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WO2020204821A1 - Système et procédé de détection d'anomalies dans des objets se déplaçant le long d'un rail - Google Patents

Système et procédé de détection d'anomalies dans des objets se déplaçant le long d'un rail Download PDF

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Publication number
WO2020204821A1
WO2020204821A1 PCT/SG2020/050176 SG2020050176W WO2020204821A1 WO 2020204821 A1 WO2020204821 A1 WO 2020204821A1 SG 2020050176 W SG2020050176 W SG 2020050176W WO 2020204821 A1 WO2020204821 A1 WO 2020204821A1
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WO
WIPO (PCT)
Prior art keywords
signals
daq
sensor
baseline
objects
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/SG2020/050176
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English (en)
Inventor
Juan Juan Dora HU
Yongwei Zhu
Bo DONG
Jian Zhong Emily HAO
Yixin Wang
Rebecca Yen-Ni WONG
Weng Kiong Peter LAU
Hwee Woon Ong
Jayachandran Maniyeri
Chuen Yuen WONG
Jiliang Eugene PHUA
Hailiang Zhang
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Smrt Corp Ltd
Agency for Science Technology and Research Singapore
Original Assignee
Smrt Corp Ltd
Agency for Science Technology and Research Singapore
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Smrt Corp Ltd, Agency for Science Technology and Research Singapore filed Critical Smrt Corp Ltd
Priority to SG11202110771PA priority Critical patent/SG11202110771PA/en
Publication of WO2020204821A1 publication Critical patent/WO2020204821A1/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66B—ELEVATORS; ESCALATORS OR MOVING WALKWAYS
    • B66B25/00—Control of escalators or moving walkways
    • B66B25/006—Monitoring for maintenance or repair
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y40/00—IoT characterised by the purpose of the information processing
    • G16Y40/10—Detection; Monitoring

Definitions

  • This invention relates to a system and method for detecting abnormalities in objects traveling along a track.
  • the system and method is configured to detect any physical abnormalities in objects as the objects roll, revolve, slide and/or move along the track.
  • Movable steps and/or stairs such as escalators or moving walkways, and such similar systems provide a way to quickly and conveniently transport people from one place to another.
  • Such conveyors are ubiquitous transportation systems for passenger circulations and they are widely used in commercial buildings, airports, railway stations, and street to overhead bridges or underground tunnels etc. and may include vertical and/or horizontal conveyors.
  • Step rollers and/or step chain rollers that are located underneath such conveyor systems interconnect each of the steps in a continuous manner. Driven by a main drive shaft and their associated gears, the step rollers and step chain rollers move the steps along a predetermined path to transport passengers between locations. By controlling the direction of the step chains, the direction of the conveyors may then be controlled accordingly.
  • conveyors are prone to various internal failures, which may cause injury to passengers on or near the conveyor. It is of importance that such conveyor systems are properly maintained as it impacts millions of passengers daily, especially in large cosmopolitan cities. Such conveyor- related accidents can lead to injuries from mild sprains to fatal consequences. These accidents may be due to incorrect usage of the conveyor system by passengers, or due to inherent failures.
  • the first cause can be addressed by educating passengers on the correct use of such conveyor system.
  • the second cause i.e. escalator failures, which is related to faulty conveyor system’s parts such as steps, rollers, belt, balustrade, comb plate, brake and handrails are harder to detect as these parts are normally hidden within the system. Faults in these parts can lead to escalator failures such as a sudden stoppage, entrapments and broken or missing steps.
  • step rollers and/or step chain rollers One of the main culprits for malfunctioning conveyors are de-bonded and/or cracked step rollers and/or step chain rollers. Although most conveyor systems typically schedule maintenance of the system’s step rollers, such scheduled maintenance are unable to sufficiently and effectively ensure that the rollers of all conveyors are 100% fit for operation all the time. While there are several existing systems which provide such safety control measures for conveyors that aim to accurately detect such faults, they have their drawbacks.
