CN104176613B - The abnormity diagnostic system of passenger conveyors - Google Patents
The abnormity diagnostic system of passenger conveyors Download PDFInfo
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- CN104176613B CN104176613B CN201310303888.1A CN201310303888A CN104176613B CN 104176613 B CN104176613 B CN 104176613B CN 201310303888 A CN201310303888 A CN 201310303888A CN 104176613 B CN104176613 B CN 104176613B
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Abstract
The abnormity diagnostic system of the passenger conveyors that the present invention discloses, while being changed by the state detecting passenger conveyors and making to be reflected in maintenance activity the burden alleviating maintenance man, and is the most suitably disposed.Long-Range Surveillance Unit (50) includes；The data store (53) of the measurement data of the sensor terminal (30) that storage obtains from transacter (40), read measurement data with the unit of analysis of each regulation from data store (53) to go forward side by side the frequency analysis portion (56a) that line frequency analyzes, the correlation coefficient calculating section (56b) of correlation coefficient is calculated according to the measurement data after frequency analysis and benchmark data set in advance, state change detection unit (56c) of the state change of passenger conveyors, and the display part (57) of display result of determination is judged according to correlation coefficient.
Description
The application is by the Japanese Patent application 2013-109036(applying date: based on 5/23/2013), according to this application
Enjoy priority.By referring to this application, the application comprises the whole of disclosure of which.
Technical field
The passenger that embodiments of the present invention relate to diagnosing such as escalator (escalator) and speedwalk etc. is defeated
Send the abnormity diagnostic system of the abnormal passenger conveyors of machine (man conveyor).
Background technology
The passenger conveyors of escalator and speedwalk etc., including the multiple pedals connecting into annular with chain.Logical
Cross and make to move along these pedals are disposed in the rail circulation within framework with motor, the passenger on pedal will be taken
The stopping port of the opposing party it is transported to from the stopping port of a side.
In the passenger conveyors of this structure, because of abrasion, the mounting and adjusting state chronological aging of articles for use, or
, there is the exception of such as moving part friction etc. in the reason of trick etc. that foreign body is mixed into or is caused by passenger.
Such exception, often shows as vibration or sound.Maintenance man's one side runs passenger conveyors, and one side determines shakes
Dynamic or sound anomaly occur in which part, carry out becoming exchange or the tune of the part of the main cause of this exception generation
Whole operation.
Here, because of the operating service of passenger conveyors to be stopped in operation, user is brought greatest trouble.At this moment,
Proficiency level according to maintenance man is different, there is difference in correctness and swiftness aspect.I other words, unskilled
In maintenance man, because the time of operation is long, so service can only be stopped for a long time.
Therefore, it is intended that the stage of certain exception occurs before the failure occurs, find this exception in advance, by dimension
Repair operation and eliminate abnormal, avoid fault.According to such hope, it is proposed that arrange in the pedal of passenger conveyors and add
Velocity sensor, the signal analyzing and processing this acceleration transducer the exception carrying out abnormality diagnostic passenger conveyors are examined
The motion of disconnected system.
But, in conventional system, there is problems of, abnormal for detection, statistical disposition is necessary, is being judged as
Require time for before exception.Also, because the state before not knowing fault appearance changes, maintenance man must detect exception
Time go to emergency action immediately.Therefore, the increased frequency of turning out for work of the maintenance man beyond regularly checking, to maintenance man
Increase one's load.
Summary of the invention
The problem to be solved in the present invention is to provide and can be changed by the state detecting passenger conveyors and make to be reflected in dimension
While repairing the burden alleviating maintenance man in operation, the passenger conveyors suitably can disposed before fault occurs
Abnormity diagnostic system.
The abnormity diagnostic system of the passenger conveyors of present embodiment, including: it is arranged at the ground of the regulation of passenger conveyors
The sensor terminal of the carried out radio communication of side, collects the transacter of the data measured by this sensor terminal,
And be connected on this transacter through communication line, and obtain described sensor terminal from described transacter
The Long-Range Surveillance Unit of measurement data.
Described Long-Range Surveillance Unit, possesses: the storage of the described measurement data that storage obtains from described transacter
Portion, reads the go forward side by side frequency of line frequency analysis of described measurement data for the unit of analysis of each regulation from this storage part and divides
Analysis portion, calculates relevant according to the described measurement data after this frequency analysis portion frequency analysis to benchmark data set in advance
The correlation coefficient calculating section of coefficient, judges described passenger conveyors according to the correlation coefficient that this correlation coefficient calculating section calculates
The state change detection unit of state change, and, show the display part of result of determination of this state change detection unit.
The abnormity diagnostic system of the passenger conveyors according to above-mentioned composition, then can be by detecting the state of passenger conveyors
While changing and making to be reflected in maintenance activity the burden alleviating maintenance man, and carry out before the failure occurs suitably
Ground is disposed.
Accompanying drawing explanation
Fig. 1 is the figure of the composition of the abnormity diagnostic system of the escalator illustrating the 1st embodiment.
Fig. 2 is to illustrate the composition sensor terminal of abnormity diagnostic system of escalator of this embodiment, data collection
Device, the block diagram of Inner Constitution of Long-Range Surveillance Unit.
