CN104444750B - The abnormity diagnostic system of passenger conveyors - Google Patents

Abstract

The present invention provides a kind of abnormity diagnostic system that can accurately carry out the abnormality diagnostic passenger conveyors of passenger conveyors.A kind of abnormity diagnostic system of the passenger conveyors of implementation method includes：Storage part, the data that its storage is measured by the sensor terminal for being arranged at the determined location of passenger conveyors；Frequency analysis portion, its will measurement data read-out go forward side by side line frequency analysis；Abnormal component extraction unit, it is based on measurement data and reference data extracts abnormal component；Correlation value calculation section, its correlation for calculating the anomaly extracting measurement data corresponding to abnormal component and multiple abnormal datas respectively；Abnormity detection portion, it is based on each correlation, and whether detection occurs exception at passenger conveyors；Abnormality estimation portion, it, based on abnormal data, estimates abnormal details in the case where exception is detected；Display part, the abnormal details of its display.

Description

The abnormity diagnostic system of passenger conveyors

The application is with Japanese patent application 2013-190161（The applying date：On September 13rd, 2013）Based on, and according to this Apply for the benefit that enjoys priority.The application by quote this application and comprising entire contents.

Technical field

Embodiments of the present invention are related to a kind of abnormity diagnostic system of passenger conveyors.

Background technology

The passenger conveyors such as escalator or mobile pavement have the multiple pedals annularly connected by chain.Passenger Conveyer is to be driven by motor, and above-mentioned pedal cycle is moved along the guide rail being disposed in inside truss, will be taken on pedal Passenger be transported to from the stopping port of a side the opposing party stopping port structure.

In this passenger conveyors, due to old by articles for use abrasion caused by long-term operating and mounting and adjusting state Change, or foreign matter is mixed into and the reason such as the mischief that is caused by passenger, it may occur that for example moving part friction, stranded etc. is different Often.

This exception often shows as vibration or sound.Generally, there is the exception of vibration or sound at passenger conveyors In the case of, maintenance personal removes live real-world operation passenger conveyors, it is determined which part vibration or sound anomaly occur in, And cause the part of the main cause that the exception occurs to exchange or adjustment operation.Due to stop passenger conveyors in operation Operating service, if so operation needs the time, then very big trouble will be brought to user.

Also, when maintenance personal determines abnormal generation main cause at the scene, according to the skilled journey of the maintenance personal for going to scene Degree is different, and difference is there is in its correctness and swiftness aspect.When the proficiency of maintenance personal is relatively low, can only stop service Only for more time.Therefore, it is intended that certain the abnormal stage of appearance before failure generation just finds this exception in advance, by dimension Operation is repaiied to eliminate exception to avoid failure.

According to this hope, it is proposed that having inside the pedal at passenger conveyors can be sensitive to representing vertical The acceleration transducer that the flip-flop of degree is measured, and to the output signal of the acceleration transducer（Moment history data） Treatment is analyzed, the abnormity diagnostic system of abnormality diagnostic passenger conveyors is carried out.

The content of the invention

However, in the abnormity diagnostic system of above-mentioned passenger conveyors, because being analyzed treatment to moment history data simultaneously Abnormity diagnosis are carried out, if so producing deviation in the collection precision of the moment history data as obtained from acceleration transducer If, the noise for being contained in moment history data will be amplified in analyzing and processing, existing can not accurately carry out abnormity diagnosis Problem.

Even if the problem to be solved by the present invention is that provide it is a kind of produce deviation in the collection precision of moment history data, Also the abnormity diagnostic system of the abnormality diagnostic passenger conveyors of passenger conveyors can be carried out.

A kind of abnormity diagnostic system of the passenger conveyors of implementation method includes：Storage part, its storage is by being arranged at passenger The data that the sensor terminal of the determined location of conveyer is measured；Frequency analysis portion, its institute that will be stored in the storage part State sensor terminal measurement data read-out go forward side by side line frequency analysis；Abnormal component extraction unit, it is based on by the frequency analysis Portion has carried out the measurement data and reference data set in advance of frequency analysis, extracts abnormal component；Correlation value calculation section, Its calculate respectively corresponding to the abnormal component extracted by the abnormal component extraction unit anomaly extracting measurement data with Represent the correlation of the respective multiple abnormal datas of feature of multiple abnormal causes；Abnormity detection portion, it is based on by the correlation Whether each correlation that value calculating part is calculated, detection there is exception at the passenger conveyors；Abnormality estimation portion, it is logical Cross in the case that the abnormity detection portion detects exception, based on the abnormal data used when obtaining each correlation, presumption Abnormal details；Display part, the abnormal details that its display is deduced by the abnormality estimation portion.