  • photoelectric sensors which use light or the interruption thereof to monitor the rollers of a conveyor.
  • a photoelectric beam of light is aligned to shine at the rollers as they pass by a point on the track. If a roller is de-bonded or cracked, the beam of light reflected will be altered and the control system then responds to the error.
  • a disadvantage of such a scheme is that such photoelectric sensors have to be extremely sensitive to be able to detect minor cracks in rollers, and such cracks would have to be detected early on before catastrophic failure of the system occurs.
  • photoelectric sensors are susceptible to dust, debris, or anything else that may be present or that may collect in the system over time thereby interrupting the light paths.
  • a first advantage of embodiments of systems and methods in accordance with the invention is that the system is able to continuously detect abnormalities in objects that are traveling along a track such as the step rollers in conveyor systems.
  • a second advantage of embodiments of systems and methods in accordance with the invention is that the system is able to utilize either an accelerometer or a Fiber-Bragg-Grating sensor to continuously detect abnormalities in objects that are traveling along a track such as the step rollers in conveyor systems.
  • a monitoring system for detecting abnormalities in objects traveling along a track comprising a sensor provided adjacent the track, the sensor configured to detect interactions between the objects and the track; a data acquisition module (DAQ) coupled to the sensor, the DAQ configured to: receive signals generated by the sensor; normalize the received signals with baseline signals; and classify the objects as abnormal when periodic spikes are detected in the normalized signals.
  • DAQ data acquisition module
  • the interactions between the objects and the track comprise vibrations generated when the objects interact with the track.
  • the classifying of the objects as abnormal by the DAQ comprises the DAQ being configured to: generate a threshold value based on the normalized signals, whereby periodic spikes that are less than the threshold value are removed from the DAQ.
  • the baseline signals comprise a baseline matrix R that is constructed by concatenating a baseline signal having a time-period P for N number of times.
  • the DAQ before the DAQ normalizes the received signals with the baseline matrix R, the DAQ is configured to: divide the received signals into N number of blocks whereby each block comprises the time-period P, and whereby incomplete blocks are assigned values based on average values of the baseline matrix; and generate a signal matrix S by concatenating the divided received signals for the N number of times.
  • the normalizing the received signals with baseline signals comprises the DAQ being configured to: compute the energy difference between the signal matrix S and the baseline matrix R.
  • the generating of the threshold value based on the normalized signals comprises the DAQ being configured to: integrate the normalized signal over a time window t wherein the threshold value is set based on the integral result, and wherein the time window t comprises at least two of the time-periods P.
  • the normalizing of the received signals with the baseline signals comprises the DAQ being configured to: divide the received signals into N number of blocks whereby each block comprises a time-period P; perform a Fast-Fourier T ransform (FFT) analysis on each of the blocks of the received signals to convert the received signals in each block from a time-domain signal into a frequency-domain signal.
  • the sensor comprises an accelerometer and the DAQ comprises an accelerometer controller.
  • the senor comprises a Fiber-Bragg-Grating (FBG) sensor and the DAQ comprises an FBG interrogator.
  • FBG Fiber-Bragg-Grating
  • the senor comprises a vibration or strain sensor, a proximity sensor and a module configured to acquire a windowed signal for individual rollers based on signals received from the vibration or strain sensor and the proximity sensor.
  • a method for detecting abnormalities in objects traveling along a track using a monitoring system comprising a sensor provided adjacent the track and a data acquisition module (DAQ) coupled to the sensor
  • the method comprising: detecting, using the sensor, interactions between the objects and the track; receiving, using the DAQ, signals generated by the sensor; normalizing, using the DAQ, the received signals with baseline signals; and classifying, using the DAQ, the objects as abnormal when periodic spikes are detected in the normalized signals.
  • DAQ data acquisition module
  • the interactions between the objects and the track comprise vibrations generated when the objects interact with the track.
