conveyor - Google Patents
Abnormality diagnostic system for passenger conveyor Download PDFInfo
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- CN104444750A CN104444750A CN201310627682.4A CN201310627682A CN104444750A CN 104444750 A CN104444750 A CN 104444750A CN 201310627682 A CN201310627682 A CN 201310627682A CN 104444750 A CN104444750 A CN 104444750A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
- B66B27/00—Indicating operating conditions of escalators or moving walkways
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Abstract
The invention provides an abnormality diagnostic system for a passenger conveyor to perform abnormality diagnosis of the passenger conveyor highly accurately. According to one embodiment, the abnormality diagnostic system for the passenger conveyor comprises a storing portion for storing data measured by a sensor terminal installed at the predetermined portion of the passenger conveyor; a frequency analyzing portion for reading the measured data and performing frequency analysis; an abnormal component extracting portion for extracting an abnormal component based on the measured data and reference data; a correlation value calculating portion for calculating correlation values between an abnormal extract measured data corresponding to the abnormal component and a plurality of abnormal data; an abnormality detecting portion for detecting whether an abnormality is generated in the passenger conveyer based on the correlation values; an abnormality estimating portion for estimating the details of the abnormality when an abnormality is detected; and a display portion for displaying the details of the abnormality.
Description
The application is by the Japanese patent application 2013-190161(applying date: on September 13rd, 2013) based on, and to enjoy priority benefit according to this application.The application's this application and comprise its full content by reference.
Technical field
Embodiments of the present invention relate to a kind of abnormity diagnostic system of passenger conveyors.
Background technology
The passenger conveyors such as escalator or mobile pavement has the multiple pedals connected annularly by chain.Passenger conveyors, for be driven by motor, is made above-mentioned pedal cycle move along the guide rail being disposed in truss inside, the passenger taken on pedal is transported to the structure of the stopping port of the opposing party from the stopping port of a side.
In this passenger conveyors, due to the articles for use wearing and tearing that cause through long-term running and mounting and adjusting state aging, or foreign matter is mixed into and the reason such as the mischief that caused by passenger, and such as moving part friction, stranded etc. exception can occur.
This exception often shows as vibration or sound.Usually, when there is the exception of vibration or sound in passenger conveyors place, maintainer removes on-the-spot real-world operation passenger conveyors, determines which part is the exception of vibration or sound occur in, and the part carrying out the major cause causing this exception to occur exchanges or adjustment operation.Due to the running service of passenger conveyors will be stopped in operation, if so operation needs the time, so bring very large trouble by user.
Again, maintainer determines abnormal when there is major cause at the scene, and according to going, the skill level of on-the-spot maintainer is different, its correctness and swiftness in there is difference.When the proficiency of maintainer is lower, the service stopping longer time can only be made.Therefore, wish that the stage of certain exception of appearance before fault occurs just finds this exception in advance, eliminated by maintenance activity and extremely avoid fault.
According to this hope, the pedal inside proposed at passenger conveyors place has can to representing the acceleration pick-up that the flip-flop of vertical sensitivity measures, and analyzing and processing is carried out to the output signal (moment history data) of this acceleration pick-up, carry out the abnormity diagnostic system of abnormality diagnostic passenger conveyors.
Summary of the invention
But, in the abnormity diagnostic system of above-mentioned passenger conveyors, because carry out analyzing and processing to moment history data and carry out abnormity diagnosis, if so produce deviation in the collection precision of the moment history data obtained by acceleration pick-up, to amplify the noise being contained in moment history data when analyzing and processing, existence can not carry out abnormality diagnostic problem accurately.
Even if the problem that the present invention will solve is that provide a kind of produces deviation in the collection precision of moment history data, also can carry out the abnormity diagnostic system of the abnormality diagnostic passenger conveyors of passenger conveyors.
The abnormity diagnostic system of the passenger conveyors of embodiment comprises: a storage part, and it stores the data measured by the sensor terminal of the determined location being arranged at passenger conveyors; Frequency analysis portion, its line frequency analysis that measurement data reading of the described sensor terminal being stored in described storage part is gone forward side by side; Abnormal component extraction unit, it, based on the described measurement data of having been carried out frequency analysis by described frequency analysis portion and the Reference data preset, extracts abnormal component; Correlation value calculation section, it calculates the measurement of the anomaly extracting corresponding to the abnormal component data extracted by described abnormal component extraction unit and the correlation representing multiple abnormal cause multiple abnormal datas of feature separately respectively; Abnormity detection portion, it is detected and whether exception occurs based on each correlation calculated by described correlation value calculation section at described passenger conveyors place; Abnormality estimation portion, it is when detecting abnormal by described abnormity detection portion, based on the abnormal data used when obtaining described each correlation, the details that presumption is abnormal; Display part, the details of the exception that its display is deduced by described abnormality estimation portion.