Fig. 3 is the figure of the composition of the data store illustrating that the Long-Range Surveillance Unit of this embodiment possesses.
Fig. 4 is the flow chart of the detection process of the state change practiced by the Long-Range Surveillance Unit illustrating this embodiment.
Fig. 5 is the direct acting portion that is divided into for a week illustrating the escalator by the 2nd embodiment and traveling district during rotation section
The figure divided.
Fig. 6 is the flow chart of the detection process of the state change practiced by the Long-Range Surveillance Unit illustrating this embodiment.
Fig. 7 is that the state change detection unit illustrating Long-Range Surveillance Unit preferably arranges abnormal data extraction unit
Time pie graph.
Detailed description of the invention
Hereinafter, with reference to the accompanying drawings of embodiment.
Also, below as a example by the one i.e. escalator of passenger conveyors, illustrate.In each figure, to identical or phase
When part be marked with identical symbol, its explanation suitably simplifies or even omits.
(the 1st embodiment)
Fig. 1 is the figure of the composition of the abnormity diagnostic system of the escalator illustrating the 1st embodiment.In figure 10 illustrate
Escalator whole.
Escalator 10 is arranged between the last layer of such as building and next layer obliquely.This escalator 10,
By making the multiple pedals (step) 11 seamlessly the connected stopping port in Machine Room, top 12 and lower mechanical
Loopy moving between the stopping port of room 13, transports the passenger taken on pedal 11.
Each pedal 11 is connected by ring-type connection chain 14, is configured at the underfloor framework 15 being located at building
In.In the inside of framework 15, configuration top sprocket wheel (top sprocket) 16 and bottom sprocket wheel (bottom sprocket)
17, connect chain 14 and be reeved between them.
The driving means 18 with motor and speed reducer etc. is connected either one of top sprocket wheel 16 and bottom sprocket wheel 17
On (this example being at top sprocket wheel 16).This driving means 18 is utilized to carry out rotating sprocket 16,17.Thus, multiple
Pedal 11 one side is guided on not shown guide rail through connecting chain 14, the one side stopping port in Machine Room, top 12
And loopy moving between the stopping port of the Machine Room 13 of bottom.
It addition, on the top of framework 15, make two sides with each pedal 11 to ground, along the moving direction of pedal 11
A pair not shown skirt panel (skirt guard) is set.Railing 19 is erected on this pair skirt panel respectively.At this
The surrounding of railing 19 is installed with the handrail (hand rail) 20 of banding.Handrail 20 is to take the passenger on pedal 11
The handrail helped, by such as transmitting the driving force of driving means 18, synchronously turn-takes with the movement of pedal 11.
Here, using at least one among multiple pedals 11 of escalator 10 as checking pedal 11a.Sensor
Terminal 30 is fixed on the inner side of this inspection pedal 11a temporarily or chronically.Installation about this sensor terminal 30
Method, because being not directly dependent upon with the present invention.Therefore the description thereof will be omitted here.
Sensor terminal 30 possesses such as Blue tooth(registered trade mark) etc. wireless near field communication function.This biography
Sensor terminal 30 measures the relevant data that operate of escalator 10, and is wirelessly transmitted to transacter 40.With
Under, the situation that sound transducer is built in sensor terminal 30 is described.
Transacter 40 is arranged at the vicinity of sensor terminal 30.In Fig. 1, it is shown that in lower mechanical room 13
The middle example that transacter 40 is set, but this transacter 40 also can be set in Machine Room, top 12.
Transacter 40 has the function as door (GW).This transacter 40 is collected by sensing
The traveling voice data that device terminal 30 is measured, and be sent in the Long-Range Surveillance Unit 50 of outside with the unit of regulation.
Transacter 40 can be with multiple stage (such as 4) sensor terminal 30 radio communication.Thus, if will
Sensor terminal 30 is respectively arranged in the multiple escalator 10 that the most each floor gap sets up, then can these be sensed
The traveling voice data that device terminal 30 is measured is sent in Long-Range Surveillance Unit 50 through 1 transacter 40.
Long-Range Surveillance Unit 50 is arranged in central monitoring position 60 remotely.At central monitoring position 60, have majority stationed
Surveillant, monitors the escalator as each equipment monitoring object on the monitor picture of Long-Range Surveillance Unit 50
The operating condition of 10.Long-Range Surveillance Unit 50 in this central monitoring position 60, through communication line 61 be located at escalator
Transacter 40 in 10 is connected.
In the example of Fig. 1, only diagram 1 escalator 10, but, the escalator 10 of actually each equipment is through logical
The Long-Range Surveillance Unit 50 that letter circuit 61 is connected in central monitoring position 60.Surveillant one is at Long-Range Surveillance Unit 50
Monitor picture on detect that some is abnormal, just sends maintenance man to arrive site disposal.
Fig. 2 illustrates the composition sensor terminal of abnormity diagnostic system of escalator of this embodiment, data collection dress
Put, the block diagram of the Inner Constitution of Long-Range Surveillance Unit.
Native system is made up of sensor terminal 30, transacter 40 and Long-Range Surveillance Unit 50.Such as Fig. 1 such as
Showing, sensor terminal 30 is arranged in the inspection pedal 11a among each pedal 11 of escalator 10.This sensing
Device terminal 30 includes control portion 31, sensor portion 32, pedal position test section 33 and wireless communication part 34.