  • the step of classifying of the objects as abnormal by the DAQ comprises the steps of: generating a threshold value based on the normalized signals, whereby periodic spikes that are less than the threshold value are removed from the DAQ.
  • the baseline signals comprise a baseline matrix R that is constructed by concatenating a baseline signal having a time-period P for N number of times.
  • the method comprises the step of: dividing, using the DAQ, the received signals into N number of blocks whereby each block comprises the time-period P, and whereby incomplete blocks are assigned values based on average values of the baseline matrix; and generating, using the DAQ, a signal matrix S by concatenating the divided received signals for the N number of times.
  • the step of normalizing the received signals with baseline signals comprises the step of: computing the energy difference between the signal matrix S and the baseline matrix R.
  • the step of generating the threshold value based on the normalized signals comprises the step of: integrating, using the DAQ, the normalized signal over a time window t wherein the threshold value is set based on the integral result, and wherein the time window t comprises at least two of the time-periods P.
  • the step of normalizing the received signals with the baseline signals comprises the step of: dividing, using the DAQ, the received signals into N number of blocks whereby each block comprises a time-period P; performing, using the DAQ, a Fast-Fourier Transform (FFT) analysis on each of the blocks of the received signals to convert the received signals in each block from a time-domain signal into a frequency-domain signal.
  • FFT Fast-Fourier Transform
  • the senor comprises a strain sensor, a proximity sensor and an acquiring module, the method comprising the step of: acquiring, using the acquiring module, a windowed signal for individual rollers based on signals received from the strain and proximity sensors.
  • FIG. 1 illustrating a block diagram of modules in the monitoring system in accordance with embodiments of the invention
  • FIG. 2 illustrating a flowchart setting out the process for monitoring abnormalities in objects traveling along a track in a conveyor system in accordance with embodiments of the invention
  • Figure 3 illustrating plots of a detected signals in accordance with embodiments of the invention including the plot when a cracked object in a conveyor system such as a cracked roller is detected
  • Figure 4 illustrating plots of a detected signals in accordance with embodiments of the invention including the plot when an abnormal object in a conveyor system such as a de- bonded roller is detected
  • FIG. 5 illustrating plots of a detected signals in accordance with embodiments of the invention when a conveyor system is under normal operating conditions
  • FIG. 6 illustrating plots of a detected signals in accordance with embodiments of the invention when a conveyor system is under abnormal loads
  • Figure 7 illustrating plots of a detected signals in accordance with embodiments of the invention including the plot when a de-bonded object in a conveyor system such as a de- bonded roller is detected;
  • Figure 8 illustrating the detection of an object and the capturing of signals based on the interaction of the object and a track in accordance with embodiments of the invention
  • Figure 9 illustrating the acquisition of signals to define a time window to capture signals based on the interaction of the object and a track
  • FIG. 10 illustrating a process for determining baseline signals and detecting abnormalities in accordance with embodiments of the invention.
  • This invention relates to a system and method for detecting abnormalities in objects traveling along a track.
  • the system and method is configured to detect any physical abnormalities in objects as the objects roll, revolve, slide and/or move along the track.
  • the objects may comprise step rollers or rollers that are configured to revolve or move along a track in a conveyor system. Flowever, one skilled in the art will recognize that the objects are not limited to such rollers only and may include any other types of objects that are configured to travel along a fixed track or rail.
  • Step 1 detect, using a sensor provided adjacent a track, interactions between objects and a track;
  • Step 2 receiving, using a data acquisition module (DAQ) coupled to the sensor, signals generator by the sensor;
  • DAQ data acquisition module
  • Step 3 normalizing, using the DAQ, the received signals with baseline signals
  • Step 4 classifying, using the DAQ, the objects as abnormal when periodic spikes are detected in the normalized signals and optionally, classifying the objects as normal when periodic spikes are not detected in the normalized signals.
