EP3663160A1 - Anomalitätsdetektionsvorrichtung, anomalitätsdetektionssystem und anomalitätsdetektionsverfahren - Google Patents

Anomalitätsdetektionsvorrichtung, anomalitätsdetektionssystem und anomalitätsdetektionsverfahren Download PDF

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Publication number
EP3663160A1
EP3663160A1 EP17920338.5A EP17920338A EP3663160A1 EP 3663160 A1 EP3663160 A1 EP 3663160A1 EP 17920338 A EP17920338 A EP 17920338A EP 3663160 A1 EP3663160 A1 EP 3663160A1
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EP
European Patent Office
Prior art keywords
abnormality
track
drive system
abnormality detection
detection device
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Pending
Application number
EP17920338.5A
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English (en)
French (fr)
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EP3663160A4 (de
Inventor
Satoshi Sumita
Akeshi Takahashi
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Hitachi Ltd
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Hitachi Ltd
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Publication of EP3663160A1 publication Critical patent/EP3663160A1/de
Publication of EP3663160A4 publication Critical patent/EP3663160A4/de
Pending legal-status Critical Current

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
    • B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
    • B61L23/042—Track changes detection
    • B61L23/044—Broken rails
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61K—AUXILIARY EQUIPMENT SPECIALLY ADAPTED FOR RAILWAYS, NOT OTHERWISE PROVIDED FOR
    • B61K9/00—Railway vehicle profile gauges; Detecting or indicating overheating of components; Apparatus on locomotives or cars to indicate bad track sections; General design of track recording vehicles
    • B61K9/08—Measuring installations for surveying permanent way
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L15/00—Indicators provided on the vehicle or train for signalling purposes
    • B61L15/0018—Communication with or on the vehicle or train
    • B61L15/0027—Radio-based, e.g. using GSM-R
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L15/00—Indicators provided on the vehicle or train for signalling purposes
    • B61L15/0081—On-board diagnosis or maintenance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L23/00—Control, warning or like safety means along the route or between vehicles or trains
    • B61L23/04—Control, warning or like safety means along the route or between vehicles or trains for monitoring the mechanical state of the route
    • B61L23/042—Track changes detection
    • B61L23/045—Rail wear
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
    • B61L27/53—Trackside diagnosis or maintenance, e.g. software upgrades for trackside elements or systems, e.g. trackside supervision of trackside control system conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L27/00—Central railway traffic control systems; Trackside control; Communication systems specially adapted therefor
    • B61L27/50—Trackside diagnosis or maintenance, e.g. software upgrades
    • B61L27/57—Trackside diagnosis or maintenance, e.g. software upgrades for vehicles or trains, e.g. trackside supervision of train conditions

Definitions

  • the present invention relates to an abnormality detection device, an abnormality detection system, and an abnormality detection method, and for example, is preferably applied to a rail vehicle that runs on a track.
  • a rail vehicle runs on a track of less friction, and thus requires less energy necessary for transportation to be one of social infrastructures.
  • an abnormality on a drive system or the track is caused, it affects other rail vehicles that runs on the same track, so that the abnormality is required to be detected without being missed.
  • the abnormality detection operation that uses a dedicated device is costly, so that a technique that can detect the abnormality in parallel with the business operation of the rail vehicle has been developed.
  • the electric current threshold is changed between during the running on a straight track and during the running on a curved track on the basis of the track position to allow the difference between the electric current values due to the speed difference between the inner wheel and the outer wheel during the running on the curved track, thereby preventing the abnormality misdetection during the running on the curved track.
  • Patent Literature 1 Japanese Unexamined Patent Application Publication No. 2015-42106
  • the present invention has been made in view of the above points, and proposes an abnormality detection device, an abnormality detection system, and an abnormality detection method, which are capable of detecting an abnormality on a track.
  • a rail vehicle that runs on a track includes an acquisition section that acquires a threshold excess time indicating the time at which a characteristic value of a physical quantity of each drive system disposed in the rail vehicle exceeds a threshold and a drive system position indicating the position of the drive system with respect to the rail vehicle at the threshold excess time, a determination section that determines whether or not there is a correlation between the plurality of threshold excess times and the drive system positions at the threshold excess times acquired for the plurality of drive systems disposed in the rail vehicle by the acquisition section, and an output section that performs output on the basis of the determination result of the determination section. If it is determined by the determination section that there is a correlation, the output section outputs a track abnormality signal indicating that there is an abnormality on the track.
