JP4569437B2 - Abnormality diagnosis device - Google Patents

Description

  The present invention relates to an abnormality diagnosis device for a rotating or sliding component used in a railway vehicle or the like, and more particularly, to an abnormality diagnosis device for specifying presence / absence or a sign of an abnormality of the component or an abnormal part thereof.

  Conventionally, after rotating parts of a railway vehicle are used for a certain period, the axle bearings and other rotating parts are regularly inspected for abnormalities such as damage and wear. This periodic inspection is performed by disassembling the mechanical device in which the rotating part is incorporated, and the operator can detect damage and wear generated in the rotating part by visual inspection. And the main defects found in the inspection are in the case of bearings, indentations caused by biting of foreign matter, peeling due to rolling fatigue, other wear, etc., in the case of gears, tooth defects and wear, etc. In the case of a wheel, there is wear such as a flat, and in any case, if irregularities or wear that is not found in a new article is found, it is replaced with a new one.

  However, in the method of disassembling the entire mechanical equipment and visually inspecting by the operator, disassembly work to remove the rotating body and sliding member from the device, and reassembly of the inspected rotating body and sliding member into the device There has been a problem that a great deal of labor is required for the work, and the maintenance cost of the apparatus is greatly increased.

  Further, when reassembling, the inspection itself may cause a defect in the rotating body or the sliding member, such as attaching a dent to the rotating body or the sliding member that was not present before the inspection. Further, since a large number of bearings are visually inspected within a limited time, there is a problem that a possibility of overlooking a defect remains. Further, the determination of the degree of this defect also varies from person to person, and parts are exchanged even if there is virtually no defect, resulting in unnecessary costs.

  Accordingly, various methods have been proposed for diagnosing abnormalities of rotating parts in an actual operating state without disassembling a mechanical device in which the rotating parts are incorporated (see, for example, Patent Documents 1 to 3). Most commonly, as described in Patent Document 1, an accelerometer is installed in the bearing section, the vibration acceleration of the bearing section is measured, and FFT (Fast Fourier Transform) processing is performed on this signal. There is known a method of performing diagnosis by extracting a signal of vibration generation frequency components.

In addition, various methods have been proposed for detecting flat portions called flats caused by friction and wear of the rails due to wheel locks and sliding due to malfunctions of brakes on the rolling surfaces of the wheels of railway vehicles (for example, patents). References 4-6.) In particular, Patent Document 4 proposes a device that detects a defect state of a railroad vehicle wheel and a track through which the train passes by a vibration sensor, a rotation measuring device, or the like.
JP 2002-22617 A JP 2003-202276 A Japanese Patent Laid-Open No. 2004-257836 Japanese National Patent Publication No. 9-500452 JP-A-4-148839 Special table 2003-535755 gazette

  However, the defect state detection device described in Patent Document 4 has a problem that it cannot be identified whether the abnormal vibration is caused by a flat wheel, an axle bearing, or a track or other abnormality.

  The present invention has been made in view of the above-described circumstances, and an object of the present invention is to detect abnormal vibration of an axle bearing and a wheel from an output signal of a vibration sensor that detects vibration of the axle bearing or the wheel, and to detect the abnormal vibration. An object of the present invention is to provide an abnormality diagnosing device that can identify whether the wheel is due to a flat wheel or an axle bearing.