Based on the abnormity diagnostic system of the passenger conveyors of said structure, passenger conveyors abnormity diagnosis can be carried out accurately.
Accompanying drawing explanation
Fig. 1 is the figure of the structure of the abnormity diagnostic system of the escalator illustrated involved by a kind of embodiment.
Fig. 2 is the sensor terminal of the abnormity diagnostic system that the escalator forming same embodiment is shown, data-capture unit, be far apart the block scheme of the inner structure of monitor unit.
Fig. 3 illustrates the figure being far apart the data store structure of monitor unit being provided to same embodiment.
Fig. 4 illustrates the diagram of circuit of an example being far apart abnormal sound extraction process that monitor unit carries out, noise reduction process and abnormality detection process by same embodiment.
Fig. 5 be to same embodiment be far apart the process that monitor unit carries out time the figure that is described of the data analysis unit that uses.
Fig. 6 (a) ~ (d) is the mode chart of the example that the traveling voice data collected by the sensor terminal of same embodiment is shown.
Fig. 7 (a) ~ (d) illustrates to be far apart with same embodiment the figure that monitor unit has carried out an example of the traveling voice data of frequency analysis.
Fig. 8 (a) ~ (c) illustrates the figure other shown in shown in Fig. 7 (a) traveling voice data and Fig. 7 (b) ~ (d) being travelled variance rate when voice data is made comparisons.
Fig. 9 be illustrate for same embodiment be far apart the process that monitor unit carries out time other the figure that is described of the data analysis unit that uses.
Detailed description of the invention
Below, be described with reference to the abnormity diagnostic system of accompanying drawing to the passenger conveyors of embodiment.
In addition, be described using escalator as passenger conveyors example below.
Fig. 1 is the figure of the abnormity diagnostic system structure of the escalator illustrated involved by a kind of embodiment.In figure 10 represents that escalator is overall.
Escalator 10 is such as inclined between the last layer of building and lower one deck.This escalator 10, by making to connect loopy moving between the stopping port of multiple pedal 11 in Machine Room, top 12 and the stopping port of lower mechanical room 13 without slot, transports the passenger taken on pedal 11.
Each pedal 11 is connected by loop connecting chain 14, and is configured in truss 15, and truss 15 is arranged at the underfloor of building.At internal configurations upper chain gear 16 and the lower chain gear 17 of truss 15, and winding connects chain 14 between which.
The actuating device 18 with motor, reductor etc. is connected with lower chain gear 17 either party (being upper chain gear 16 in this example) at upper chain gear 16.Utilize this actuating device 18, sprocket gear (Sprocket) 16,17 carries out rotating and connection chain 14 by being snapped at sprocket gear 16,17, and not shown guide rail guides multiple pedal 11 loopy moving between the stopping port 12 and the stopping port of lower mechanical room 13 of Machine Room, top 12.
Again, on the top of truss 15, with the two sides of each pedal 11 relatively, the moving direction along pedal 11 arranges a pair not shown skirt guard.This skirt guard on respectively erect bannister 19 is set.Banded handrail 20 is installed around this bannister 19.Handrail 20 takes the handrail that the passenger in pedal 11 grasps, such as, utilize the propulsive effort transmitting actuating device 18, synchronously carries out cycle rotation with the movement of pedal 11.
Here, in multiple pedals 11 of escalator 10, a spot check pedal 11a is at least established.The inner side of this spot check pedal 11a, establishes or standing fixation of sensor terminal 30 temporarily.In addition, about the installation method of this sensor terminal 30, because do not have direct relation with the present invention, the description thereof will be omitted here.
This sensor terminal 30 possesses such as Bluetooth(registered trade mark (RTM)) etc. wireless near field communication function, measure about escalator 10 run data and be wirelessly transmitted to data-capture unit 40.Below, be that example is described as following situation: sound transducer is built in sensor terminal 30.
Data-capture unit 40 is arranged near sensor terminal 30.In Fig. 1, although illustrate example data-capture unit 40 being arranged on lower mechanical room 13, also can be arranged on Machine Room, top 12.
Data-capture unit 40 possesses the function as gateway (GW).This data-capture unit 40 collects the traveling voice data measured by sensor terminal 30, is sent to outside is far apart monitor unit with the unit specified.