Control portion 31 carries out the control of sensor terminal 30.The operating about escalator 10 is measured in sensor portion 32
Data.In present embodiment, carry sound transducer as this sensor portion 32, the row of measurement escalator 10
Sail voice data.
Pedal position test section 33 is made up of inclination sensor or gyro sensor.This pedal position test section 33 with
On the basis of checking the position of pedal 11a, check a week of escalator 10.Wireless communication part 34 fills with data collection
Putting and carry out wireless near field communication between 40, the traveling audio data transmitting measured in sensor portion 32 is delivered to data and is received
Acquisition means 40.
Transacter 40 is arranged at the place of the regulation of escalator 10 (for lower mechanical room in the example of Fig. 1
13).This transacter 40 includes wireless communication part 41, data store 42 and transmits control portion 43.
Wireless communication part 41 carries out short-range communication between sensor terminal 30 and transacter 40, receive from
The traveling voice data that sensor terminal 30 is sent here.Data store 42 stores the traveling received by wireless communication part 41
Voice data.At this moment, such as there is multiple stage as the escalator 10 monitoring object, then add each escalator 10 and set
Fixed ID also stores traveling voice data.Transmit control portion 43 and read storage in data store 42 with the unit of regulation
Traveling voice data, and be sent to Long-Range Surveillance Unit 50.
Long-Range Surveillance Unit 50 is arranged in central monitoring position 60 remotely.This Long-Range Surveillance Unit 50 is through communication line
61 are connected to be located at the transacter 40 of escalator 10.This Long-Range Surveillance Unit 50 includes data transmitting and receiving portion
51, operation inputting part 52, data store 53, data processing division 54, display part 57 and reporting unit 58.
Data transmitting and receiving portion 51 carries out the transmitting-receiving letter of various data and processes.Operation inputting part 52 is made up of keyboard etc., logical
The operation crossing surveillant carries out input or the instruction of data.
As it is shown on figure 3, data store 53 stores the traveling of the escalator 10 obtained from transacter 40
Voice data, as travelling audio files F1.Also in this data store 53, the benchmark of storage com-parison and analysis
Voice data, as base sound file F2.The base sound data of this com-parison and analysis, in advance with escalator 10
Installation time or maintenance overhaul after measure travel and be made based on voice data.
Also, as the preservation form of base sound data, base sound can be pre-saved with the form of wav file
Wave data.Or, it is possible to pre-save and use FFT(fast Fourier transform) by after the analysis of base sound working frequency
Data.Hereinafter, for the sake of explanation simply, illustrate data store 53 preserves in advance the data after frequency analysis
Situation as base sound data.
Data processing division 54, is made up of microprocessor etc., the traveling voice data of analyzing and processing escalator 10.Should
Data processing division 54 includes memorizer 55 and state change-detection portion 56.
Memorizer 55 is the working storage temporarily stored.In this memorizer 55, storage is from data store 53
Traveling audio files F1 read travel voice data and from base sound file F2 read base sound data.
State change-detection portion 56 is the part of the state change of detection escalator 10.This state change-detection portion 56
It is made up of frequency analysis portion 56a, correlation function calculating section 56b and state change detection unit 56c.It practice, should
State change-detection portion 56 realizes with algorithm.
The frequency analysis portion 56a FFT traveling voice data to reading from memorizer 55 with each provision discussion unit
Carry out frequency analysis.Correlation function calculating section 56b calculates the measurement data after representing the 56a frequency analysis of frequency analysis portion
And the correlation coefficient ρ of the degree of correlation between benchmark data.State change detection unit 56c is according to correlation function calculating section
The correlation coefficient ρ that 56b calculates, it is determined that the state change of escalator 10.
Display part 57 is with the result of determination of the form display state change detection unit 56c of regulation.It addition, reporting unit 58
The local time in of existing to be checked among the result of determination of state change detection unit 56c, by display or these feelings of acoustic notifications
Condition.
In above composition, first, the surveillant in central monitoring position operates the operation inputting part of Long-Range Surveillance Unit 50
52, input periodically carries out the timetable travelling sound collecting of escalator 10, and is sent to transacter 40.
The timetable that transacter 40 is sent here according to Long-Range Surveillance Unit 50, starts sensor terminal 30 instruction
Collect the traveling voice data of escalator 10.So, sensor terminal 30 uses and is carried as sensor portion
Sound transducer measures the traveling voice data of escalator 10, and this measurement data (traveling voice data) is delivered to
Transacter 40.Specifically, sensor terminal 30 is by utilizing pedal position test section 33 to check pedal
On the basis of the position of 11a, check that escalator 10 has turned over the situation of a week, in units of week, detection data are delivered to
Transacter 40.
The traveling voice data received from sensor terminal 30 is stored in data store 42 by transacter 40.This
Time, it is considered to the input of the external sound of burst, at least collect the traveling voice data of degree of two weeks preferably.Data are received
Acquisition means 40 when have collected the traveling voice data of ormal weight from sensor terminal 30 according to above-mentioned timetable,
Just sensor terminal 30 notice is terminated to travel the collection of voice data.