  • the steps set out above may be performed by the modules and components illustrated in Figure 1 .
  • the monitoring system comprises objects 1 10 with abnormal object 125 that are configured to travel along tracks 1 12a and/or 1 12b, a sensor 1 15 connected to a data acquisition module (DAQ) 105 through line 120 whereby sensor 1 15 is configured to detect interactions between objects 1 10 and tracks 1 12a and/or 1 12b.
  • DAQ data acquisition module
  • the interactions between objects 1 10 and tracks 1 12a and/or 1 12b may comprise objects 1 10 rolling along, sliding along, revolving along, or any other similar motion, along these tracks.
  • vibrations, sounds, and/or heat may be generated by objects 1 10 and/or tracks 1 12a/b and depending on the type of interaction that is to be detected, DAQ 105 and sensor 1 15 may be selected accordingly.
  • Process 200 begins at step 205 with process 200 calibrating a sensor system connected to DAQ 105.
  • the sensor system comprises a sensor that is provided adjacent objects 1 10 and tracks 1 12a/b.
  • process 200 will generate baseline signals based on the interactions between the objects 1 10 and tracks 1 12a b whereby these baseline signals are signals that represent the normal operation of objects 1 10 with tracks 1 12a/b.
  • process 200 proceeds to step 210 whereby it continuously acquires data from the sensor system.
  • process 200 determines at step 220 that periodic abnormalities exist in the normalized signals, process 200 then proceeds to step 230 whereby it determines that one or more of objects 1 10 is faulty. Conversely, if process 200 determines that period abnormalities do not exist in the normalized signal at step 220, process 200 then proceeds to step 225 whereby it determines that all the objects in object 1 10 are normal. Process 200 then ends.
  • Embodiment 1 A monitoring system comprising a DAQ, a Fibre-Bragg-Grating (FBG) interrogator and a FBG sensor.
  • FBG Fibre-Bragg-Grating
  • the DAQ comprises a FBG interrogator such as, but not limited to, the Micron Optics SM130 whereby its sampling frequency is set as 1 kHz while the FBG sensor(s) comprises the Technica S.A. Acrylate Fibre FBG.
  • the FBG sensor(s) may be mounted via, epoxy mounting or wax mounting to one or more of the tracks as required. Further, the FBG sensors are connected to the FBR interrogator through optical fibres.
  • objects 1 10 are taken to be the step rollers in a conveyor system while tracks 1 12a/b comprise the rails of the system.
  • the conveyor system may comprise a travellator, an escalator or any form of movable horizontal/diagonal/vertical walkway.
  • vibrations may be detected as changing wavelengths by the FBG sensors.
  • a baseline measurement is conducted for a conveyor system that is operating normally. This baseline measurement which was obtained using the FBG sensor and the FBG interrogator is then used as a reference/baseline signal for the conveyor system.
  • the received signals may not be directly compared to the baseline matrix R.
  • the received signal is initially divided into N blocks, i.e. Si , S2,..., SN where each block corresponds to a signal having a time period P. Due to this division, there is the likelihood that the first block and the final block would be incomplete, as these blocks would be less than one period P in length. Hence, these incomplete blocks will be assigned values based on the average values of the baseline matrix.
  • the blocks Si , S2,..., SN are then concatenated to form a signal matrix S.
  • Such a received signal is illustrated as plot 304 in Figure 3.
  • the signal matrix S is then normalized with the baseline matrix R by computing the energy difference between these two signals.
  • the energy difference is obtained by the following equation:
  • the normalized signal in order to correct the misalignment of signals in the normalization process, is integrated over a time window t and the resulting threshold value l t h is used to identify signal spikes in the normalized signal whereby normalized signal spikes that are equal to or above the threshold value, l t h, are considered as strong signal spikes and logged as a new data set for further processing. Otherwise, when the normalized signal spikes are less than the threshold value, l t h these signals are considered as weak signals and will not be logged for further processing.