The abnormity diagnostic system of the passenger conveyors based on said structure, can accurately carry out passenger conveyors exception Diagnosis.

Brief description of the drawings

Fig. 1 is the figure of the structure of the abnormity diagnostic system for showing the escalator involved by a kind of implementation method.

Fig. 2 is that the sensor terminal of the abnormity diagnostic system of the escalator for showing to constitute same implementation method, data are received Acquisition means, be far apart monitoring arrangement internal structure block diagram.

Fig. 3 is the figure for being shown provided with the data store structure for being far apart monitoring arrangement in same implementation method.

Fig. 4 is to show to be far apart abnormal sound extraction process, the noise drop that monitoring arrangement is carried out by same implementation method Reduction process and a flow chart for example of abnormality detection treatment.

The data analysis unit that Fig. 5 is used when being the treatment for being far apart monitoring arrangement implementation to same implementation method is carried out The figure of explanation.

Fig. 6（a）~（d）It is show to be collected by the sensor terminal of same implementation method one that travels voice data The oscillogram of example.

Fig. 7（a）~（d）It is to show with the traveling sound that monitoring arrangement has carried out frequency analysis that is far apart of same implementation method One figure of example of data.

Fig. 8（a）~（c）It is to show Fig. 7（a）Shown traveling voice data and Fig. 7（b）~（d）Shown its He travels the figure of variance rate when voice data is made comparisons.

Fig. 9 is to show the data analysis that is used during for the treatment for being far apart monitoring arrangement implementation to same implementation method Other figures that unit is illustrated.

Specific embodiment

Below, the abnormity diagnostic system referring to the drawings to the passenger conveyors of implementation method is illustrated.

Additionally, being illustrated using escalator as passenger conveyors example below.

Fig. 1 is the figure of the abnormity diagnostic system structure for showing the escalator involved by a kind of implementation method.In figure 10 represent escalator entirety.

Escalator 10 is for example inclined between the last layer of building and next layer.The escalator 10 is by making Multiple pedals 11 are connected without slot shifting is circulated between the stopping port of top Machine Room 12 and the stopping port of lower mechanical room 13 Dynamic, transport is taken in the passenger on pedal 11.

Each pedal 11 is attached by loop connecting chain 14, and is configured in truss 15, and truss 15 is then set Below the floor of building.In the inside configuration top sprocket 16 and bottom sprocket 17 of truss 15, and between them Winding connection chain 14.

Either one of sprocket 16 and bottom sprocket 17 on top（It is top sprocket 16 in the example）Connection has horse Up to the drive device 18 of, reductor etc..Using the drive device 18, sprocket（Sprocket）16th, 17 are rotated and are passed through The connection chain 14 of sprocket 16,17 is snapped at, multiple pedals 11 are guided on guide rail (not shown) in top Machine Room 12 Loopy moving between the stopping port of stopping port 12 and lower mechanical room 13.

Also, on the top of truss 15, with the two sides of each pedal 11 relatively, set a pair along the moving direction of pedal 11 Skirt guard (not shown).Bannister 19 is set to upper setting respectively in the skirt guard.Banding is installed around the bannister 19 Handrail 20.Handrail 20 is the handrail for taking the passenger's grasping in pedal 11, such as using the driving force for transmitting drive device 18, With pedal 11 it is mobile synchronously be circulated rotation.

Here, a point inspection pedal 11a is at least set in multiple pedals 11 of escalator 10.Point inspection pedal 11a's Inner side, temporarily sets or standing fixed sensor terminal 30.Additionally, the installation method on the sensor terminal 30 because with this Invention is not directly dependent upon, and the description thereof will be omitted here.

The sensor terminal 30 possesses such as Bluetooth（Registration mark）Deng wireless near field communication function, measurement is closed In escalator 10 operation data and be wirelessly transmitted to transacter 40.Below, as being said as a example by situations below It is bright：Sound transducer is built in sensor terminal 30.

Transacter 40 is arranged on the vicinity of sensor terminal 30.In Fig. 1, although show transacter 40 examples for being arranged on lower mechanical room 13, but it is also possible to be arranged on top Machine Room 12.

Transacter 40 possesses as gateway（GW）Function.The transacter 40 is collected and passes through sensor end End 30 measurement traveling voice datas, with specify unit be sent to outside be far apart monitoring arrangement.

Additionally, the transacter 40 can be with many（Such as 4）Sensor terminal 30 carries out radio communication.Cause And, it is separately positioned in the multiple escalator 10 for be for example set up in each floor gap if by sensor terminal 30, then By 1 transacter 40, it becomes possible to which the traveling voice data that will be measured with above-mentioned sensor terminal 30 is sent to and is far apart Monitoring arrangement 40.