On the other hand, Long-Range Surveillance Unit 50 is by connecting transacter 40, data collection with communication line 61
The traveling voice data of storage in the data store 42 of collection device 40.At this moment traveling voice data is as traveling
Audio files F1 is stored in data store 53, manages with the device id etc. of this escalator 10.
Here, Long-Range Surveillance Unit 50 stores the traveling sound of storage part 53 according to timetable set in advance from data
File F1 reads and travels voice data.It addition, Long-Range Surveillance Unit 50 reads corresponding to this from base sound file F2
Travel the base sound data of voice data, be stored in the storage of data processing division 54 together with above-mentioned traveling voice data
In device 55.Then, the traveling sound that Long-Range Surveillance Unit 50 will store in memorizer 55 with the unit of analysis of each regulation
Sound data and base sound data give state change-detection portion 56, the state change of detection escalator 10.
Detection the following detailed description of state change processes.
Fig. 4 illustrates the flow chart that the detection of the state change carried out by Long-Range Surveillance Unit 50 processes.
When generally operating, the traveling voice data of the escalator 10 that sensor terminal 30 is measured is through transacter
40 are returned.In the data store 53 of Long-Range Surveillance Unit 50, traveling voice data now is saved
As travelling audio files F1.It addition, by traveling voice data during escalator 10 initial after frequency analysis
The base sound data of com-parison and analysis be saved as base sound file F2.
First, surveillant is by the operation of regulation, it is intended that date, time or the equipment being analyzed.So, it is located at
Data processing division 54 in Long-Range Surveillance Unit 50, selects suitable traveling voice data from travelling audio files F1,
And be stored in the memorizer 55 of data processing division 54 (step A11).It addition, data processing division 54 is from base sound
File F2 selects the base sound data corresponding to above-mentioned traveling voice data, and is stored in (step in memorizer 55
A12).
Here, in the 1st embodiment, the unit of analysis of determination data (traveling voice data) is taken as predetermined list
Bit time t.The state change-detection portion 56 time per unit t being located in data processing division 54 extracts from memorizer 55
Travel voice data (step A13).
Above-mentioned unit interval t be by the one of escalator week desired time subdivision after time.Such as, if the t=10 second,
Then the traveling scope of every 10 seconds from reference position as analyzing object, is carried by the inspection pedal 11a of escalator 10
Take the traveling voice data measured therebetween.
The traveling voice data that this unit interval t is divided by state change-detection portion 56 delivers to frequency analysis portion 56a, carries out
Frequency analysis (step A14).State change-detection portion 56 by the traveling voice data after frequency analysis with correspond to
The base sound data of this traveling voice data deliver to correlation function calculating section 56b, obtain the relevant journey representing two data
The correlation coefficient ρ (step A15) of degree.
Here, as the method for detection state change, there is the covariance of the degree of the linear relationship represented between 2 variablees.
Illustrate that the computing formula of covariance is as follows.
Covariance
In formula,
: the meansigma methods of X variable
: the meansigma methods of Y variable
N: data amount check
But, because covariance is not normalized, it is difficult to directly with result of calculation evaluation.Therefore, native system uses energy
The correlation coefficient of simple evaluation result of calculation.Illustrate and seek Pearson product-moment correlation coefficient (Pearson product-moment
Correlation coefficient) formula as follows.
Pearson product-moment correlation coefficient
In formula,
SX: the standard deviation of X variable
SY: the standard deviation of Y variable
Furthermore, above-mentioned formula (1) is with formula (2), and i represents unit of analysis.
Correlation coefficient is to represent 2 variable X, the statistical index of relevant (homophylic degree) between Y.
Correlation coefficient is the real number value between-1～1, claims variable X when close to 1, and Y is positive correlation, such as close-1, then claims
Variable X, Y is negative correlation.During close to 0, variable X, Y's is relevant more weak.
In present embodiment, variable X is to travel voice data, and variable Y is base sound data.Also, i is equivalent to
Unit interval t.I other words, by the traveling voice data that divides of unit interval t extracted in above-mentioned steps A13 with correspond to
The base sound data of this traveling voice data substitute into the variable X of above-mentioned formula (2), in Y, obtain correlation coefficient ρ.
The value of the correlation coefficient ρ that state change detection unit 56c calculates according to correlation function calculating section 56b judges automatically to hold up
The changed condition of ladder 10.Further, in the present system because of negative correlation not constituent relation, therefore it is set between 0～1 judgement shape
Condition changes.
I.e., when ρ=1 (step A16 be), state change detection unit 56c judges that escalator 10 is as ill-mannered
State change (step A17).So-called " stateless change " refers to " not changing from initial start state ".I.e. anticipate
Taste sound without exception and is occurred, and escalator 10 normally operates.
On the other hand, when not being ρ=1 (step A16 no), state change detection unit 56c is judged to automatically hold up
Ladder changes (step A18) for there being state.So-called " having state to change " refers to " have some to change from initial start state
Become ".I.e. meaning have abnormal sound to occur, there is the probability not being normally operate in escalator 10.
When being judged to have state to change, state change detection unit 56c determines phase according to the time of this traveling voice data
The place of symbol, and recorded together with the value of correlation coefficient ρ in the regulation region of such as memorizer 55 (step A19).