In addition, this data-capture unit 40 can carry out radio communication with multiple stage (such as 4) sensor terminal 30.Thus, if being separately positioned on by sensor terminal 30 is such as set up in the multiple escalator 10 of each floor gap, so by 1 data-capture unit 40, just the traveling voice data measured with above-mentioned sensor terminal 30 can be sent to and be far apart monitor unit 40.
Be arranged on being far apart monitor unit 50 in the central monitoring position 60 being far apart ground.Multidigit monitoring personnel resides in central monitoring position 60, and the monitor picture being far apart monitor unit 50 monitors the running state of the escalator 10 of each object as monitored object.The monitor unit 50 that is far apart in this central monitoring position 60 is connected by communication line 61 with the data-capture unit 40 being arranged at escalator 10.
In addition, in the example of Fig. 1, although illustrate only 1 escalator 10, the escalator 10 of in fact each object is connected to by communication line 61 and is far apart monitor unit 50 in central monitoring position 60.Monitoring personnel, once certain exception be detected on the monitor picture being far apart monitor unit 50, just sends maintainer to process to scene etc.
Fig. 2 is the sensor terminal of the abnormity diagnostic system that the escalator forming same embodiment is shown, data-capture unit, be far apart the block scheme of the inner structure of monitor unit.
Native system by sensor terminal 30, data-capture unit 40, be far apart monitor unit 50 and form.As shown in Figure 1, sensor terminal 30 is arranged at the spot check pedal 11a in each pedal 11 of escalator 10.This sensor terminal 30 comprises control part 31, sensor part 32, pedal position test section 33, wireless communication part 34.
Control part 31 carries out the control of sensor terminal 30.Sensor part 32 measures the data of the operation about escalator 10.In present embodiment, be equipped with sound transducer as this sensor part 32, the traveling voice data of measurement escalator 10.
Pedal position test section 33 is made up of inclination sensor, gyrosensor etc., one week that is benchmaring escalator 10 with the position of spot check pedal 11a.Wireless communication part 34 and data-capture unit 40 carry out wireless near field communication, and the traveling audio data transmitting measured by sensor part 32 is delivered to data-capture unit 40.
Data-capture unit 40 is arranged at the determined location (being lower mechanical room 13 in the example of Fig. 1) of escalator 10.This data-capture unit 40 comprises wireless communication part 41, data store 42, transfer control portion 43.
Wireless communication part 41 carries out wireless near field communication between sensor terminal 30 and data-capture unit 40, receives the traveling voice data sent from sensor terminal 30.Data store 42 stores the traveling voice data received by wireless communication part 41.In this case, if there is the escalator 10 of multiple stage as monitored object, add and store traveling voice data with being set in the ID of each escalator 10.Transfer control portion 43 will be stored in the traveling voice data of data store 42 to specify that unit reads, and is sent to and is far apart monitor unit 50.
Being arranged on being far apart monitor unit 50 in the central monitoring position 60 being present in and being far apart ground, being connected to the data-capture unit 40 being arranged at escalator 10 by communication line 61.This Long-Range Surveillance Unit 50 comprises data transmit-receive portion 51, operation inputting part 52, data store 53, data processing division 54, display part 58, notification unit 59.
Data transmit-receive portion 51 carries out the transmitting-receiving process of various data.Operation inputting part 52 is made up of keyboard etc., and data input, instruction are carried out in the operation according to monitoring personnel.
As shown in Figure 3, in data store 53, the traveling voice data of the escalator 10 obtained from data-capture unit 40 is stored as traveling audio files F1.Again, in this data store 53, the base sound data of the com-parison and analysis be made by the traveling voice data measured in advance when installing according to escalator 10, after maintainer's spot check store as base sound file F2.The base sound data at each position (such as rolled portion, direct acting portion etc.) of escalator 10 are comprised in base sound file F2.Further, in this data store 53, abnormal sound data stored as abnormal sound file F3, these abnormal sound data occur to obtain by making the exception likely occurring in escalator 10 in analog.Abnormal sound data are the data representing the frequency content feature of each abnormal sound for each with the multiple abnormal cause that abnormal sound produces.Specifically, the abnormal sound data about abnormal sound as described below are stored as abnormal sound file F3.
(1) pedal contacts with skirt guard and the abnormal sound produced;
(2) abnormal sound that produced by guide rail of foreign matter;
(3) abnormal sound (pedal reversion sound) produced owing to connecting the lax of chain;
(4) stopping port rubber contacts with pedal clamping plate and the abnormal sound produced;
(5) pedal trailing wheel roller bearing distortion or impaired and produce abnormal sound;
(6) bearing of pedal trailing wheel roller bearing bad and produce abnormal sound.