The object of the present invention is achieved by the following constitution.
1. An abnormality diagnosis device for a mechanical device having a plurality of parts having different vibration characteristics,
A sensor signal processing unit that samples an output signal of a vibration sensor that detects vibration of the mechanical device;
A diagnostic processing unit for performing abnormality diagnosis based on vibration data sampled by the sensor signal processing unit;
With
The diagnosis processing unit
While continuously acquiring the vibration data from the sensor signal processing means, it is divided into intervals of a fixed period, the vibration data for one interval is processed as vibration data for component diagnosis of the first vibration characteristic, and 1 interval An abnormality diagnosing device characterized in that data obtained by adding the last predetermined time data of the previous section to the head of vibration data for one minute is processed as vibration data for component diagnosis of the second vibration characteristic.
(2) A rail vehicle axle bearing and wheel abnormality diagnosis device,
A sensor signal processing unit that samples an output signal of a vibration sensor that detects vibrations of an axle bearing and a wheel;
A diagnostic processing unit for diagnosing an abnormality in an axle bearing and a wheel based on vibration data sampled by the sensor signal processing unit;
With
The diagnosis processing unit
While continuously acquiring the vibration data from the sensor signal processing means, it is divided into intervals of a fixed period, the vibration data for one section is processed as vibration data for bearing diagnosis, and the head of the vibration data for one section is processed. An abnormality diagnosing device characterized in that the data obtained by adding the data for the last predetermined time in the previous section is processed as vibration data for wheel diagnosis.
(3) The diagnosis processing unit detects an abnormality of the axle bearing based on the rotational speed of the axle bearing and the frequency peak obtained by processing the envelope waveform of the vibration, and the vibration level generated in synchronization with the rotation of the wheel. The abnormality diagnosis device according to (2), wherein abnormality of a wheel is detected based on a frequency that exceeds a threshold value, and abnormality diagnosis is performed based on a detection result of each abnormality.
(4) The signal processing means
The abnormality diagnosis device according to any one of (2) to (3), wherein output signals of a plurality of vibration sensors are switched and sampled channel by channel.
(5) The above configuration is characterized in that the abnormality diagnosis of the axle bearing and the wheel is performed based on the vibration data obtained by sampling the output signal of the vibration sensor in synchronization with the rotation of the wheel and performing the averaging process ( The abnormality diagnosis device according to 3) or (4).
(6) The above-mentioned (3), (4), and (5) are characterized in that abnormality detection is performed a plurality of times for each of the axle bearing and the wheel, and the abnormality diagnosis is statistically performed from the total values for each of the plurality of times . Any abnormality diagnosis device.
(7) The abnormality diagnosis device according to any one of (1) to (6) above, which has a function of storing data used when an abnormality is detected.

According to the abnormality diagnosis apparatus of the present invention, the following effects (I) to (IV) can be obtained.
(I) While continuously acquiring vibration data, it is divided into intervals of a fixed period, and vibration data for one section is processed as vibration data for component diagnosis of the first vibration characteristic, and vibration data for one section Since the data obtained by adding the data for the last predetermined time of the previous section to the head of the first is processed as the vibration data for component diagnosis of the second vibration characteristic, the vibration for detecting the vibration of the component having both vibration characteristics Detects abnormal vibration of parts with both vibration characteristics in real time from the output signal of the sensor, and specifies whether the abnormal vibration is due to abnormality of the first vibration characteristic part or the second vibration characteristic part can do.
(II) While continuously acquiring vibration data, it is divided into intervals of a fixed period, and vibration data for one section is processed as vibration data for bearing diagnosis, and one of them is added to the head of vibration data for one section. Since the data obtained by adding the data for the last predetermined time in the previous section is processed as vibration data for wheel diagnosis, abnormal vibrations of the axle bearings and wheels are detected from the output signals of the vibration sensors that detect the vibrations of the axle bearings and wheels. By detecting in real time, it can be specified whether the abnormal vibration is caused by a flat wheel or an axle bearing.
(III) While continuously acquiring vibration data, the vibration data is converted into two types of data having different sampling frequencies or sampling lengths for parts diagnosis of the first vibration characteristic and parts diagnosis of the second vibration characteristic and processed. Therefore, the abnormal vibration of the part having both vibration characteristics is detected in real time from the output signal of the vibration sensor that detects the vibration of the part having both vibration characteristics, and the abnormal vibration is caused by the abnormality of the part having the first vibration characteristic. It can be specified whether the component is of the second vibration characteristic.
(IV) Since vibration data is continuously captured and converted into two types of data having different sampling frequencies or sampling lengths for axle bearing diagnosis and wheel diagnosis, the vibrations of the axle bearing and the wheel are detected. Abnormal vibrations of the axle bearing and the wheel can be detected in real time from the output signal of the vibration sensor, and it can be specified whether the abnormal vibration is caused by the wheel flat or the axle bearing.

  Hereinafter, a first embodiment, a second embodiment, a third embodiment, a fourth embodiment, a fifth embodiment, a sixth embodiment, a seventh embodiment, and an eighth embodiment of the abnormality diagnosis apparatus according to the present invention. Will be described in detail with reference to the drawings.