  • the abnormality detection device it is possible to achieve the abnormality detection device, the abnormality detection system, and the abnormality detection method, which are capable of detecting an abnormality on the track.
  • Fig. 1 is a diagram illustrating an example of the configuration of an abnormality detection system 100 according to a first embodiment.
  • the abnormality detection system 100 includes a plurality of rail vehicles, such as a rail vehicle 1, and a host system 20 that can communicate with the plurality of rail vehicles. It should be noted that the respective configurations of the plurality of rail vehicles are basically the same, and thus, this embodiment will be described by taking the rail vehicle 1 as an example.
  • an abnormality related to the rail vehicle 1 that runs on a track 2 an abnormality on the rail vehicle 1, an abnormality on the track 2, and the like
  • an abnormality related to the rail vehicle 1 that runs on a track 2 an abnormality on the rail vehicle 1, an abnormality on the track 2, and the like
  • the rail vehicle 1 is configured of vehicles 1a to 1n, includes wheels 3a to 3k, and runs on the track 2 at, for example, a vehicle speed v. It should be noted that in this embodiment, the distances to the respective wheel axes relative to the end of the rail vehicle 1 (relative positions) will be described as drive system positions y a to y k .
  • the host system 20 generates an instruction for controlling the running of the rail vehicle 1 (a d-axis electric current instruction, a q-axis electric current instruction, a stop instruction, a fall back instruction, and the like) to transmit the instruction to the rail vehicle 1 (an example of an instruction section), and receives data transmitted from the rail vehicle 1 to record the data.
  • the functions of the host system 20 may be achieved such that a CPU reads and executes the program of a memory, may be achieved by hardware, such as a dedicated circuit, or may be achieved by other methods.
  • the host system 20 appropriate configurations can be adopted.
  • computers may be disposed for the respective rail vehicles, a computer that manages all these computers may be further disposed, or other configurations may be adopted.
  • some components of the host system 20 may be disposed in the rail vehicle 1, and may be disposed to be separated from the host system 20 and the rail vehicle 1.
  • Fig. 2(A) is a diagram illustrating an example of the configuration of one (vehicle 1a) of the rail vehicle 1. It should be noted that the other vehicles 1b to 1n other than the vehicle 1a of the rail vehicle 1 have the same configuration, and the illustration and description thereof are thus omitted.
  • the vehicle 1a includes the wheels 3a to 3d, motors 4a to 4d, inverters 5a to 5d, gears 6a to 6d, and an abnormality detection device 8.
  • the torque of the motor 4a that is a drive source is controlled by the inverter 5a, and is transmitted through the gear 6a to the wheel 3a.
  • the inverter 5a includes a main circuit 5a1 and a control circuit 5a2.
  • the case where the motor 4a and the inverter 5a are included in the wheel 3a, the motor 4b and the inverter 5b are included in the wheel 3b, the motor 4c and the inverter 5c are included in the wheel 3c, and the motor 4d and the inverter 5d are included in the wheel 3d is illustrated, but when the motors and the inverters are included in at least two front and rear wheels, the detection of an abnormality on the track 2 can be achieved.
  • the wheel 3a, the motor 4a, and the gear 6a are defined as a drive system 7a
  • the wheel 3b, the motor 4b, and the gear 6b are defined as a drive system 7b
  • the abnormality detection targeting the track 2 and the drive systems 7a and 7b will be mainly described. It should be noted that the abnormality detection of drive systems 7c to 7k (not illustrated) can also be executed in the same manner.
  • the abnormality detection device 8 is an FPGA (Field-Programmable Gate Array), a personal computer, and the like.
  • the functions of the abnormality detection device 8 illustrated in Fig. 2(B) may be achieved by, for example, an integrated circuit in which the designer can set its configuration, may be achieved such that a CPU reads and executes the program of a memory, may be achieved by hardware, such as a dedicated circuit, and may be achieved by other methods.