In addition, the preservation form of base sound data and abnormal sound data, the Wave data wav file form of base sound and abnormal sound can be preserved, also can preserve by FFT(Fast Fourier Transform: fast Fourier transform) data after frequency analysis are carried out to base sound and abnormal sound.Below, for the purpose of simplifying the description, in data store 53, be that example is described as following situation: the data of having carried out frequency analysis are in advance preserved as base sound data and abnormal sound data.
Data processing division 54 is made up of microprocessor etc., carries out analyzing and processing to the traveling voice data of escalator 10.This data processing division 54 comprises memory device 55, anomaly extracting portion (noise reduction portion) 56 and abnormity detection portion 57.
Memory device 55 is the working storage for storing temporarily.In this memory device 55, store the traveling voice data read from the traveling audio files F1 of data store 53, from the base sound data that base sound file F2 reads, and from the abnormal sound data that abnormal sound file F3 reads.
Anomaly extracting portion 56 carries out frequency analysis to the traveling voice data read from memory device 55 by plural FFT.Again, anomaly extracting portion 56 uses the traveling voice data that carries out frequency analysis and is stored in the base sound data of memory device 55 to extract abnormal sound composition.Further, the abnormal sound composition extracted is transformed to anomaly extracting by reverse plural FFT and travels voice data (abnormal sound extraction process) by anomaly extracting portion 56.Again, anomaly extracting portion 56, for the unit of analysis of each regulation, carries out and makes to be contained in the noise reduction process that anomaly extracting travels the noise reduction of voice data.Noise is outside environmental sounds, such as, and the sound that its expression is walked by passenger and produced on the pedal of escalator 10, or produce the sound etc. around escalator 10.
Abnormity detection portion 57 detects, at escalator 10 place, whether abnormal part occurs, and is made up of anomalous content presumption unit 57a and the abnormal place presumption unit 57 that occurs.In addition, in fact this abnormity detection portion 57 is realized by calculation procedure algorithm.
Anomalous content presumption unit 57a uses the anomaly extracting implementing noise reduction process to travel voice data and is stored in the abnormal sound data of memory device 55, carries out relevant treatment, detects and whether exception occurs at escalator 10 place.Again, anomalous content presumption unit 57a, when detecting abnormal, estimates the content of this exception.The abnormal place presumption unit 57b that occurs, when detecting abnormal by anomalous content presumption unit 57a, estimates the position, where that this exception occurs in escalator 10.
Display part 58 result of prescribed form display abnormity detection portion 57.Again, notification unit 59 exists when needing the place of spot check in the result of abnormity detection portion 57, notifies this situation by display, sound etc.
In such an embodiment, first, in central monitoring position 60, the operation inputting part 52 of monitor unit 50 is far apart in monitoring personnel operation, and input is regularly carried out the schedule of the traveling sound collecting of escalator 10 and is sent to data-capture unit 40.
Data-capture unit 40, according to from the schedule being far apart monitor unit 50 and sending here, indicates the collection of the traveling voice data of escalator 10 to start to sensor terminal 30.Thus, the sound transducer that sensor terminal 30 is used as sensor part 32 to be equipped with, the traveling voice data of measurement escalator 10, and these measurement data (traveling voice data) are sent to data-capture unit 40.In detail, measurement data by being circled for benchmaring goes out escalator 10 with the position of spot check pedal 11a by pedal position test section 33, and are sent to data-capture unit 40 by sensor terminal 30 in units of week.
The traveling voice data received from sensor terminal 30 is stored into data store 42 by data-capture unit 40.In this case, consider paroxysmal external Speech input, preferably collect the traveling voice data of at least two all parts.Data-capture unit 40, once collect the traveling voice data of the amount of regulation from sensor terminal 30 according to above-mentioned schedule, just will travel voice data and collect end notification to sensor terminal 30.
On the other hand, be far apart monitor unit 50 according to the connection formed by communication line 61 with data-capture unit 40, the traveling voice data of the data store 42 being stored in data-capture unit 40 is reclaimed.At this moment traveling voice data is stored in data store 53 as traveling audio files F1, and utilizes the object ID etc. of this escalator 10 to manage it.
Here, be far apart monitor unit 50 according to the schedule preset, read from the traveling audio files F1 of data store 53 and travel voice data.Again, be far apart monitor unit 50 and read the base sound data corresponding with this traveling voice data from base sound data file F2, and it is stored in the memory device 55 of data processing division 54 together with above-mentioned traveling voice data.Then, be far apart monitor unit 50 and the traveling voice data and base sound data that are stored in this memory device 55 are given to anomaly extracting portion 56, and carry out abnormal sound extraction process and noise reduction process.Further, be far apart monitor unit 50 and read abnormal sound data from abnormal sound file F3, and be stored in the memory device 55 of data processing division 54.Then, be far apart monitor unit 50 and the traveling voice data and abnormal sound data of implementing noise reduction process are given to abnormity detection portion 57, and detect, at escalator 10 place, whether exception (abnormality detection process) occurs.