To be far apart monitoring arrangement 50 be arranged on be far apart ground central monitoring position 60 in.Multidigit monitoring personnel is resided in central monitoring position 60, in the running status for being far apart on the monitor picture of monitoring arrangement 50 escalator 10 to each object as supervision object Monitored.Being far apart monitoring arrangement 50 and pass through with the transacter 40 for being arranged at escalator 10 in the central monitoring position 60 Communication line 61 is connected.

Additionally, in the example of Fig. 1, although illustrate only 1 escalator 10, but actually each object escalator 10 It is connected in central monitoring position 60 by communication line 61 and is far apart monitoring arrangement 50.Monitoring personnel is once being far apart monitoring arrangement 50 Monitor picture on detect certain exception if, maintenance personal is just sent to scene etc. to be processed.

Fig. 2 is that the sensor terminal of the abnormity diagnostic system of the escalator for showing to constitute same implementation method, data are received Acquisition means, be far apart monitoring arrangement internal structure block diagram.

The system by sensor terminal 30, transacter 40, be far apart monitoring arrangement 50 and constitute.As shown in figure 1, sensing Device terminal 30 is arranged at the point inspection pedal 11a in each pedal 11 of escalator 10.The sensor terminal 30 includes control unit 31st, sensor portion 32, pedal position test section 33, wireless communication part 34.

Control unit 31 carries out the control of sensor terminal 30.Sensor portion 32 measures the number of the operation on escalator 10 According to.In present embodiment, sound transducer is equipped with as the sensor portion 32, measure the traveling voice data of escalator 10.

Pedal position test section 33 is made up of inclination sensor, gyrosensor etc., is base with a position of inspection pedal 11a One week of quasi- detection escalator 10.Wireless communication part 34 carries out wireless near field communication with transacter 40, will pass through The traveling audio data transmitting of the measurement of sensor portion 32 is sent to transacter 40.

Transacter 40 is arranged at the determined location of escalator 10（It is lower mechanical room in the example of Fig. 1 13）.The transacter 40 includes wireless communication part 41, data store 42, transmission control unit 43.

Wireless communication part 41 carries out wireless near field communication between sensor terminal 30 and transacter 40, receives From the traveling voice data that the transmission of sensor terminal 30 comes.Data store 42 stores the traveling received by wireless communication part 41 Voice data.In this case, it is additional to be set in each if there is if many escalators 10 as supervision object The ID ground storage traveling voice data of dynamic staircase 10.Transmission control unit 43 will be stored in the traveling voice data of data store 42 To specify that unit reads, and be sent to be far apart monitoring arrangement 50.

To be far apart monitoring arrangement 50 be arranged on be present in be far apart ground central monitoring position 60 in, be connected to by communication line 61 It is arranged at the transacter 40 of escalator 10.The Long-Range Surveillance Unit 50 includes data sending and receiving department 51, operation inputting part 52nd, data store 53, data processing division 54, display part 58, notification unit 59.

Data sending and receiving department 51 carries out the transmitting-receiving process of various data.Operation inputting part 52 is made up of keyboard etc., according to monitoring The operation of personnel carries out data input, indicates.

As shown in figure 3, in data store 53, the traveling sound of the escalator 10 that will be obtained from transacter 40 Sound data are stored as traveling audio files F1.Also, in the data store 53, will be installed according to escalator 10 When, after the inspection of maintenance personal's point the traveling voice data of measured in advance and the base sound data of com-parison and analysis that are made as base Quasi- audio files F2 is stored.Each position comprising escalator 10 in base sound file F2（Such as rolled portion, direct acting Portion etc.）Base sound data.Further, in the data store 53, using abnormal sound data as abnormal sound text Part F3 is stored, and the abnormal sound data are taken in abnormal the generation with simulating of escalator 10 by making it possible to generation .Abnormal sound data are directed to the frequency that each represents each abnormal sound with various abnormal causes that abnormal sound is produced The data of rate composition characteristics.Specifically, using the abnormal sound data on abnormal sound as described below as abnormal sound File F3 is stored.

（1）The abnormal sound that pedal and skirt guard are contacted and produced；

（2）The abnormal sound that foreign matter is produced by guide rail；

（3）The abnormal sound produced due to the lax of connection chain（Pedal inverts sound）；

（4）The abnormal sound that stopping port rubber and pedal clamping plate are contacted and produced；

（5）Pedal trailing wheel roller bearing deforms or abnormal sound that is impaired and producing；

（6）The abnormal sound that the bearing of pedal trailing wheel roller bearing is bad and produces.