  • the abnormality detection device 8 is connected communicatively to the inverters 5a and 5b, acquires physical quantities, such as an electric current flowing through the inverter 5a to the motor 4a and an electric current flowing through the inverter 5b to the motor 4b, or an electric current flowing to the inverter 5a and an electric current flowing to the inverter 5b (an example of the acquisition section), and detects (determines) an abnormality on the track 2 and the drive systems 7a and 7b on the basis of the acquired physical quantities (an example of the determination section).
  • the abnormality detection device 8 is connected communicatively to the host system 20, and transmits and receives various information (for example, transmits a track abnormality signal, a drive system abnormality signal, and the like described later, and receives the d-axis electric current instruction, the q-axis electric current instruction, the stop instruction, the fall back instruction, and the like) (an example of the output section).
  • Fig. 3(A) is a graph illustrating the electric current waveform of a U-phase electric current i u-4a of the motor 4a when an abnormality is caused on the track 2 or the drive system 7a
  • Fig. 3 (B) is a graph illustrating the electric current waveform of a U-phase electric current i u-4b of the motor 4b when an abnormality is caused on the track 2 or the drive system 7b.
  • Thresholds ⁇ i ux represent the range within which when the track 2 and the drive systems 7a and 7b are normal, the U-phase electric currents i u-4a and i u-4b fall, and for example, can be set on the basis of the rated electric current value of the motor 4a.
  • examples of the abnormality on the track 2 include wearing, rupturing, and the like.
  • Examples of the abnormality on the drive system 7a include the deformation of the wheel 3a, the seizing of the bearing of the wheel 3a, the deformation of the rotation shaft of the motor 4a, the loss of the tooth of the gear 6a, and the like.
  • both of the abnormality on the track 2 and the abnormality on the drive system 7a finally become the disturbance torques to the motor 4a, but since the torque of the motor 4a and the electric current of the motor 4a are in a proportional relationship, each of the abnormalities appear as the electric current waveform.
  • the U-phase electric current i u-4a exceeds the threshold i ux . This is ditto for the U-phase electric current i u-4b of the motor 4b.
  • the problem is that when the abnormality is detected by observing the U-phase electric current i u-4a , that cause cannot be limited to one of the track 2 and the drive system 7a. Consequently, the maintenance operator is required to inspect both of the track 2 and the drive system 7a, thereby increasing the operation time.
  • the abnormality detection is performed by observing the U-phase electric currents i u-4a and i u-4b , and further, the abnormality cause is required to be capable of being identified.
  • Figs. 4 are diagrams illustrating flowcharts related to processes executed by the abnormality detection device 8.
  • a flowchart 8a illustrated in Fig. 4 (A) is a flowchart for the electric current observation
  • a flowchart 8b illustrated in Fig. 4(B) is a flowchart for the abnormality detection
  • each of the flowcharts is repeatedly processed independently.
  • the processing contents illustrated in the flowcharts 8a and 8b are forms of the implementation examples of the abnormality detection device 8, and when there are the data of threshold excess times and drive system positions, which will be described later, immediately before the track abnormality and drive system abnormality discrimination flow in Figs. 4 , that is, at the stage of a flow (A0), the flow therebefore is arbitrary.
  • the abnormality detection device 8 observes the U-phase electric current (step S10). Subsequently, the abnormality detection device 8 determines whether or not a characteristic value of the U-phase electric current exceeds a threshold (step S12). If determining that the characteristic value exceeds the threshold, the abnormality detection device 8 records time at that time (hereinafter, threshold excess time) and the drive system number of the threshold excess (step S14), and if determining that the characteristic value does not exceed the threshold, the abnormality detection device 8 ends the process.
  • the drive system number is information that can uniquely identify the drive systems 7a to 7k, and the drive system number is associated with the drive system position.
  • the characteristic value is the maximum value of the physical quantity (for example, the U-phase electric current), the effective value of the physical quantity, the bandpass filter value of the physical quantity, a particular frequency component by Fourier analysis of the physical quantity (for example, FFT (Fast Fourier Transform, and the like), a data processing result by a statistical method for the physical quantity, and the like, and can enhance the accuracy of the abnormality detection as compared with the case where the observation value of the physical quantity is directly used.
  • the physical quantity for example, the U-phase electric current
  • the effective value of the physical quantity for example, the bandpass filter value of the physical quantity
  • a particular frequency component by Fourier analysis of the physical quantity for example, FFT (Fast Fourier Transform, and the like)
  • the following observation targets may be added. Also, instead of that, the observation of the U-phase electric current may be stopped.