If the process of one week degree is not over (step A20 no), then state change-detection portion 56 extracts next
The traveling voice data that unit interval t divides, and repeat above-mentioned same process.(walk when terminating the process of one week degree
Rapid A20 is), state change-detection portion 56 just with reference to the content recorded in the region of the regulation of above-mentioned memorizer 55,
And the result of determination that state changes detection unit 56c is shown in display part 57(step A21 with the form of regulation).
As the display packing of result of determination, there is each unit of analysis to determination data (traveling voice data) will just
Often (without changed condition)/abnormal (having changed condition) make what form showed together with the value of correlation coefficient ρ
Method etc..So, surveillant can hold the state in each place of escalator 10 by the picture of display part 57
Change.
Now, the value of correlation coefficient ρ is closer to 0, it is meant that abnormal probability is the highest.I.e., in token phase
Close the value of coefficient ρ to below arithmetic point when the 4th, such as, arrive " 0.9999 "～" 0.9955 ", the most normally,
Present situation can determine that into no problem.On the other hand, value becomes the lowest from " 0.9954 ", and abnormal probability is more
High.
Surveillant is when seeing that from the picture of display part 57 value of correlation coefficient ρ has less than threshold value ρ x(such as
" 0.5000 ") local time, be judged as needing checking.And, taked to send maintenance man before breaking down
To countermeasures such as scenes.
It addition, local time correlation coefficient ρ exists less than threshold value ρ x, data processing division 54 is by reporting unit 58
Report this situation.Also, above-mentioned threshold value ρ x was at random determined by the structure of escalator 10 and operation time etc..
Also, in the example of Fig. 4, judge changed condition with the traveling voice data of a week of escalator 10, but also may be used
Judge changed condition with the traveling voice data more than the degree of two weeks, and show its result.
So, by obtain on-site measurement travel between voice data and pre-prepd base sound data relevant
Coefficient ρ, can detect the state change of escalator 10 accurately according to the value of correlation coefficient ρ.I other words, energy
Value according to correlation coefficient ρ judges the appearance changed at leisure from original state of escalator 10.Thus, as followed the tracks of
The value of this correlation coefficient ρ, even if infrequently sending maintenance man to scene, the state that also can hold escalator 10 becomes
Change.And, in local time finding to be checked, maintenance man can be sent before breaking down to scene and to take suitably
Countermeasure.
Also, in above-mentioned 1st embodiment, be to utilize the sensor terminal 30 being arranged in inspection pedal 11a to examine
Survey the structure of a week of escalator 10 but it also may such as in Machine Room, top 12, lower mechanical room 13 or framework
Proximity sensor was set in 15, with one week of this proximity sensor detection escalator 10.
As proximity sensor, such as available light electric transducer or magnetic sensor etc..As with by these proximity sensors
Detection signal be directly inputted to the structure of transacter 40, then can eliminate the time lag of Wireless transceiver, accordingly, it is capable to
Alleviate synchronization process when simultaneously collecting sound in multiple places.
Also, in above-mentioned 1st embodiment, consist of, it is arranged at the sensor terminal 30 checked in pedal 11a,
By utilizing pedal position test section 33 on the basis of the position checking pedal 11a, detection escalator 10 turns over one
In week, in units of week, send measurement data to transacter 40.But, it is possible to it is configured to, such as, is measuring number
Additional one week detection signal according to, and the measurement data in each week it is transformed in transacter 40 side.
I other words, it is also possible to it not the composition each week sending measurement data, but utilize streaming to be transferred into row data and send out
The composition sent.Now, within additional one week in measurement data, detect signal, be transformed in transacter 40 side every
The measurement data of one week.So, the measurement data amount that sensor terminal 30 keeps can be reduced, reduce sensor terminal
Cost.
Also, sensor terminal 30 may be not necessarily provided in inspection pedal 11a.The most also top machine can be fixedly installed on
Tool room 12, in lower mechanical room 13 or framework 15 etc., measures the traveling sound of escalator 10 in its setting place
Data.
Also, in the case of analyzing using the traveling voice data of a week of escalator 10 as time per unit t,
Also overlap more or less before and after can making unit interval t and extract data.Such as, before and after making unit interval t
50% overlapping extracts data.So, then can prevent the loss of data before and after unit interval t, carry out correct dividing
Analysis.
(the 2nd embodiment)
Explanation the 2nd embodiment below.
In above-mentioned 1st embodiment, take the traveling voice data of degree of a week to escalator 10 every unit
The composition of time series analysis.In contrast, in the 2nd embodiment, it not the unit interval, but by escalator 10
The traveling voice data of one week degree be at least divided into direct acting portion and rotation section, and the traveling of each segmentation distinguished carry out
Analyze.
Fig. 5 illustrates, by one week of the escalator of the 2nd embodiment, is divided into direct acting portion and traveling during rotation section
The figure distinguished.Also, mark identical symbol to the part identical for Fig. 1 in above-mentioned 1st embodiment, and omit
The explanation repeated.To the Long-Range Surveillance Unit 50 shown in Fig. 1, central monitoring position 60 and communication line 61, it is illustrated that from
Slightly.