Additionally, the preservation form of base sound data and abnormal sound data, can be by base sound and abnormal sound Wave data is preserved with wav file form, it is also possible to is preserved and is passed through FFT（Fast Fourier Transform：Fast Flourier Conversion）The data after frequency analysis are carried out to base sound and abnormal sound.Below, for the purpose of simplifying the description, in data store In 53, as being illustrated as a example by situations below：The data of frequency analysis as base sound data and different will in advance have been carried out Normal voice data is preserved.

Data processing division 54 is made up of microprocessor etc., and the traveling voice data to escalator 10 is analyzed treatment. The data processing division 54 includes memory 55, anomaly extracting portion（Noise reduction portion）56 and abnormity detection portion 57.

Memory 55 is the working storage for interim storage.In the memory 55, store from data store 53 The traveling voice datas that read of traveling audio files F1, from the base sound data that base sound file F2 reads, Yi Jicong The abnormal sound data that abnormal sound file F3 reads.

56 pairs, the anomaly extracting portion traveling voice data read from memory 55 carries out frequency analysis by plural number FFT.Also, Anomaly extracting portion 56 is come using the traveling voice data for having carried out frequency analysis and the base sound data for being stored in memory 55 Extract abnormal sound composition.Further, anomaly extracting portion 56 passes through the abnormal sound composition change that reverse plural number FFT will have been extracted It is changed to anomaly extracting traveling voice data（Abnormal sound extraction process）.Also, analysis of the anomaly extracting portion 56 for each regulation Unit, implementation processes the noise reduction of the noise reduction for being contained in anomaly extracting traveling voice data.Noise is external environment condition Sound, for example, the sound that its expression is walked by passenger and produced on the pedal of escalator 10, or produce in escalator 10 Around sound etc..

Abnormity detection portion 57 is whether detection occurs abnormal part at escalator 10, by anomalous content presumption unit 57a and abnormal generation place presumption unit 57 are constituted.Additionally, actually the abnormity detection portion 57 by calculation procedure algorithm come real It is existing.

Anomalous content presumption unit 57a travels voice data and is stored in using the anomaly extracting for implementing noise reduction treatment The abnormal sound data of reservoir 55, carry out relevant treatment, and whether detection occurs exception at escalator 10.Also, anomalous content Presumption unit 57a estimates the abnormal content in the case where exception is detected.It is abnormal that place presumption unit 57b occurs by different In the case that normal content presumption unit 57a detects exception, the where position for anomaly occurring in escalator 10 is estimated.

Display part 58 shows the result of abnormity detection portion 57 with prescribed form.Also, notification unit 59 is in abnormity detection portion In the case of there is the place for needing point inspection in 57 result, the situation is notified by display, sound etc..

In this configuration, first, the operation input of monitoring arrangement 50 is far apart in monitoring personnel operation in central monitoring position 60 Portion 52, input regularly carries out the schedule of the traveling sound collecting of escalator 10 and is sent to transacter 40.

Transacter 40 is indicated sensor terminal 30 automatically according to from the schedule that monitoring arrangement 50 is sent is far apart The collection of the traveling voice data of staircase 10 starts.Thus, sensor terminal 30 uses the sound being equipped with as sensor portion 32 Sensor, measures the traveling voice data of escalator 10, and by the measurement data（Traveling voice data）It is sent to data receipts Acquisition means 40.In detail, sensor terminal 30 by by pedal position test section 33 on the basis of a position of inspection pedal 11a Detect that escalator 10 has circled, and measurement data are sent to transacter 40 in units of week.

The traveling voice data that transacter 40 will be received from sensor terminal 30 is stored to data store 42. In such case, it is contemplated that paroxysmal external sound input, preferably collects at least two weeks traveling voice datas of part.Data Collection device 40 is once collected if the traveling voice data of amount of regulation according to above-mentioned schedule from sensor terminal 30, just will Traveling voice data collects end notification to sensor terminal 30.

On the other hand, it is far apart monitoring arrangement 50 according to the connection formed by communication line 61 with transacter 40, Traveling voice data to being stored in the data store 42 of transacter 40 is reclaimed.At this moment traveling voice data Data store 53 is stored in as traveling audio files F1, and object ID using the escalator 10 etc. is carried out to it Management.

Here, it is far apart monitoring arrangement 50 according to schedule set in advance, from the traveling audio files F1 of data store 53 It is middle to read traveling voice data.Also, be far apart monitoring arrangement 50 being read from base sound data file F2 and the traveling sound number According to corresponding base sound data, and it is stored the memory 55 of data processing division 54 together with above-mentioned traveling voice data In.Then, it is far apart monitoring arrangement 50 and will be stored in the traveling voice data and base sound data of the memory 55 and is given to exception Extraction unit 56, and carry out abnormal sound extraction process and noise reduction treatment.Further, it is far apart monitoring arrangement 50 from abnormal sound Abnormal sound data are read in sound file F3, and is stored in the memory 55 of data processing division 54.Then, it is far apart monitoring arrangement The 50 traveling voice datas and abnormal sound data that will implement noise reduction treatment are given to abnormity detection portion 57, and detect automatic Whether there is exception at staircase 10（Abnormality detection treatment）.