  • Machine data the rotation speeds of the drive systems 7a to 7d, the torques of the drive systems 7a to 7d, the vibrations of the drive systems 7a to 7d, and the noises of the drive systems 7a to 7d
  • Control software variable a d-axis electric current instruction deviation, and a q-axis electric current instruction deviation
  • the d-axis electric current instruction deviation is, for example, one of the control software variables of the control circuit 5a2, and is also the deviation between the d-axis electric current instruction of the control software variable (the instruction value transmitted from the host system 20) and the d-axis electric current (actual measurement value) of the motor 4a. This is ditto for the q-axis electric current instruction deviation.
  • the control software variable that represents the deviation between the control instruction and the control target quantity, such as the d-axis electric current instruction deviation and the q-axis electric current instruction deviation is zero in the normal state, and is increased at the change to the abnormal state, so that the control software variable is suitable as the observation target for the abnormality detection.
  • the abnormality detection device 8 determines whether or not the number of data of the threshold excess times and the drive system numbers is sufficient (whether or not there are a predetermined number of data) (step S20). If determining that there are a predetermined number of data, the abnormality detection device 8 moves the process to step S22, and if determining that there are not a predetermined number of data, the abnormality detection device 8 ends the process. It should be noted that the predetermined number is referred to as a number for determining whether or not there is a correlation between the plurality of threshold excess times and the drive system positions .
  • the correlation can be determined depending on whether or not the threshold excess times and the drive system positions are matched with the vehicle speed v, so that when there are at least two data (two sets of the threshold excess times and the drive system positions), the correlation can be determined.
  • step S22 the abnormality detection device 8 reads the data of the threshold excess times and the drive system numbers. Subsequently, the abnormality detection device 8 examines a correlation between the threshold excess times and the drive system positions (step S24), and if determining that there is a correlation, the abnormality detection device 8 outputs the track abnormality signal indicating that there is an abnormality on the track 2 (track abnormality) (step S26), and if determining that there is not a correlation, the abnormality detection device 8 outputs the drive system abnormality signal indicating that there is an abnormality on the drive systems 7a to 7d (drive system abnormality) (step S28), and ends the process.
  • the output may transmit the signal to the host system 20, may transmit the signal to other rail vehicles different from the rail vehicle 1, may represent a determination result on a display, an alarm light, and the like (not illustrated) disposed in the rail vehicle 1, may be other outputs, and may be a combination of these.
  • Figs. 5 illustrate examples of the relationships between threshold excess times t a to t k and the drive system positions y a to y k .
  • Fig. 5(A) is a graph illustrating an example of the relationships between the threshold excess times t a to t k and the drive system positions y a to y k in the track abnormality
  • Fig. 5 (B) is a graph illustrating an example of the relationships between the threshold excess times t a to t k and the drive system positions y a to y k in the drive system abnormality.
  • a log related to the threshold excess is called a threshold excess log, as needed.
  • the difference between the case where the track abnormality 9a is present and the case where the drive system abnormalities 9b1 to 9b4 are present appears to be the presence or absence of a correlation between the threshold excess times and the drive system positions. Accordingly, in the flowchart 8b, if there is a correlation between the plurality of threshold excess times and the drive system positions, the abnormality detection device 8 outputs the track abnormality signal, and if there is not a correlation between the plurality of threshold excess times and the drive system positions, the abnormality detection device 8 outputs the drive system abnormality signal.
  • the points (t a , y a ) to (t k , y k ) in Fig. 5 (A) are aligned on the straight line with the tilt v only when the vehicle speed v is constant.
  • the abnormality detection device 8 outputs the track abnormality signal when the points (t a , y a ) to (t k , y k ) are overlapped on the integral curve of a vehicle acceleration p, and outputs the drive system abnormality signal when the points (t a , y a ) to (t k , y k ) are not overlapped on the integral curve of the vehicle acceleration p.
  • the difference between the track abnormality 9a and the drive system abnormalities 9b1 to 9b4 appears to be the presence or absence of a correlation between the threshold excess times and the drive system positions, which is ditto for the case where an abnormality is caused only on some wheels (for example, the wheel 3a) in Fig. 6(B) .