Direct acting portion and rotation section at least it is divided into by one week of escalator 10.In the example of Fig. 5, to be built in sensor
In terminal 30, inclination sensor as pedal position test section 33 detects the position of 90 degree in lower mechanical room 13
Put the reference position P as a week, be divided into direct acting portion and rotation section.Thus divide into A～E5 part.In figure
B Yu D part be direct acting portion, the part of A, C, E is rotation section.
In rotation section A, C, E, because there is sound when each pedal 11 rotates, therefore have with direct acting portion B, D not
Same traveling sound property.Thus, it is divided into direct acting portion B, D with rotation section A, C, E to carry out data analysis preferably.
Especially in the case of carrying out frequency analysis with FFT, because of the data number in addition equalization of the frequency resultant in principle, therefore
It is difficult to the state change of detection burst.Carry out data by the part close by traveling sound property (frequency characteristic) to divide
Analysis, can improve the accuracy of detection of state change.
The process action that following description is concrete.
Fig. 6 illustrates the flow chart that the detection of the state change practiced by Long-Range Surveillance Unit 50 processes.
When generally operating, reclaimed the escalator measured by sensor terminal 30 termly by transacter 40
The traveling voice data of 10.In the data store 53 of Long-Range Surveillance Unit 50, traveling voice data at this moment is made
Preserved for travelling audio files F1.And traveling voice data during escalator 10 initial is carried out frequency divide
The base sound data of the com-parison and analysis after analysis are preserved as base sound file F2.
First, surveillant utilizes the operation of regulation to specify date, time and the equipment being analyzed.It addition, determine extremely
The traveling voice data collected less two weeks is analyzed.When carrying out data analysis, surveillant utilizes the operation of regulation
Preassign analysis times.
So, the data processing division 54 being arranged in Long-Range Surveillance Unit 50 is suitable from travelling audio files F1 selection
Travel voice data, and be stored in the memorizer 55 of data processing division 54 (step B11).Also, data processing division
54 select the base sound data corresponding with above-mentioned traveling voice data from base sound file F2, and are stored in memorizer
In 55 (step B12).
Here, in the 2nd embodiment, the unit of analysis of determination data (traveling voice data) is distinguished as travelling.
Each predetermined traveling is distinguished from memorizer 55 extraction row by the state change-detection portion 56 being arranged at data processing division 54
Sail voice data (step B13).So-called traveling is distinguished, refer to direct acting portion B, the D shown in Fig. 5 and rotation section A,
C、E。
When generally operating, escalator 10 is with the speed revolution of regulation.Thus, can using reference position P as starting point,
Sensor terminal 30 is utilized to pass through according to the determined time travelling voice data, it is determined that rotation section A → direct acting portion B
The traveling of → rotation section C → direct acting portion D → rotation section E is distinguished.It is initially rotation section A.State change-detection portion 56
According to the time process of traveling voice data, just extract the row travelling differentiation corresponding to rotation section A from memorizer 55
Sail voice data.
The traveling voice data that this traveling is distinguished by state change-detection portion 56 is delivered to frequency analysis portion 56a and is carried out frequency and divide
Analysis (step B14).State change-detection portion 56 is by this traveling voice data after frequency analysis and corresponding to this row
The base sound data sailing voice data give correlation function calculating section 56b, obtain the phase representing two data degrees of correlation
Close coefficient ρ (step B15).
Specifically, the traveling voice data distinguished is travelled and corresponding to this traveling sound by what above-mentioned steps B13 was extracted
The base sound data of sound data substitute into variable X and the variable Y of the formula (2) illustrated in above-mentioned 1st embodiment
In, obtain correlation coefficient ρ.
The value of the correlation coefficient ρ that state change detection unit 56c calculates according to correlation function calculating section 56b, it is determined that automatically
The changed condition of staircase 10.Further, in the present system because of negative correlation not constituent relation, therefore it is set between 0～1 judgement
Changed condition.
I.e., when ρ=1 (step B16 be), state change detection unit 56c judges that escalator 10 is as ill-mannered
State change (step B17).So-called " stateless change " refers to " not changing from initial start state ".I.e. anticipate
Taste sound without exception and is occurred, and escalator 10 normally operates.
On the other hand, when not being ρ=1 (step B16 no), state change detection unit 56c judges escalator
For there being state to change (step B18).So-called " having state to change " refers to " having some to change from initial start state ".
I.e. meaning have abnormal sound to occur, there is the probability the most normally operated in escalator 10.
When being judged to have state to change, state change detection unit 56c distinguishes really according to the traveling of this traveling voice data
Surely the place met, and recorded (step in the regulation region of such as memorizer 55 together with the value of correlation coefficient ρ
B19).
If the process of a week is not over (step B20 no), then state change-detection portion 56 extracts next traveling
The traveling voice data distinguished, and repeat above-mentioned same process.If the next one of example rotation section A, just extract straight
What dynamic portion B was corresponding travels the running data distinguished, the process that repeat the above steps B13 rises.
When terminating the process of a week (step B20 be), data processing division 54 is judged as the analysis times of regulation
Process whether terminate (step B21).When the process of the analysis times of regulation is not over (step B21 no)
Data processing division 54 returns the process of above-mentioned steps B11, repeats above-mentioned same process.