Below, an example of the action of being far apart monitor unit 50 is described.Here, mainly abnormal sound extraction process, noise reduction process and abnormality detection process are described in detail.
Fig. 4 is the diagram of circuit of an example illustrated by being far apart abnormal sound extraction process that monitor unit 50 carries out, noise reduction process and abnormality detection process.Here, in the data store 53 of being far apart monitor unit 50, when day-to-day operation, by data-capture unit 40, the traveling voice data regularly reclaiming the escalator 10 measured by sensor terminal 30 is preserved as traveling audio files F1.Again, the base sound data of the com-parison and analysis after having carried out frequency analysis to the traveling voice data at escalator 10 initial stage are preserved as base sound file F2.Further, abnormal sound data stored as abnormal sound file F3, these abnormal sound data occur to obtain by making the exception likely occurring in escalator 10 in analog.
First, monitoring personnel operates date-time, the object of specifying and carrying out analyzing according to the rules.Thus, be arranged at the operation voice data that the data processing division 54 being far apart monitor unit selects at least two all parts met from traveling audio files F1, and be stored in the memory device 55(step S1 of data processing division 54).Here, for the purpose of simplifying the description, from travelling the traveling voice data selecting two weeks parts audio files F1, and memory device 55 is stored in.Again, data processing division 54 selects the base sound data corresponding with above-mentioned traveling voice data from base sound file F2, and is stored in (step S2) in memory device 55.Further, data processing division 54 selects whole abnormal sound data from abnormal sound file F3, and is stored in (step S3) in memory device 55.
After this, the traveling voice data of anomaly extracting portion 56 to the two weeks parts be stored in memory device 55 of data processing division 54 carries out the frequency analysis based on respective plural FFT.Then, anomaly extracting portion 56, according to the traveling voice data carrying out said frequencies analysis, reduces the dB(decibel predetermined being stored in the base sound data of memory device 55 respectively) value.Thus, anomaly extracting portion 56, according to the traveling voice data carrying out said frequencies analysis, can distinguish and only extract abnormal sound composition.Anomaly extracting portion 56 carries out reverse plural FFT respectively to the abnormal sound composition extracted, and this abnormal sound composition is transformed to respectively anomaly extracting operation voice data (step S4).That is to say, the anomaly extracting that anomaly extracting portion 56 can obtain two weeks parts travels voice data.
Here, in the present embodiment, the unit of analysis of moment history data (travel voice data and travel voice data with anomaly extracting) is undertaken splitting (separation) by the unit time tn that each predetermines this moment history data.That is to say, as shown in Figure 5, the unit of analysis of moment history data is Tn, Tn+1 ..., Tn+n.Above-mentioned anomaly extracting is travelled voice data and presses unit of analysis Tn, Tn+1 by anomaly extracting portion 56 ..., Tn+n extracts one by one (step S5).That is to say, anomaly extracting portion 56 travels voice data according to the anomaly extracting of each two weeks parts, and the anomaly extracting that can obtain each unit of analysis travels voice data (analyze and travel voice data).
Anomaly extracting portion 56 runs voice data to each analysis and carries out frequency analysis.Then, anomaly extracting portion 56 calculate the unit of analysis Tn of first week that carries out frequency analysis analysis travel voice data and carry out frequency analysis second week unit of analysis Tn analysis traveling voice data arithmetic average.Similarly, anomaly extracting portion 56 calculate successively the unit of analysis Tn+1 ~ Tn+n of first week that carries out frequency analysis analysis travel voice data and carry out frequency analysis second week unit of analysis Tn+1 ~ Tn+n analysis traveling voice data arithmetic average (step S6).Thus, anomaly extracting portion 56 analysis that can obtain as the unit of analysis Tn ~ Tn+n of first week that carries out frequency analysis travels voice data and travels the arithmetic average traveling voice data of the arithmetic average of voice data as the analysis of unit of analysis Tn ~ Tn+n of the second week carrying out frequency analysis.