  • Such a threshold value l t h is plotted as line 315 in Figure 3. The logged data set will then be checked for periodicity relative to a complete period of rotation of the conveyor system.
  • the logged data set comprises periodically recurring signals (such as spikes 310) relative to the conveyor period, this implies that a defective roller is present.
  • the threshold value may be used to set the minimum value of the recurring spikes that is to be logged.
  • plots in Figure 4 illustrate the plots that are used to determine whether the rollers in the conveyor system comprise de-bonded rollers.
  • plot 402 represents the baseline plot
  • plot 404 represents the plot of the received signal
  • plot 406 represents the normalized received signal.
  • plot 406 shows a number of periodically occurring signal peaks 410, this implies that the received signals associated with the rollers in the conveyor system comprise de-bonded rollers.
  • the plots in Figure 5 illustrate the plots that are obtained when a normal roller is used in the system.
  • plot 502 represents the baseline plot
  • plot 504 represents the plot of the received signal
  • plot 506 represents the normalized received signal.
  • plot 506 only shows a single signal peak 510, i.e. there are no periodically occurring signal peaks, this implies that the received signal associated with the rollers in the conveyor system comprise normal rollers.
  • non-periodically occurring signal peaks could be caused by factors such as passengers or objects that are loading the conveyor system, or noise from neighbouring conveyor systems.
  • the plots in Figure 6 illustrate such a scenario whereby plot 602 represents the baseline plot, plot 604 represents the plot of the received signal and plot 606 represents the normalized received signal.
  • plot 606 shows two randomly occurring groups of signal peaks 610a and 610b, this implies that the received signal associated with the rollers in the conveyor system comprise normal rollers and the variation in the normalized received signal is due to the loading of the conveyor system or noise from other external sources that were detected by the FBG sensors.
  • Embodiment 2 A monitoring system comprising a DAQ, an accelerometer controller and an accelerometer.
  • the DAQ comprises an accelerometer controller and accelerometer(s) that may be mounted via, epoxy mounting or wax mounting to the bottom of one or more of the tracks as required.
  • the accelerometer may comprise any device that is able to measure acceleration and may include, and is not limited to, a microelectromechanical system (MEMS) accelerometer.
  • MEMS microelectromechanical system
  • the accelerometers may be connected to the accelerometer controller through electrical cables or any other type of cables that may be configured to transfer data from the accelerometers to the controller.
  • objects 1 10 are similarly taken to be the step rollers in a conveyor system while tracks 1 12a/b comprise the rails of the system.
  • the conveyor system may comprise a travellator, an escalator or any form of movable horizontal/diagonal/vertical walkway.
  • vibrations may be detected as data logged in the time domain by the MEMS sensors.
  • a baseline measurement is conducted for a conveyor system that is operating normally. This baseline measurement which was obtained using the accelerometer and the accelerometer controller is then used as a reference/baseline signal for the conveyor system to compute the threshold values for the system.
  • a Fast-Fourier-Transform (FFT) is then applied to the received signals plotted in plot 710 and the resulting frequency domain signals are plotted over a time domain as plot 715.
  • the frequency domain signals are then analysed for periodic spikes and as it was found that periodic spikes 720 are present in plot 715, this implies that the detected roller contains defects.
  • Figure 8 illustrates an expanded view of rollers 805 as configured in both embodiments of the monitoring system.
  • Figure 8 shows that when a roller 810 of rollers 805 passes sensor 815 (which may be an accelerometer or FBG sensor), the vibrations generated between roller 810 and track 813 may be captured as signals by sensor 815.
  • sensor 815 which may be an accelerometer or FBG sensor
  • Figure 9 illustrates the signal acquisition steps performed by the monitoring system in accordance with embodiments of the invention by which the strain/vibration signal for each roller is cut into windows based on the timing of the roller passing by the sensor.
  • the roller track is set up to sense signals from the track.