  • the data related to the track abnormality 9a can also be extracted first according to the correlation to discriminate the presence or absence of the drive system abnormalities 9b1 to 9b4 from the remaining data.
  • the operation principle of the abnormality detection device 8 has been described above. According to such the configuration, the track abnormality and the drive system abnormality can be discriminated, so that the maintenance operation time can be shortened. Means for enhancing the effectiveness of the abnormality detection device 8 will be described below.
  • the following may be executed.
  • the host system 20 may output the track abnormality signal by using the recording data of the plurality of rail vehicles .
  • the host system 20 can estimate (identify) the portion in which the track abnormality is caused (abnormality portion) at high accuracy by mutually comparing the track abnormality positions acquired from the recording data of the individual rail vehicles (an example of the identification section).
  • the abnormality detection device 8 may measure environment data related to the environment conditions, such as temperature and humidity, and adjust the threshold on the basis of the measurement result (an example of the adjustment section). For example, since at low temperature, the track 2 is shrunk and the gap between joints, the gap between pointers, and the like on the track 2 are increased, there is a fear that when the rail vehicle 1 passes through them, the threshold excess is caused. In that case, the abnormality detection device 8 misdetects the joint and the pointer that should be normal as the track abnormality 9a. Accordingly, the misdetection can be prevented by adjusting the threshold according to temperature.
  • environment data related to the environment conditions such as temperature and humidity
  • the abnormality detection device 8 desirably adjusts the threshold with respect to temperature.
  • the abnormality detection device 8 may transmit the abnormality detection signal to the neighboring rail vehicle (for example, the subsequent rail vehicle), the host system 20 (for example, a railway operation management system), and the like.
  • the railway operation management system may be included in the host system 20, or may be non-included in the host system 20.
  • the host system 20 desirably stops or fall back operates the corresponding drive system. Also in the case where the corresponding drive system is stopped, when the normal drive system remains in the corresponding rail vehicle, the host system 20 may transmit, to the corresponding rail vehicle, an instruction to move the rail vehicle to the nearby station or the maintenance site by the normal drive system.
  • Fig. 7 is a diagram illustrating a flowchart related to a process executed by the abnormality detection device 8 according to a second embodiment. However, the illustration and description of the same points as the first embodiment are omitted, as needed.
  • the abnormality detection device 8 can discriminate the track abnormality and the joint. Its operation principle will be described below.
  • Fig. 8(A) is a schematic diagram illustrating joints 10, and Fig. 8(B) is a schematic diagram illustrating the track abnormalities 9a.
  • the joint 10 is not an abnormality on the track 2, but there is a possibility that the threshold excess is caused also by the joint 10, so that a configuration discriminating the joint 10 and the track abnormality 9a is preferably adopted.
  • the abnormality detection device 8 determines the presence or absence of the periodicity of a correlation between the threshold excess times and the drive system positions (step S30). For example, the abnormality detection device 8 determines the presence or absence of the periodicity on the basis of at least two or more correlations when the intervals between the joints 10 are known, and determines the presence or absence of the periodicity on the basis of at least three or more correlations when the intervals between the joints 10 are unknown.
  • the abnormality detection device 8 If determining that the periodicity is present, the abnormality detection device 8 outputs a joint detection signal indicating the joint 10 on the track 2 (step S32), and if determining that the periodicity is absent, the abnormality detection device 8 outputs the track abnormality signal (step S34), and ends the process. It should be noted that in step S28, the abnormality detection device 8 outputs the drive system abnormality signal like the first embodiment.
  • the operation principle of the abnormality detection device 8 according to this embodiment has been described above. According to such the configuration, in addition to the effect of the first embodiment, the misdetection of the track abnormality due to the joint can be prevented, so that the maintenance cost can be reduced.
  • Fig. 10 is a diagram illustrating a flowchart related to a process executed by the abnormality detection device 8 according to a third embodiment. However, the illustration and description of the same points as the first embodiment are omitted, as needed.
  • the abnormality detection device 8 can identify the abnormality portions on the drive systems 7a to 7d. Its operation principle will be described below.
  • abnormalities on the drive systems 7a to 7k are classified into abnormalities on the wheels 3a to 3k, the gears 6a to 6k, and the motors 4a to 4k.
  • a plurality of abnormalities are caused at the same time.