The anomalous content presumption unit 57a of abnormity detection portion 57 uses above-mentioned arithmetic average to travel voice data and is stored in multiple abnormal sound data implementation relevant treatment of memory device 55.Specifically, abnormity detection portion 57 is obtained and is represented that the arithmetic average of unit of analysis Tn travels voice data and corresponds to the correlation (absolute value of coefficient of correlation) that this arithmetic average travels the degree of correlation of the abnormal sound data at the position of voice data.Here correlation represents that arithmetic average travels the mark statistically of the correlativity (homophylic degree) of voice data and abnormal sound data, gets the real number between 0 ~ 1.The correlativity that relevance values more travels voice data and abnormal sound data close to 0 arithmetic average is lower, more higher close to 1 both correlativity.This correlation, once try to achieve correlation by above-mentioned relevant treatment, is just recorded in (step S7) in the regulation region of memory device 55 by abnormity detection portion 57.
After this, anomalous content presumption unit 57a determines whether that travelling voice data to whole arithmetic average carries out relevant treatment (step S8).When not to (NO of step S8) when whole arithmetic average traveling voice data implementation relevant treatment, voice data should be travelled to the arithmetic average of next unit of analysis and carry out relevant treatment, and will the process of step S7 be turned back to.
When travelling (YES of step S8) voice data carries out relevant treatment to whole arithmetic average, whether anomalous content presumption unit 57a determining storage exists the correlation (step S9) exceeding the threshold value preset in multiple correlations of memory device 55.When there is not the correlation exceeding the threshold value preset (NO of step S9), by display part 58 and notification unit 59, anomalous content presumption unit 57a notifies that monitoring personnel is the abnormal generation (step S10) of escalator 10, and make the release of this action example.
On the other hand, exist when exceeding the correlation of the threshold value preset (YES of step S9), anomalous content presumption unit 57a only from memory device 55 extract defined amount closer to 1 the correlation of numerical value.At this, anomalous content presumption unit 57a from memory device 55 extract numerical value close to 15 correlations (step S11).In addition, the number of the correlation of extraction is 5 is examples, and the number of the correlation of extraction is not limited to this.
Then, anomalous content presumption unit 57a judges anomalous content according to the abnormal sound data used when obtaining above-mentioned correlation, and its content prescribed form is shown in display part 58.Again, the abnormal place presumption unit 57b of generation judges extremely place to occur according to the abnormal sound data used when obtaining above-mentioned correlation, and its abnormal place that occurs is shown in display part 58(step S12 with prescribed form).
In addition, method anomalous content and abnormal generation place being shown in display part 58 is not particularly limited.Such as also can suppose in the image of the escalator 10 comprising spot check pedal 11a, expression anomalous content and the abnormal mark overlap that place occurs are shown.
According to embodiment described above, frequency analysis is carried out and the data obtained and pre-prepd Reference data based on the measurement data (moment history data) to site measurement, because extract the abnormal component being contained in these measurement data, even if so produce deviation in the collection precision of moment history data, also abnormal component can be extracted accurately.Again, by obtaining the correlation of abnormal component and the pre-prepd abnormal data extracted accurately, the exception of passenger conveyors can be detected accurately according to this correlation.That is to say, according to the present embodiment, the abnormity diagnosis of passenger conveyors can be carried out accurately.
At this, with reference to Fig. 6 to Fig. 8, the effect of present embodiment is described in detail.
First, with reference to Fig. 6 (a) to Fig. 6 (d), different from present embodiment, not carrying out frequency analysis and use according to the former state of moment history data and travel voice data, being described carrying out the abnormality diagnostic situation of escalator 10.
Fig. 6 is the mode chart of the example that the traveling voice data collected by sensor terminal 30 is shown.In figure 6 (a), among the traveling voice data of two weeks parts of being collected by sensor terminal 30, show the waveform 101 of the traveling voice data of first week in the lump, and the waveform 101a after the waveform 101 in time t1 to t2 interval is expanded.In Fig. 6 (b), among the traveling voice data of two weeks parts of being collected by sensor terminal 30, show the waveform 102 of the traveling voice data of first week in the lump, and the waveform 102a after expanding has been carried out to the waveform 102 in time t1 to t2 interval.In addition, here, as shown in Fig. 6 (a) and Fig. 6 (b), due to the fault etc. of sensor terminal 30, the collection time opening of the traveling voice data of first week and the traveling voice data of second week is considered as only offset by t3(and produces deviation in the collection precision travelling voice data).
At this moment, become remarkable with difference time abnormal during in order to make usual, the waveform 103 when waveform 101 being added waveform 102 is shown in Fig. 6 (c).In this Fig. 6 (c), in the same manner as Fig. 6 (a) and Fig. 6 (b), display expands the waveform 103a of the waveform 103 in time t1 to t2 interval in the lump.In this case, as mentioned above, because the collection time opening of the traveling voice data of the traveling voice data of first week and second week only offsets t3, although so become remarkable with difference time abnormal when making usual, but create useless noise, the abnormity diagnosis of escalator 10 can not be carried out accurately.