  1A is a schematic plan view of a railway vehicle equipped with the abnormality diagnosis device of the present invention, FIG. 1B is a schematic side view of the railway vehicle, and FIG. 2 illustrates the positional relationship between an axle bearing and a vibration sensor. FIG. 3 is a block diagram of the first embodiment of the abnormality diagnosis apparatus according to the present invention. FIG. 4 is a time chart of vibration data acquisition and data analysis for four channels by the abnormality diagnosis apparatus of the present invention. Is a flowchart showing the operation contents of the diagnostic processing unit of FIG. 3, FIG. 6 is a diagram showing the relationship between the scratched portion of the axle bearing and the vibration generation frequency caused by the scratch, and FIG. 7 is related to the present invention FIG. 8 is a block diagram of the third embodiment of the abnormality diagnosis apparatus according to the present invention, FIG. 9 is a flowchart showing the operation contents of the diagnosis processing unit of FIG. 8, and FIG. Fourth embodiment of abnormality diagnosis apparatus according to the present invention FIG. 11 is a block diagram of a fifth embodiment of the abnormality diagnosis apparatus according to the present invention, FIG. 12 is a block diagram of the sixth embodiment of the abnormality diagnosis apparatus according to the present invention, and FIG. 13 is an abnormality according to the present invention. FIG. 14A is a conceptual diagram showing the relationship between bearing separation diagnosis data and wheel flat diagnosis data on the time-frequency plane, and FIG. 14A is a flowchart showing the operation content of the diagnosis processing unit in the sixth embodiment of the diagnosis apparatus. FIG. 15B is a conceptual diagram showing the relationship between the frequency range of the bearing and the wheel, FIG. 15 is a flowchart showing the operation content of the diagnosis processing unit in the seventh embodiment of the abnormality diagnosis apparatus according to the present invention, and FIG. 16 is the eighth embodiment. FIG. 17 is a flowchart showing the operation contents of the diagnosis processing unit in the eighth embodiment of the abnormality diagnosis apparatus according to the present invention.

(First embodiment)
First, with reference to FIGS. 1-6, the abnormality diagnosis apparatus of 1st Embodiment is demonstrated.
As shown in FIG. 1, one rail vehicle 100 is supported by two front and rear chassis, and four wheels 101 are attached to each chassis. A vibration sensor 111 that detects vibration generated from the rotation support device 110 during operation is attached to the rotation support device (bearing box) 110 of each wheel 101.

  The control panel 115 of the railway vehicle 100 is equipped with two abnormality diagnosis devices 150 that take in sensor signals for four channels simultaneously (substantially simultaneously) and perform diagnosis processing. In other words, the output signals of the four vibration sensors 111 provided in each chassis are input to another abnormality diagnosis device 150 for each chassis via the signal lines 116. The abnormality diagnosis device 150 also receives a rotation speed pulse signal from a rotation speed sensor (not shown) that detects the rotation speed of the wheel 101.

  As shown in FIG. 2, the rotation support device 110 is provided with an axle bearing 130 that is a rotating part as an example, and the axle bearing 130 is a rotating wheel that is externally fitted to a rotation shaft (not shown). An inner ring 131, an outer ring 132 that is a fixed ring fitted in a housing (not shown), a ball 133 that is a plurality of rolling elements disposed between the inner ring 131 and the outer ring 132, and the ball 133 can freely roll. And a retainer (not shown). The vibration sensor 111 is held in a posture capable of detecting vibration acceleration in the direction of gravity and is fixed near the outer ring 132 of the housing. Various sensors such as an acceleration sensor, an AE (acoustic emission) sensor, an ultrasonic sensor, and a shock pulse sensor can be used as the vibration sensor 111.

  As shown in FIG. 3, the abnormality diagnosis device 150 includes a sensor signal processing unit 150A and a diagnosis processing unit (MPU) 150B. The sensor signal processing unit 150 </ b> A includes four amplifiers / filters (AFILTs) 151. The output signals of the four vibration sensors 111 are individually input to the amplifier / filter 151. Each amplifier / filter 151 has both an analog amplifier function and an anti-aliasing filter function. The four-channel analog signals amplified and filtered by these four amplifying / filtering devices 151 are signaled for each channel by a multiplexer (MUX) 152 functioning as a switch function based on a signal of a diagnostic processing unit (MPU) 150B. And converted into a digital signal by the AD converter (ADC) 153 and taken into the diagnosis processing unit (MPU) 150B. On the other hand, the rotational speed pulse signal from the rotational speed sensor is shaped by the waveform shaping circuit 155, and then the number of pulses per unit time is counted by a timer counter (not shown), and the value is used as a rotational speed signal as a diagnostic processing unit. (MPU) is input to 150B. The diagnosis processing unit (MPU) 150B performs abnormality diagnosis based on the vibration waveform detected by the vibration sensor 111 and the rotation speed signal detected by the rotation speed sensor. The diagnosis result by the diagnosis processing unit (MPU) 150B is output to the communication line 120 (see FIG. 1) via the line driver (LD) 156. The communication line 120 is connected to an alarm device, and an appropriate alarm operation is performed when an abnormality such as a flatness of the wheel 101 occurs.