  • this will be described by taking, as an example, the case where the vehicle abnormality 9b1 (the same meaning as the sign in Fig. 6(B) ) is caused on the drive system 7a, a gear abnormality 9c is caused on the drive system 7b, and a motor abnormality 9d is caused on the drive system 7d.
  • Fig. 11(A) is a diagram illustrating the wheel abnormality 9b1, Fig.
  • FIG. 11(B) is a diagram illustrating the gear abnormality 9c
  • Fig. 11(C) is a diagram illustrating the motor abnormality 9d.
  • a gear abnormality 9c' on the wheel side is also considered, but the result when this is caused is the same as the wheel abnormality 9b1 in Fig. 11(A) , and the description thereof is thus omitted.
  • Fig. 12 is a graph illustrating the threshold excess logs related to the wheel abnormality 9b1, the gear abnormality 9c, and the motor abnormality 9d.
  • the influence that the respective abnormality causes give to the motors 4a to 4d is periodic, and as illustrated in Fig. 12 , the threshold excesses are observed for periods T 1 to T 3 different for the respective abnormality portions (abnormality causes).
  • the threshold excess periods T 1 to T 3 are derived as follows.
  • the threshold excess periods T 1 to T 3 are different for the respective abnormality causes. Accordingly, as illustrated in the flowchart (drive system abnormality discrimination flow) in Fig. 10 , the abnormality causes are identified according to the threshold excess periods T 1 to T 3 , thereby outputting any one of a wheel abnormality signal indicating that there is an abnormality on the wheels 3a to 3d, a gear abnormality signal indicating that there is an abnormality on the gears 6a to 6d, and a motor abnormality signal indicating that there is an abnormality on the motors 4a to 4d, more than one of the wheel abnormality signal indicating that there is an abnormality on the wheels 3a to 3d, the gear abnormality signal indicating that there is an abnormality on the gears 6a to 6d, and the motor abnormality signal indicating that there is an abnormality on the motors 4a to 4d, or all of the wheel abnormality signal indicating that there is an abnormality on the wheels 3a to 3d
  • the abnormality detection device 8 determines whether or not the threshold excess period becomes wheel circumference/vehicle speed (step S40), and if determining that the threshold excess period becomes the wheel circumference/vehicle speed, the abnormality detection device 8 moves the process to step S42, and if determining that the threshold excess period does not become the wheel circumference/vehicle speed, the abnormality detection device 8 moves the process to step S44.
  • step S42 the abnormality detection device 8 outputs a wheel and wheel axis side gear abnormality signal (wheel abnormality signal), and moves the process to step S44.
  • step S44 the abnormality detection device 8 determines whether or not the threshold excess period becomes wheel circumference/vehicle speed/gear ratio, and if determining that the threshold excess period becomes the wheel circumference/vehicle speed/gear ratio, the abnormality detection device 8 moves the process to step S46, and if determining that the threshold excess period does not become the wheel circumference/vehicle speed/gear ratio, the abnormality detection device 8 moves the process to step S48.
  • step S46 the abnormality detection device 8 outputs a motor side gear abnormality signal (gear abnormality signal), and moves the process to step S48.
  • step S48 the abnormality detection device 8 determines whether or not the threshold excess period becomes wheel circumference/vehicle speed/gear ratio/the number of pole pairs, and if determining that the threshold excess period becomes the wheel circumference/vehicle speed/gear ratio/the number of pole pairs, the abnormality detection device 8 moves the process to step S50, and if determining that the threshold excess period does not become the wheel circumference/vehicle speed/gear ratio/the number of pole pairs, the abnormality detection signal 8 ends the process.
  • step S50 the abnormality detection device 8 outputs the motor abnormality signal, and ends the process.
  • step S26 the abnormality detection device 8 outputs the track abnormality signal like the first embodiment.
  • the presence or absence of all the respective abnormalities is determined successively, so that when there are a plurality of abnormalities on the certain drive system, the flowchart illustrated in Fig. 10 is applicable.
  • the number of deforming portions can also be identified by a data processing technique, such as machine learning.
  • the abnormality detection device 8 according to this embodiment, the abnormality portion of the drive system can be identified, so that by arranging only any necessary maintenance components, the number of maintenance component inventories can be reduced.
  • Figs. 13 are diagrams illustrating flowcharts related to processes executed by the abnormality detection device 8 according to a fourth embodiment.