Again, making the noise reduction being contained in waveform 101 and 102, in order to make abnormal to become remarkable, the waveform 104 when obtaining the arithmetic average of waveform 101 and waveform 102 being represented in Fig. 6 (d).In figure (d), in the same manner as Fig. 6 (a) to Fig. 6 (c), represent the waveform 104a expanding the waveform 104 in time t1 to t2 interval in the lump.In this case, as mentioned above, because the collection time opening of the traveling voice data of the traveling voice data of first week and second week only offsets t3, so abnormal time the amplitude of wave form of traveling sound diminish, in the same manner as noted earlier, the abnormity diagnosis of escalator 10 can not be carried out accurately.
As mentioned above, once produce deviation on the collection time opening of the traveling voice data of first week and the traveling voice data of second week, even if so use this traveling voice data by the former state of moment history data, the abnormity diagnosis of escalator 10 can not be carried out accurately.
Then, with reference to Fig. 7 (a) to Fig. 7 (d) and Fig. 8 (a) to Fig. 8 (c), to not being use by the former state of moment history data to travel voice data to carry out the abnormity diagnosis of escalator 10, but the traveling voice data having carried out frequency analysis is used to be described to carry out abnormality diagnostic situation.
Fig. 7 is the example that the traveling voice data carrying out frequency analysis is shown.In Fig. 7 (a), traveling voice data 111a when carrying out frequency analysis to the waveform 101a shown in Fig. 6 (a) is shown.In Fig. 7 (b), traveling voice data 112a when carrying out frequency analysis to the waveform 102a shown in Fig. 6 (b) is shown.In Fig. 7 (c), traveling voice data 114a when carrying out frequency analysis to the waveform 104a shown in Fig. 6 (d) is shown.Further, in Fig. 7 (d), the traveling voice data 115a as the arithmetic average travelling voice data 111a and traveling voice data 112a is shown.
Fig. 8 be illustrate the traveling voice data 111a shown in Fig. 7 and other travel the figure of variance rate when voice data 112a, 114a, 115a make comparisons.In Fig. 8 (a), the variance rate 116 when the traveling voice data 112a shown in traveling voice data 111a and Fig. 7 (b) shown in Fig. 7 (a) makes comparisons is shown.This variance rate 116 is calculated according to " (112a-111a) × 100/111a ".In this case, as mentioned above, in waveform 101a and waveform 102a, although only produce t3 deviation on the collection time opening, by frequency analysis, variance rate can be made to diminish to a certain extent.
In Fig. 8 (b), the variance rate 117 when the traveling voice data 114a shown in traveling voice data 111a and Fig. 7 (c) shown in Fig. 7 (a) makes comparisons is shown.This variance rate 117 is calculated according to " (114a-111a) × 100/111a ".In this case, as mentioned above, although only produce t3 deviation in waveform 101a and waveform 102a on the collection time opening, after trying to achieve the waveform 114a as the arithmetic average of above-mentioned waveform, carry out frequency analysis, above-mentioned deviation cannot have been absorbed, variance rate becomes large.
In Fig. 8 (c), the variance rate 118 when the traveling voice data 115a shown in traveling voice data 111a and Fig. 7 (d) shown in Fig. 7 (a) makes comparisons is shown.This variance rate 118 is calculated according to " (115a-111a) × 100/111a ".In this case, after carrying out frequency analysis at the waveform 101a and waveform 102a that collect each generation deviation on the time opening and absorb above-mentioned deviation, in order to make noise reduction, obtain the arithmetic average of traveling voice data 111a, the 112a carrying out frequency analysis, variance rate can be made thus to become less.
In present embodiment, the data obtained based on carrying out frequency analysis to measurement data (moment history data) and Reference data, extract after being contained in the abnormal component of these measurement data, because obtain the arithmetic average of this abnormal component for each unit of analysis, the effect identical with situation about illustrating in above-mentioned Fig. 8 (c) can be obtained.That is to say, even if produce deviation in the collection precision of measurement data, the impact that this deviation also can be made to cause reduces, and also can make the noise reduction that burst produces.
In addition, in present embodiment, the structure of a week of escalator 10 is detected as the sensor terminal 30 by being arranged at spot check pedal 11a, also proximity transducer can be set at such as Machine Room, top 12, lower mechanical room 13 or truss 15 place, detect escalator 1 with this proximity transducer one week.