Below, an example for being far apart the action of monitoring arrangement 50 is illustrated.Here, mainly abnormal sound is extracted Treatment, noise reduction treatment and abnormality detection treatment are described in detail.

Fig. 4 is shown by being far apart abnormal sound extraction process, noise reduction treatment and exception that monitoring arrangement 50 is carried out One flow chart of example of detection process.Here, in the data store 53 for being far apart monitoring arrangement 50, in day-to-day operation When, by transacter 40, regularly reclaim the traveling sound number of the escalator 10 measured by sensor terminal 30 Preserved according to as traveling audio files F1.Also, the traveling voice data to the initial stage of escalator 10 has been carried out into frequency Com-parison and analysis after analysis with base sound data preserved as base sound file F2.Further, by abnormal sound Sound data are stored as abnormal sound file F3, and the abnormal sound data are by making it possible to occur in escalator 10 Abnormal simulation ground occur and obtain.

First, monitoring personnel operates date-time, the object specified and be analyzed according to the rules.Thus, it is arranged at and is far apart The data processing division 54 of the monitoring arrangement at least two weeks operation voice datas of part that selection meets from traveling audio files F1, and It is stored in the memory 55 of data processing division 54（Step S1）.Here, for the purpose of simplifying the description, selected from traveling audio files F1 Two weeks traveling voice datas of part, and it is stored in memory 55.Also, data processing division 54 selected from base sound file F2 with The corresponding base sound data of above-mentioned traveling voice data, and be stored in memory 55（Step S2）.Further, at data Reason portion 54 selects whole abnormal sound data from abnormal sound file F3, and is stored in memory 55（Step S3）.

Hereafter, 56 pairs, the anomaly extracting portion of data processing division 54 is stored in the traveling sound number of two weeks parts in memory 55 According to the frequency analysis for be based on respective plural FFT.Then, anomaly extracting portion 56 is according to the row for having carried out said frequencies analysis Voice data is sailed, the pre-determined dB of the base sound data for being stored in memory 55 is reduced respectively（Decibel）Value.Thus, it is different Normal extraction unit 56 can only extract abnormal sound composition respectively according to the traveling voice data for having carried out said frequencies analysis.It is different The abnormal sound composition that normal extraction unit 56 pairs has been extracted carries out reverse plural number FFT respectively, and the abnormal sound composition is become respectively It is changed to anomaly extracting operation voice data（Step S4）.I other words, anomaly extracting portion 56 can obtain two weeks anomaly extractings of part Traveling voice data.

Here, in the present embodiment, by moment history data（Traveling voice data and anomaly extracting traveling sound number According to）Unit of analysis split by each unit interval tn for predetermining the moment history data（Separate）.I other words, such as Shown in Fig. 5, the unit of analysis of moment history data is Tn, Tn+1 ..., Tn+n.Anomaly extracting portion 56 is by above-mentioned anomaly extracting row Voice data is sailed by unit of analysis Tn, Tn+1 ..., Tn+n is extracted one by one（Step S5）.I other words, anomaly extracting portion 56 is according to each The anomaly extracting traveling voice data of individual two weeks parts, can obtain the anomaly extracting traveling voice data of each analysis unit（Point Analysis traveling voice data）.

Respectively analysis operation voice data carries out frequency analysis in 56 pairs, anomaly extracting portion.Then, anomaly extracting portion 56 calculates The analysis traveling voice data for having carried out the unit of analysis Tn of first week of frequency analysis and the second week for having carried out frequency analysis Unit of analysis Tn analysis traveling voice data arithmetic average.Similarly, anomaly extracting portion 56 calculates successively has been carried out The analysis traveling voice data of the unit of analysis Tn+1~Tn+n of first week of frequency analysis and carry out the second of frequency analysis The arithmetic average of the analysis traveling voice data of the unit of analysis Tn+1~Tn+n in week（Step S6）.Thus, anomaly extracting portion 56 Can obtain and travel voice data and conduct as the analysis of the unit of analysis Tn~Tn+n of carried out frequency analysis first week The arithmetic for having carried out the arithmetic average of the analysis traveling voice data of the unit of analysis Tn~Tn+n of the second week of frequency analysis is put down Travel voice data.