When terminating the process of provision discussion number of times (step B21 be), state change-detection portion 56 is just with reference to upper
The content of record in the region of the regulation stating memorizer 55, and state is changed the result of determination of detection unit 56c with regulation
Form be shown in display part 57(step B22).
As the display packing of result of determination, there is each unit of analysis to determination data (traveling voice data) will just
Often (without changed condition)/abnormal (having changed condition) make what form showed together with the value of correlation coefficient ρ
Method etc..So, surveillant can hold state change everywhere by the picture of display part 57.
As illustrated in above-mentioned 1st embodiment, the value of correlation coefficient ρ is closer to 0, it is meant that abnormal
Probability the highest.Surveillant one is when seeing that from the picture of display part 57 value of correlation coefficient ρ is less than threshold value ρ x(example
Such as " 0.5000 ") local time, be judged as needing checking.And, taked to send maintenance before breaking down
Member is to countermeasures such as scenes.
It addition, local time correlation coefficient ρ has less than threshold value ρ x, state change-detection portion 56 is by reporting unit 58
Report this situation.Also, above-mentioned threshold value ρ x was at random determined by the structure of escalator 10 and operation time etc..
Such that make at least to be divided into one week of escalator 10 direct acting portion and rotation section, and to each segmentation after
Traveling differentiation is analyzed, and also can detect the state change of escalator 10 accurately.
And, by being divided into direct acting portion and rotation section, the part that sound property (frequency characteristic) is close can travelled
Carry out data analysis.Thus, the distinctive noise in such as rotation section can be avoided direct acting portion is travelled what sonic measurements brought
Impact, improves the accuracy of detection of state change.
Also, in above-mentioned 2nd embodiment, with being arranged at the sensor terminal 30 checking pedal 11a, in units of week
Measure the traveling voice data of escalator 10.But, it is possible to the top Machine Room 12 corresponding in such as rotation section,
Each of the framework 15 that lower mechanical room 13 is corresponding with direct acting portion is local, is fixedly installed sensor terminal 30, uses these
Sensor terminal 30 measures traveling voice data respectively.
So, then the time travelling voice data according to one week that need not is through judging that each traveling is distinguished and extract data
Such process.At this moment, if using check pedal 11a by analyzing by sensor terminal 30 everywhere as benchmark
The traveling voice data measured, then determine driving chain and other pedals 11 according to the position relationship checking pedal 11a
State change just become easy.
Although at least collect the traveling voice data of two weeks degree also, be set to and be analyzed, but in state change-detection
When Shi Shihang majority rule judges, it is desirable to the traveling voice data collecting more than the degree of three weeks is analyzed.
Again, it is also possible to the traveling voice data of days past is analyzed, decision state change is carried out according to its analysis result.
So, the effect of noise suddenly occurred can be got rid of.
And, as shown in Figure 7, it is possible in state change-detection portion 56, abnormal data extraction unit 56d is set,
Before obtaining the correlation coefficient ρ travelled between voice data and base sound data, extract from travelling voice data
Abnormal data.The most so-called " abnormal data ", refers to the data of the ambient sound of the noise etc. of surrounding.Work as traveling
Time in voice data containing this abnormal data, just the accuracy of detection of state change is produced impact.Therefore, carry
Take and seek correlation coefficient ρ again after travelling the abnormal data contained in voice data.
The extraction of abnormal data, by deducting the frequency content of base sound from the frequency content travelling sound, determine with
The different composition of common traveling sound is abnormal sound.But, when all proposing abnormal data, cannot be from logical
Normal traveling sound detects state change, therefore, extracted amount to a certain degree will be suppressed.
So, as extract travel in voice data the abnormal data contained after seek correlation coefficient ρ again if, then can be
The state change of escalator 10 is detected accurately under the state doing one's utmost to eliminate the ambient sound of ambient noise etc..
According at least one above-mentioned embodiment, then it is provided that and can change by the state of detection passenger conveyors and make
Reflecting the burden alleviating maintenance man in maintenance activity, the passenger simultaneously suitably taken some countermeasures before fault occurs is defeated
Send the abnormity diagnostic system of machine.
Also, in the respective embodiments described above, as a example by escalator, passenger conveyors is described, but also is able to fit
In walking footpath etc..
Also, be not limited to travel sound, such as, it is possible with the vibration occurred when acceleration transducer etc. measures motion, root
State change is detected as described above according to its data recorded.
In a word, though understanding several embodiments of the invention, but these embodiments propose as an example, and
It is not intended to limit the scope of invention.The embodiment implementing these new rule with other various forms is possible,
Without departing from the scope of the spirit of the present invention, various omission can be carried out, replace, change.These embodiments and
Deformation, be included in the scope or spirit of invention, be simultaneously contained in the invention described in the scope of claim and etc.
In the scope of effect.