  • a strain/vibration sensor picks up the continuous strain/vibration signal and this signal is stored at step 930.
  • the strain sensor may comprise a strain gauge or any other type of deice that may be used to measure strain on an object.
  • a proximity sensing point provided with a proximity sensor is configured to detect a proximity signal at step 905 and this signal is used to indicate the timing of the movement of the conveyor’s steps.
  • the proximity signal is stored at step 910 and at step 915, the timing of each roller passing by the track’s sensing point is stored so that it can be derived at step 920, which may or may not take the average step running speed into account.
  • the derived timing of each roller as obtained from step 920 is then provided to step 925 which uses this information to cut the continuous vibration signal obtained at step 930 into fixed time windows for each specific roller and this happens at step 925.
  • FIG. 10 illustrates the process 1005 of conducting roller condition data calibration and the process 1010 of roller condition monitoring as performed by the monitoring system in accordance with embodiments of the invention.
  • Process 1005 is conducted when the rollers are in normal condition, e.g. after replacing new rollers, and only need to be done once.
  • process 1010 this process is conducted continuously when the conveyor is in actual operation and being monitored.
  • step 1010 uses an acquiring module to acquire the signal whereby the signal (strain/vibration) is obtained in a fixed time window for each individual roller S R N at step 1015, which consists of R x N windowed signals where R is the total number of rollers in one loop.
  • each roller r (1 £ r £ R) comprises N acquired signals S r n , ((1 £ n £ N).
  • the features are extracted from the signals at step 1020 which performs a time frequency decomposition at this step.
  • the features obtained from step 1020 are stored as R x N features F R N .
  • a time frequency decomposition method that may be utilized involves the calculation of the short time interval Fourier transformation, which produces a spectrogram of the windowed signal.
  • the N features stored at step 1025 for each roller is then used to estimate a model G R for the normal condition of that roller and this takes place at step 1030.
  • the model may comprise a statistical normalized distribution model Q(m,s 2 ), where m and o 2 are the mean and variance of the N values.
  • the conveyor runs at a same direction and speed as in the calibration process 1005, such that the signals acquired from the two processes are comparable.
  • the number of loops K may or may not be the same as N, as in the calibration process.
  • Process 1010 begins at step 1055 by acquiring the signal using an acquiring module.
  • each roller r (1 £ r £ R) comprises K acquired signals S R K ((1 £ k £ K).
  • the features are extracted from the signals at step 1045 which performs a time frequency decomposition at this step.
  • a time frequency decomposition method that may be utilized involves the calculation of the short time interval Fourier transformation, which produces a spectrogram of the windowed signal.
  • the features obtained from step 1045 are stored as R x K features F R K .
  • the K feature vectors F R K are compared to the statistical model G R for each roller, which produces a distance D r (1 £ r £ R).
  • the distance computation relies on the mean and variance values.
  • the detection of faulty rollers may be performed at step 1065.
  • process 1010 is able to predict roller conditions at step 1075 and this is done based on the rate of degradation if sufficient amount of experiment data are available.

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Abstract

L'invention concerne un système et un procédé de détection d'anomalies dans des objets se déplaçant le long d'un rail. En particulier, le système et le procédé sont conçus pour détecter de quelconques anomalies physiques dans des objets lorsque les objets roulent, tournent, coulissent et/ou se déplacent le long du rail.
PCT/SG2020/050176 2019-03-29 2020-03-27 Système et procédé de détection d'anomalies dans des objets se déplaçant le long d'un rail Ceased WO2020204821A1 (fr)

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SG11202110771PA SG11202110771PA (en) 2019-03-29 2020-03-27 System and method for detecting abnormalities in objects traveling along a track

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CN116424768B (zh) * 2023-04-18 2024-01-23 四川省自贡运输机械集团股份有限公司 基于数字孪生技术的轮轨带式输送机集中监测方法及系统

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