When the rotational speed signal detected by the rotational speed sensor is a substantially constant predetermined speed (185 to 370 min ^{−1 in the} present embodiment), the diagnostic processing unit (MPU) 150B performs sampling frequency fs and sampling number Ns. Is processed to detect the flatness of the wheel 101. Specifically, if fs = 2 kHz and Ns = 2000, the block data section length is 1 sec. The flat is detected by comparing the number of times that the vibration waveform pulse due to the flat is counted per second and the number of times the wheel 101 is rotated per second from the vehicle speed detected by the rotational speed sensor.

  The vibration acceleration in the state where the flatness is generated in the wheel 101 is large, and the value of the vibration acceleration caused by the normal vehicle vibration is often smaller than that. Further, the vibration of the rail joint becomes a level of vibration acceleration equivalent to or larger than that of the flat. Furthermore, the level of vibration acceleration resulting from the friction between the rail and the wheel 101 in the rail curve is equivalent to that due to the flat or rail joint.

  On the other hand, a flat impact occurs once per rotation, whereas an impact caused by a rail joint occurs at a longer cycle, and an impact caused by rail friction occurs irregularly. Therefore, in this embodiment, paying attention to the regularity of shock (pulse) generation exceeding the threshold of vibration acceleration peculiar to flat, the number of shock waves per unit time at a substantially constant speed is counted, and the counted number is almost equal to that of the wheel. If it matches the rotation speed, an abnormality diagnosis is performed assuming that there is a high possibility that a flat has occurred.

  Furthermore, in this embodiment, an algorithm that repeatedly performs diagnosis processing on the same wheel 101 is designed, and the reliability of abnormality diagnosis is improved by a statistical judgment method that takes into account variations in the number of pulses and the influence of noise. .

  FIG. 4 shows a time chart of the vibration data acquisition and data analysis for four channels by the abnormality diagnosis device 150. The vibration data is constantly taken into the abnormality diagnosis device 150, but can be divided into certain sampling intervals according to the diagnosis target. The take-in period T1 required for the diagnosis (peeling detection) of the bearing 130 is sufficient to be less than 1 second. In order to reduce the influence of the contact noise between the rail and the wheel 101, it is desirable that it is as short as possible. On the other hand, in order to detect an abnormality in the rolling surface of the wheel 101, it is necessary to detect an impact every time the wheel 101 makes one revolution, so a long period T2 of about 1 second is required.

  When the vibration data capturing period T1 for bearing diagnosis is set to 0.67 seconds, for example, corresponding to the time required for capturing vibration data for four channels and the sampling frequency is 20 kHz, 4 × 0. Data of 67 × 20,000 points are captured. Therefore, assuming that the vibration data capturing period T2 for wheel diagnosis is 1 second, it is 0.33 seconds short if vibration data is captured at the vibration data capturing period T1 for bearing diagnosis. Therefore, the data for the period T2 is obtained by adding the data for one section, that is, the period T1 and the data for the last 0.33 seconds of the previous section. However, since the number of data can be thinned out by decimating processing after filtering as will be described later, it can be 2,000 points or less per channel. As a result, the time required to execute the diagnosis of the wheel 101 and the bearing 130 for four channels is made smaller than the cycle T1, that is, 0.67 seconds, so that the processing time of the wheel / bearing diagnosis data is given a margin. Can do.

  In the present embodiment, the diagnostic processing unit (MPU) 150B performs the acquisition of the vibration data and the wheel / bearing diagnostic data processing in parallel. In other words, real-time processing for completing the wheel / bearing diagnosis data processing is performed within the vibration data fetch period T1 for four channels. This real-time processing is realized by the sampling processing of the multiplexer 152 and the AD converter 153 of the sensor signal processing unit 150A by the diagnostic processing unit (MPU) 150B and performing data sampling. It can also be realized by data sampling by a direct memory access controller (DMA).

  In this way, the wheel / bearing diagnosis data processing time is given a margin, and by taking in the vibration data and processing the wheel / bearing diagnosis data in parallel, it is possible to eliminate data loss. The reliability of the diagnostic result obtained by statistically processing data including a stochastic process due to irregularity of the vehicle body, shaking of the vehicle body, load fluctuation, and the like can be improved.

  FIG. 5 shows an operation flow of the diagnostic processing unit (MPU) 150B. The diagnostic processing unit (MPU) 150B captures vibration data, that is, performs AD conversion and sampling of sensor signals for four channels (ie, S100) and bearing / wheel diagnostic data processing (ie, S200) in parallel. To do.