  • a flowchart 8c illustrated in Fig. 13(A) is a flowchart for the electric current observation
  • a flowchart 8d illustrated in Fig. 13(B) is a flowchart for the abnormality detection
  • each of the flowcharts is repeatedly processed independently.
  • the abnormality detection device 8 discriminates each of the track abnormality, the drive system abnormality, the joint, and the pointer. Its operation principle will be described below.
  • step S60 the abnormality detection device 8 observes the electric current like the first embodiment. Subsequently, the abnormality detection device 8 records a characteristic value of the electric current and a track position (step S62), and ends the process.
  • the feature of this embodiment is to record the characteristic value of the electric current and the track position, and hereinafter, this is called a characteristic value log.
  • the track position represents the position on the track 2 (absolute position) .
  • the track position can be measured by for example, GPS (Global Positioning System). Also, the track position can also be estimated, for example, from the integral value of the vehicle speed v. Also, by other methods, the abnormality detection device 8 can also acquire the track position.
  • the origin of the track position is arbitrary, but for example, the stop position target of a starting station can be set to the origin.
  • step S70 the abnormality detection device 8 determines whether or not the number of data of the characteristic values and the track positions is sufficient for discriminating the track abnormality, the drive system abnormality, the joint, and the pointer. If determining that the number of data is sufficient, the abnormality detection device 8 moves the process to step S72, and if determining that the number of data is not sufficient, the abnormality detection device 8 ends the process.
  • step S72 the abnormality detection device 8 reads the data of the characteristic values and the track positions. Subsequently, the abnormality detection device 8 determines whether or not the characteristic values of all the read data are near "0" (step S74), and if determining that the characteristic values of all the read data are near "0", the abnormality detection device 8 ends the process, and if determining that the characteristic value of any one of all the read data is not near "0", the abnormality detection device 8 moves the process to step S76.
  • step S76 the abnormality detection device 8 determines dependence of the characteristic values with respect to the track positions, and if determining that there is dependence, the abnormality detection device 8 moves the process to step S78, and if determining that there is not dependence, the abnormality detection device 8 outputs the drive system abnormality signal (step S88), and ends the process.
  • Fig. 14 (A) when there is an abnormality on the drive systems 7a to 7k, normalized characteristic values s exceed the threshold "1" irrespective of track positions x.
  • the track abnormality, the joint, and the pointer are present, these are phenomena depending on the track positions x, so that the characteristic values s exceed the threshold "1" only at particular track positions x j and x k .
  • the abnormality detection device 8 discriminates the track abnormalities 9a illustrated in Fig. 15(A) , the joints 10 illustrated in Fig. 15(B) , and pointers 11 illustrated in Fig. 15(C) .
  • the track abnormalities 9a illustrated in Fig. 15 (A) are the same as those illustrated in Fig. 8(B)
  • the joints 10 illustrated in Fig. 15 (B) are the same as those illustrated in Fig. 8(A)
  • the position intervals between the joints 10 are constant, so that the joint 10 can be discriminated by the process of the flowchart illustrated in Fig. 7 (see the second embodiment).
  • the abnormality detection device 8 determines whether or not the intervals between the track positions at which the characteristic values exceed the threshold (hereinafter, threshold excess positions) are constant, and if determining that the intervals are constant, the abnormality detection device 8 outputs the joint detection signal (step S80), and ends the process. On the other hand, if determining that the intervals are not constant, the abnormality detection device 8 cannot discriminate the pointers 11 and the track abnormalities 9a only by the position intervals at this time since the position intervals between the pointers 11 illustrated in Fig. 15(C) are not constant like the track abnormalities 9a.
  • the pointer 11 and the track abnormality 9a are discriminated on the basis of the change with time of the characteristic values s of the pointer 11 and the track abnormality 9a.
  • the characteristic values s at the track position x j are increased according to the elapse of the time t (t0 ⁇ t1 ⁇ t2 ⁇ t3).
  • the characteristic values s at the track position x k are not dependent on time t because the pointer 11 has been present since the installation of the track 2.
  • the abnormality detection device 8 determines whether or not the characteristic values are increased with the elapse of time, and if determining that the characteristic values are increased, the abnormality detection device 8 outputs the track abnormality signal (step S84), and if determining that the characteristic values are not increased, the abnormality detection device 8 outputs a pointer detection signal indicating the pointer 11 on the track 2 (step S86), and ends the process.