The anomalous content presumption unit 57a of abnormity detection portion 57 travels voice data and is stored in using above-mentioned arithmetic average Multiple abnormal sound data of reservoir 55 carry out relevant treatment.Specifically, abnormity detection portion 57 obtains expression unit of analysis Tn Arithmetic average traveling voice data and corresponding to the arithmetic average traveling voice data position abnormal sound data phase The correlation of pass degree（The absolute value of coefficient correlation）.Here correlation is to represent that arithmetic average travels voice data and exception The correlation of voice data（Homophylic degree）Mark statistically, take the real number between 0~1.Relevance values are got over and are connect The correlation of nearly 0 arithmetic average traveling voice data and abnormal sound data is lower, and closer to 1, both correlations are got over It is high.Abnormity detection portion 57 tries to achieve correlation upon above-mentioned relevant treatment, and the correlation just is recorded into the rule in memory 55 In determining region（Step S7）.

Hereafter, anomalous content presumption unit 57a determines whether to carry out at correlation whole arithmetic averages traveling voice data Reason（Step S8）.In the case where relevant treatment is not carried out to whole arithmetic averages traveling voice data（The NO of step S8）, Relevant treatment should be carried out to the arithmetic average of next unit of analysis traveling voice data, and the treatment of step S7 will be returned to.

In the case where relevant treatment is carried out to whole arithmetic averages traveling voice data（The YES of step S8）, it is abnormal Content presumption unit 57a judgements whether there is the correlation for exceeding threshold value set in advance in being stored in multiple correlations of memory 55 Value（Step S9）.In the case of the correlation more than threshold value set in advance（The NO of step S9）, anomalous content presumption Portion 57a notifies that monitoring personnel does not have abnormal in escalator 10 by display part 58 and notification unit 59（Step S10）, and make The release of this action example.

On the other hand, in the case of there is the correlation more than threshold value set in advance（The YES of step S9）, anomalous content Presumption unit 57a only extracts the correlation of the numerical value of closer the 1 of defined amount from memory 55.Here, anomalous content presumption unit 57a extracts 5 correlations that numerical value is close to 1 from memory 55（Step S11）.Additionally, the number of the correlation for extracting is one for 5 Individual example, the unlimited number of the correlation of extraction schedules this.

Then, anomalous content presumption unit 57a judges abnormal according to the abnormal sound data used when obtaining above-mentioned correlation Content, and its content prescribed form is shown in display part 58.Also, abnormal generation place presumption unit 57b is above-mentioned according to obtaining The abnormal sound data used during correlation judge abnormal generation place, and its generation place extremely is shown in prescribed form Display part 58（Step S12）.

Additionally, being not particularly limited the method that anomalous content and abnormal generation place are shown in into display part 58.For example It can also be assumed that in comprising an image of the escalator 10 of inspection pedal 11a, making expression anomalous content and abnormal generation place Mark overlap and shown.

Implementation method from the description above, based on the measurement data to site measurement（Moment history data）Enter line frequency The data and pre-prepd reference data analyzed and obtain, because extracting the abnormal component for being contained in the measurement data, Even if producing deviation in the collection precision of moment history data, it is also possible to accurately extract abnormal component.Also, by obtaining The abnormal component and the correlation of pre-prepd abnormal data for accurately extracting, can be accurately according to the correlation Detect the exception of passenger conveyors.I other words, according to present embodiment, can accurately carry out the exception of passenger conveyors Diagnosis.

Here, reference picture 6 is to Fig. 8, the effect to present embodiment is described in detail.

First, reference picture 6（a）To Fig. 6（d）, it is different from present embodiment, frequency analysis is not carried out and shines moment resume number According to use as former state traveling voice data, to carry out escalator 10 abnormality diagnostic situation illustrate.

Fig. 6 is the oscillogram for example for travelling voice data for showing to be collected by sensor terminal 30.In Fig. 6 （a）In, among the traveling voice data of the two weeks parts collected by sensor terminal 30, the traveling sound number of first week is shown in the lump According to waveform 101, and the waveform 101a after the waveform 101 interval to time t1 to t2 is enlarged.In Fig. 6（b）In, by passing Among the traveling voice data of two weeks parts that sensor terminal 30 is collected, the waveform of the traveling voice data of first week is shown in the lump 102, and the waveform 102a after the waveform 102 interval to time t1 to t2 is expanded.Additionally, herein, such as Fig. 6（a）And Fig. 6（b）It is shown, due to the failure etc. of sensor terminal 30, the traveling voice data and the traveling sound number of second week of first week According to the collection time started be considered as and only offset by t3（Deviation is produced in the collection precision of traveling voice data）.