Such as use opto-electronic pickup, Magnetic Sensor etc. as proximity transducer.As the structure detection signal of this proximity transducer directly being inputted data-capture unit 40, because the delay of Wireless transceiver can be eliminated, so the synchronous process when sound is collected in many places simultaneously can be reduced.
Again, in present embodiment, be arranged at the sensor terminal 30 of spot check pedal 11a by being circled for benchmaring goes out escalator 10 with the position of spot check pedal 11a by pedal position test section 33, thus as structure measurement data being sent in units of week data-capture unit 40, also such as one week detection signal can be attached to the detection data being converted to each week after detecting data at data-capture unit 40.
That is to say, be not send the structure detecting data week about, but carry out data transmission by data flow.At that time, one week detection signal is attached to detection data, and is converted to the detection data in each week at data-capture unit 40.Thus, because the detection data volume remaining on sensor terminal 30 can be reduced, the loss of sensor terminal can be reduced.
Again, sensor terminal 30 not necessarily will be arranged on spot check pedal 11a, also can be fixedly installed on such as Machine Room, top 12, lower mechanical room 13 or truss 15 etc., and measures the traveling voice data of escalator 10 in this setting place.
Again, in present embodiment, can per interval tn divide the traveling voice data of unit of analysis to elevator 10 1 weeks parts extract, also can such as make the front and back of time tn overlap the to each other a little to extract, wherein, the traveling voice data of one week deal of escalator 10 is split in Such analysis unit for each time tn.Specifically, as shown in Figure 9, also can the front and back 50% of time tn are overlapped extracts data as making.If done like this, can prevent the data before and after time tn from omitting, thus can analyze accurately.
Further, in present embodiment, passenger conveyors is used as escalator and is illustrated, but be not limited to this, also can be applied to the elevator etc. of fixing floor.
In addition, although be illustrated some embodiments of the present invention, these embodiments propose as an example, are not intended to limit scope of invention.The embodiment of these novelties can be implemented with other various forms, and without departing from the scope of spirit of the present invention, can carry out various omission, displacement, change.These embodiments and/or its distortion are all included in scope of the present invention and/or main points, and are also contained in the scope of invention described in the scope with claim and equalization thereof.
Claims (5)
1. an abnormity diagnostic system for passenger conveyors, is characterized in that, comprising:
Storage part, it stores the data measured by the sensor terminal of the determined location being arranged at passenger conveyors;
Frequency analysis portion, its line frequency analysis that measurement data reading of the described sensor terminal being stored in described storage part is gone forward side by side;
Abnormal component extraction unit, it, based on the described measurement data of having been carried out frequency analysis by described frequency analysis portion and the Reference data preset, extracts abnormal component;
Correlation value calculation section, it calculates the measurement of the anomaly extracting corresponding to the abnormal component data extracted by described abnormal component extraction unit and the correlation representing multiple abnormal cause multiple abnormal datas of feature separately respectively;
Abnormity detection portion, it is detected and whether exception occurs based on each correlation calculated by described correlation value calculation section at described passenger conveyors place;
Abnormality estimation portion, it is when detecting abnormal by described abnormity detection portion, based on the abnormal data used when obtaining described each correlation, the details that presumption is abnormal;
Display part, the details of the exception that its display is deduced by described abnormality estimation portion.
2. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, is characterized in that, described correlation value calculation section is for the unit time respectively preset, and more described anomaly extracting measurement data and described each abnormal data, calculate described correlation respectively.
3. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterized in that, described abnormal component extraction unit has noise reduction portion, this noise reduction portion is based on the measurement data of at least two all parts and described Reference data, extract respectively and be contained in the abnormal component that this respectively detects data, by obtaining the arithmetic average of each anomaly extracting measurement data corresponding to these abnormal components, reduce the noise being contained in described each abnormal component.
4. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterized in that, described abnormality estimation portion has anomalous content presumption unit, this anomalous content presumption unit is when described abnormity detection portion detects abnormal, according to the abnormal data used when obtaining described each correlation, judge anomalous content, estimate described abnormal details.
5. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterized in that, described abnormality estimation portion has abnormal generation place presumption unit, there is place presumption unit when described abnormity detection portion detects abnormal in this exception, according to the abnormal data used when obtaining described each correlation, judge extremely place to occur, estimate described abnormal details.
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| JP2013190161A JP5743347B2 (en) | 2013-09-13 | 2013-09-13 | Abnormality diagnosis system for passenger conveyor |
| JP2013-190161 | 2013-09-13 |
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| JP5743347B2 (en) | 2015-07-01 |
| CN104444750B (en) | 2017-05-31 |
| JP2015054780A (en) | 2015-03-23 |
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