  • the abnormality detection device 8 discriminates the pointer 11 and the track abnormality 9a. It should be noted that to observe the elapse of time, the abnormality detection device 8 may read the data recorded in the past, and may acquire the past data from the host system 20.
  • Fig. 17 illustrates the threshold excess logs when the track abnormality 9a, the joints 10, and the pointers 11 illustrated in Fig. 15(D) are present.
  • the operation of the abnormality detection device 8 at this time is as follows.
  • the joints 10 are present at track positions x 1 , x 2 , x 3 , and x 4 illustrated in Fig. 17 , and intervals ⁇ x between the threshold excess positions are constant, so that the joint detection signal is outputted.
  • the track abnormality 9a is present at the x j illustrated in Fig. 17 , the intervals between the threshold excess positions are not constant, and the characteristic values are increased with the elapse of time, so that the track abnormality signal is outputted.
  • the pointer 11 is present at the x k illustrated in Fig. 17 , the intervals between the threshold excess positions are not constant, and the characteristic values are not increased with the elapse of time, so that the pointer detection signal is outputted.
  • the abnormality detection device 8 can output the abnormality signal or the detection signal matched with them.
  • the abnormality detection device 8 can also identify the causing position of the track abnormality 9a by comparison with the recording of the track position (or by reference to the track position).
  • an abnormality on the joint 10 and the pointer 11 can also be detected by means, for example, changing the threshold for each abnormal state, and changing the definition of the observation amount and the characteristic value for each abnormal state.
  • each of the track abnormality, the drive system abnormality, the joint, and the pointer can be discriminated, so that the maintenance operation time can be shortened. Also, according to such the configuration, like the third embodiment, the number of maintenance component inventories can be reduced.
  • the abnormality detection device 8 performs the abnormality detection has been described, but the present invention is not limited to this, and other devices may perform the abnormality detection.
  • the process illustrated in the flowchart 8b in Fig. 4 the process illustrated in the flowchart in Fig. 7 , the process illustrated in the flowchart in Fig. 10 , and the process illustrated in the flowchart 8d in Fig. 13 may be executed by the host system 20.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • General Health & Medical Sciences (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
  • Control Of Electric Motors In General (AREA)
EP17920338.5A 2017-08-04 2017-08-04 Anomalitätsdetektionsvorrichtung, anomalitätsdetektionssystem und anomalitätsdetektionsverfahren Pending EP3663160A4 (de)

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US5579013A (en) * 1994-05-05 1996-11-26 General Electric Company Mobile tracking unit capable of detecting defective conditions in railway vehicle wheels and railtracks
DE19837485A1 (de) * 1998-08-12 2000-02-17 Siemens Ag Verfahren zum Erkennen von Schäden an Schienenfahrzeugen und/oder Gleisen
JP4319101B2 (ja) * 2004-07-08 2009-08-26 株式会社日立製作所 移動体異常検知システム
JP2006327551A (ja) * 2005-05-30 2006-12-07 Tmp:Kk 車両運行管理システム及びこれを用いた車両および軌道異常診断方法
JP2007275244A (ja) * 2006-04-05 2007-10-25 Kawasaki Plant Systems Ltd 挙動異常検出装置および検出方法
JP5382991B2 (ja) * 2006-12-11 2014-01-08 三菱重工業株式会社 軌道系交通システムの異常診断方法及び異常診断システム
DE102008049224A1 (de) * 2008-09-27 2010-06-02 Thales Defence Deutschland Gmbh Verfahren und Vorrichtung zum Überprüfen mindestens eines Laufwerks eines auf einem Gleis fahrbaren Schienenfahrzeugs auf einen Defekt
JP5432818B2 (ja) * 2010-05-24 2014-03-05 株式会社日立製作所 鉄道車両の状態監視装置及び状態監視方法、並びに鉄道車両
JP5525404B2 (ja) * 2010-10-01 2014-06-18 株式会社日立製作所 鉄道車両の状態監視装置及び状態監視方法、並びに鉄道車両
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JP2015042106A (ja) 2013-08-23 2015-03-02 三菱重工業株式会社 軌道走行電動車両の故障検出装置、および軌道走行電動車両

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