At this moment, in order that generally when with it is abnormal when difference become notable, in the case that waveform 101 is added into waveform 102 Waveform 103 be shown in Fig. 6（c）.In the Fig. 6（c）In, with Fig. 6（a）And Fig. 6（b）Similarly, display expands the time in the lump The waveform 103a of t1 to t2 interval waveform 103.In this case, as described above, because the traveling voice data of first week The collection time started with the traveling voice data of second week only offsets t3, so while difference when making usual and when abnormal becomes Obtain significantly, but generate useless noise, it is impossible to accurately carry out the abnormity diagnosis of escalator 10.

Also, making to be contained in the noise reduction of waveform 101 and 102, in order that abnormal become notable, the He of waveform 101 will be obtained Waveform 104 in the case of the arithmetic average of waveform 102 is represented in Fig. 6（d）In.In figure（d）In, with Fig. 6（a）To Fig. 6（c）Together Sample ground, represents the waveform 104a for expanding the interval waveforms 104 of time t1 to t2 in the lump.In this case, as described above, because For the collection time started of the traveling voice data of first week and the traveling voice data of second week only offsets t3, so when abnormal The amplitude of wave form of traveling sound diminish, in the same manner as noted earlier, it is impossible to which the exception for accurately carrying out escalator 10 is examined It is disconnected.

As described above, when the collection of the traveling voice data at first week and the traveling voice data of second week starts Between upper produce deviation, then even if use the traveling voice data as former state by moment history data, can not accurately reality The abnormity diagnosis of row escalator 10.

Then, reference picture 7（a）To Fig. 7（d）And Fig. 8（a）To Fig. 8（c）, to being made by the former state of moment history data Carry out the abnormity diagnosis of escalator 10 with traveling voice data, but using having carried out the traveling voice data of frequency analysis Illustrated to carry out abnormality diagnostic situation.

Fig. 7 is an example for showing to have carried out the traveling voice data of frequency analysis.In Fig. 7（a）In, show to Fig. 6 （a）Shown waveform 101a carries out traveling voice data 111a during frequency analysis.In Fig. 7（b）In, show to Fig. 6（b）It is shown Traveling voice data 112as of waveform 102a when carrying out frequency analysis.In Fig. 7（c）In, show to Fig. 6（d）Shown waveform 104a carries out traveling voice data 114a during frequency analysis.Further, in Fig. 7（d）In, it is shown as travelling voice data The traveling voice data 115a of the arithmetic average of 111a and traveling voice data 112a.

Fig. 8 is to show that the traveling voice data 111a shown in Fig. 7 makees with other traveling voice datas 112a, 114a, 115a The figure of variance rate when comparing.In Fig. 8（a）In, Fig. 7 is shown（a）Shown traveling voice data 111a and Fig. 7（b）Shown Variance rate 116 when traveling voice data 112a makes comparisons.According to "（112a-111a）× 100/111a " calculates the variance rate 116.In this case, as described above, in waveform 101a and waveform 102a, although only produce t3 on the time started is collected Deviation, but by frequency analysis, variance rate can be made to diminish to a certain extent.

In Fig. 8（b）In, Fig. 7 is shown（a）Shown traveling voice data 111a and Fig. 7（c）Shown traveling voice data Variance rate 117 when 114a makes comparisons.According to "（114a-111a）× 100/111a " calculates the variance rate 117.In this feelings Under condition, as described above, although only produce t3 deviations on the time started is collected in waveform 101a and waveform 102a, try to achieve work After the waveform 114a of the arithmetic average of above-mentioned waveform, frequency analysis is carried out, it is impossible to absorbed above-mentioned deviation, variance rate becomes Greatly.

In Fig. 8（c）In, Fig. 7 is shown（a）Shown traveling voice data 111a and Fig. 7（d）Shown traveling voice data Variance rate 118 when 115a makes comparisons.According to "（115a-111a）× 100/111a " calculates the variance rate 118.In this feelings Under condition, waveform 101a and waveform 102a to each generation deviation on the time started is collected carry out frequency analysis and absorb above-mentioned After deviation, in order that noise reduction, obtains the arithmetic average of traveling voice data 111a, 112a for having carried out frequency analysis, Thus enable that variance rate becomes smaller.

In present embodiment, based on to measurement data（Moment history data）The data and base for carrying out frequency analysis and obtaining Quasi- data, after extraction is contained in the abnormal component of the measurement data, because obtaining the abnormal component for each unit of analysis Arithmetic average, can obtain with above-mentioned Fig. 8（c）The situation identical effect of middle explanation.I other words, even if in measurement data Collection precision on produce deviation, it is also possible to make the influence reduction that the deviation causes, and can also make the noise that burst produces Reduce.