Claims (7)
1. the abnormity diagnostic system of a passenger conveyors, it is characterised in that including:
It is arranged at the sensor terminal that can carry out radio communication at the regulation of passenger conveyors；
Collect the transacter of the data that this sensor terminal is measured；And
It is connected on this transacter through communication line, and obtains described sensor eventually from described transacter
The Long-Range Surveillance Unit of the measurement data of end,
Described Long-Range Surveillance Unit possesses:
The storage part of the described measurement data that storage obtains from described transacter；
Read the go forward side by side frequency of line frequency analysis of described measurement data for the unit of analysis of each regulation from this storage part to divide
Analysis portion；
Illustrate that described passenger carries according to by the described measurement data after this frequency analysis portion frequency analysis with set in advance
The benchmark data of the original state of machine, calculates the correlation coefficient calculating section of correlation coefficient；
The correlation coefficient calculated according to this correlation coefficient calculating section judges the shape from original state of described passenger conveyors
The state change detection unit of state change；And
Show the display part of the result of determination of this state change detection unit.
2. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterised in that
Described frequency analysis portion reads described measurement data line frequency of going forward side by side for the per unit time from described storage part and divides
Analysis.
3. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterised in that
Described frequency analysis portion was at least divided into direct acting portion and rotation section, for each by one week of described passenger conveyors
Distinguish corresponding to the traveling of described direct acting portion and described rotation section and read described measurement data from described storage part and go forward side by side line frequency
Rate is analyzed.
4. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterised in that
Possess the abnormal data from being extracted abnormal data by the described measurement data after described frequency analysis portion frequency analysis to carry
Take portion,
Described correlation coefficient calculating section extracts the described measurement number after described abnormal data with by described abnormal data extraction unit
According to, calculate correlation coefficient.
5. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterised in that
Described display part, according to the result of determination of described state change detection unit, shows different for each described measurement data
Normal presence or absence and correlation coefficient.
6. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterised in that
Farther include: when described state change detection unit is judged to the state change needing to check, notification is by above-mentioned
The reporting unit of the situation needing to check that state change detection unit judges.
7. the abnormity diagnostic system of passenger conveyors as claimed in claim 1, it is characterised in that
Described sensor terminal measures the traveling voice data of a week of described passenger conveyors.
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| JP2013-109036 | 2013-05-23 | ||
| JP2013109036A JP5709327B2 (en) | 2013-05-23 | 2013-05-23 | Man conveyor abnormality diagnosis system |
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| CN104176613A CN104176613A (en) | 2014-12-03 |
| CN104176613B true CN104176613B (en) | 2016-11-23 |
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| CN201310303888.1A Active CN104176613B (en) | 2013-05-23 | 2013-07-18 | The abnormity diagnostic system of passenger conveyors |
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| JP6429195B2 (en) * | 2015-04-23 | 2018-11-28 | 三菱電機株式会社 | Abnormality diagnosis apparatus for passenger conveyor and abnormality diagnosis method for passenger conveyor |
| JP6782915B2 (en) * | 2016-08-09 | 2020-11-11 | 清水建設株式会社 | Belt conveyor abnormality detection device and abnormality detection method |
| EP3287410B1 (en) | 2016-08-24 | 2020-02-26 | Otis Elevator Company | Passenger conveyor and method for monitoring vibrations in a passenger conveyor |
| JP6781612B2 (en) * | 2016-12-02 | 2020-11-04 | 大成建設株式会社 | Deterioration diagnosis method for shield machines |
| JP2020527816A (en) * | 2017-07-13 | 2020-09-10 | アナンド デシュパンデＡｎａｎｄ Ｄｅｓｈｐａｎｄｅ | Devices for voice-based monitoring of machine operations and how to operate them |
| JP6453424B1 (en) * | 2017-11-14 | 2019-01-16 | 東芝エレベータ株式会社 | Passenger conveyor |
| US11225399B2 (en) | 2017-12-29 | 2022-01-18 | Kone Corporation | Escalator monitoring system, method, sound data collection device and fixture therefor |
| JP6825072B1 (en) * | 2019-12-09 | 2021-02-03 | 東芝エレベータ株式会社 | Passenger conveyor anomaly detection system |
| CN111606177B (en) * | 2020-06-04 | 2022-04-12 | 上海三菱电梯有限公司 | Passenger conveying device and fault detection monitoring method and device thereof |
| CN111606176B (en) * | 2020-06-04 | 2022-10-14 | 上海三菱电梯有限公司 | Passenger conveyor, abnormality diagnosis device and method thereof, and cycle recognition method |
| JP7095167B1 (en) | 2021-07-19 | 2022-07-04 | 三菱電機株式会社 | Diagnostic device for passenger conveyors |
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| JP5344797B2 (en) * | 2007-03-13 | 2013-11-20 | 東芝エレベータ株式会社 | Remote monitoring system with variable function of escalator |
| JP4761276B2 (en) * | 2008-07-10 | 2011-08-31 | 東芝エレベータ株式会社 | Abnormality diagnosis system for passenger conveyor |
| JP4662382B2 (en) * | 2008-07-14 | 2011-03-30 | 東芝エレベータ株式会社 | Abnormality diagnosis system for passenger conveyor |
| JP4577794B2 (en) * | 2008-07-29 | 2010-11-10 | 東芝エレベータ株式会社 | Abnormality diagnosis system for passenger conveyor |
| JP4829335B2 (en) * | 2009-11-04 | 2011-12-07 | 株式会社東芝 | Diagnostic device for conveyor and diagnostic system therefor |
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| JP2014227272A (en) | 2014-12-08 |
| JP5709327B2 (en) | 2015-04-30 |
| CN104176613A (en) | 2014-12-03 |
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