Additionally, in present embodiment, helped automatically as being detected by being arranged at a sensor terminal 30 of inspection pedal 11a The structure of a week of ladder 10, it is also possible to sensing is positioned proximate at such as top Machine Room 12, lower mechanical room 13 or truss 15 Device, is detected one week of escalator 1 with the proximity transducer.

For example with photoelectric sensor, Magnetic Sensor etc. as proximity transducer.Believe as by the detection of the proximity transducer Number the structure of transacter 40 is directly inputted, because the delay of Wireless transceiver can be eliminated, it is possible to reducing in many places The synchronization process in the case of sound is collected simultaneously.

Also, in present embodiment, being arranged at a sensor terminal 30 of inspection pedal 11a by by pedal position test section 33 Detect that escalator 10 circles on the basis of a position of inspection pedal 11a, thus conduct will measure data in units of week It is sent to the structure of transacter 40, it is also possible to for example one week detection signal is attached to after detection data and is received in data Acquisition means 40 are converted to the detection data in each week.

I other words, it is not the structure for sending detection data week about, but data is activation is carried out by data flow.That When, one week detection signal is attached to detection data, and the detection data in each week is converted in transacter 40.By This, because the detection data amount for being maintained at sensor terminal 30 can be reduced, it is possible to reducing the loss of sensor terminal.

Also, sensor terminal 30 may be not necessarily provided at an inspection pedal 11a, it is also possible to be fixedly installed on such as top machinery Room 12, lower mechanical room 13 or truss 15 etc., and determine the traveling voice data of escalator 10 in the setting place.

Also, in present embodiment, the unit of analysis that can be divided every time tn is to the 10 1 weeks traveling sound of part of elevator Data are extracted, it is also possible to for example extracted front and rear somewhat the overlapping the to each other of time tn, wherein, Such analysis unit For the traveling voice data of one week deal of each time tn segmentation escalators 10.Specifically, as shown in figure 9, also may be used Overlapped data are extracted as making before and after time tn 50%.If it does, the data before and after being prevented from time tn Omit such that it is able to accurately analyzed.

Further, in present embodiment, passenger conveyors are illustrated as escalator, but are not limited to This, it is also possible to it is applied to fix elevator of floor etc..

In addition, though some implementation methods of the invention are illustrated, but these implementation methods are carried as example Go out, it is not intended to which the scope to inventing is defined.These novel implementation methods can be implemented with other various forms, and And without departing from the scope of spirit of the present invention, various omissions, displacement, change can be carried out.These implementation methods and/or its Deformation is all contained in the scope of the present invention and/or main points, and is also contained in hair described in the scope with claim In bright and its impartial scope.

Claims (4)

1. a kind of abnormity diagnostic system of passenger conveyors, it is characterised in that including：

Storage part, the data that its storage is measured by the sensor terminal for being arranged at the determined location of passenger conveyors；

Frequency analysis portion, the measurement data read-out of its sensor terminal that will be stored in the storage part is gone forward side by side line frequency point Analysis；

Abnormal component extraction unit, it is based on having carried out the measurement data of frequency analysis by the frequency analysis portion and sets in advance Fixed reference data, extracts abnormal component；

Correlation value calculation section, it calculates different corresponding to the abnormal component extracted by the abnormal component extraction unit respectively Often extract measurement data and represent the correlation of the respective multiple abnormal datas of feature of multiple abnormal causes；

Abnormity detection portion, it is based on each correlation calculated by the correlation value calculation section, and detection is in passenger conveying Whether there is exception at machine；

Abnormality estimation portion, its in the case where exception is detected by the abnormity detection portion, based on obtaining each correlation When the abnormal data that uses, estimate abnormal details；

Display part, the abnormal details that its display is deduced by the abnormality estimation portion,

The abnormal component extraction unit has noise reduction portion, and the noise reduction portion is based on the measurement data and institute of at least two weeks parts Reference data is stated, the abnormal component for being contained in each detection data is extracted respectively, by obtaining corresponding to these abnormal components The arithmetic average of each anomaly extracting measurement data is contained in each abnormal component, measurement data collection precision to reduce Deviation caused by noise.

2. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterised in that the correlation value calculation section pin To each unit interval set in advance, anomaly extracting measurement data and each abnormal data are respectively compared, calculate institute State correlation.

3. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterised in that the abnormality estimation portion has Anomalous content presumption unit, the anomalous content presumption unit in the case where the abnormity detection portion detects exception, according to obtaining The abnormal data used during each correlation is stated, anomalous content is judged, the abnormal details is estimated.

4. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterised in that the abnormality estimation portion has It is abnormal that place presumption unit occurs, exception generation place presumption unit in the case where the abnormity detection portion detects exception, root According to the abnormal data used when obtaining each correlation, abnormal generation place is judged, estimate the abnormal details.