  In the wheel / bearing diagnosis data processing (ie, S200), every time the vibration data for four channels is updated (ie, S201), the rotational speed detection processing (ie, S202), the diagnosis processing (ie, S203), The diagnosis result storage and holding process (ie, S204) and the determination result output process (ie, S205) are sequentially performed.

  The rotation speed detection process (ie, S202) is a process for detecting the rotation speed of the bearing 130 based on the signal from the rotation speed sensor.

  The diagnosis process (ie, S203) includes a bearing diagnosis process (ie, S210) and a wheel diagnosis process (ie, S220).

  The bearing diagnosis process (that is, S210) is a process for detecting an abnormality of the bearing 130 based on the rotational speed of the bearing 130 and the frequency peak obtained by processing the envelope waveform of vibration. In the bearing diagnosis process (ie, S210), first, a bandpass filter (BPF) process is performed to extract mid-range vibration data in which high-frequency (3 kHz or higher) and low-frequency (200 Hz or lower) components are attenuated from the acquired vibration data. (I.e., S211), and after the decimating process (i.e., S212) is performed on the extracted data at a predetermined decimation rate, the absolute value process (i.e., S213) and the low frequency (1 kHz or less) component The low-pass filter process (that is, S214) is extracted sequentially. Further, after the decimating process (that is, S215) is further performed on the extracted data, the zero interpolation fast Fourier transform (FFT) process (that is, S216) is performed, thereby obtaining frequency data with a resolution of 1 Hz. This frequency data is subjected to peak detection processing by smoothing differentiation (ie, S217), and the basic frequency of bearing defects (see FIG. 6) obtained from the rotational speed and the bearing internal specifications is compared with the fourth order. Match or mismatch is determined (ie, S218: bearing defect determination processing).

  The wheel diagnosis process (ie, S220) is a process for detecting an abnormality of the wheel 110 from a phenomenon in which an impact occurs in synchronization with the rotation of the wheel 110. The main cause of the impact generated in synchronization with the rotation of the wheel 110 is the presence of a flat portion called a flat generated on the rolling surface of the wheel 110. In the wheel diagnosis process (i.e., S220), a low-pass filter (LPF) process (i.e., S221) for extracting a component having a predetermined frequency (1 kHz) or less is first executed from the acquired vibration data. After executing the decimating process (ie, S222) at the thinning rate, the current sampling period is used to secure data in a period (period T2) longer than one sampling period (period T1) as described in FIG. Overlap processing (that is, S223) is performed in which the last one third of the previous sampling interval is added to the beginning of the data of the current sampling interval. Next, of the data that has undergone the overlap processing (that is, S223), the data that exceeds the threshold is converted into an absolute value by the peak hold processing (that is, S224) and held at a value that exceeds the threshold for a certain time (τ). . This holding time (τ) is determined by the rotation speed of the wheel 110, and is selected to be shorter than one rotation of the wheel. This peak hold processing that converts the absolute value and holds it for a certain period of time enables stable peak measurement. Then, the number of times the pulse has exceeded the threshold is counted (that is, S225: threshold value exceeded count processing), and it is determined whether the count matches the number of rotations of the wheel 110 (that is, S226: wheel defect determination processing). .

  The bearing diagnosis process (that is, S210) and the wheel diagnosis process (that is, S220) are repeated for the vibration data for the four channels updated in step S201. That is, for each data update, the bearing diagnosis process (that is, S210) and the wheel diagnosis process (that is, S220) are each performed four times. And the determination result by each determination process (namely, S218, S226) is memorize | stored and hold | maintained in diagnostic processing part (MPU) 150B (namely, S204). The diagnosis processing unit (MPU) 150B stores and holds the results of the past N determination processes (that is, S218 and S226) retroactively from the latest determination result, and statistically determines abnormality from the N determination results. And output the result (ie, S205).

In other words, in the present embodiment, it is not determined that there is an abnormality if both the axle bearing 130 and the wheel 101 match the defect frequency of one time and the wheel rotation speed. This is because the frequency matching is based on a stochastic process, and therefore it is necessary to make a statistical determination from a plurality of tabulated values.
As a statistical determination method, generally, the average of spectrum can be raised. However, in the determination method used in this embodiment, if the bearing is a bearing, data representing the degree of coincidence of the spectrum with an integer value is obtained multiple times. If the reference value is reached after adding 16 times, it is determined that there is an abnormality, and if not, it is not determined that there is an abnormality, but it can be sufficiently applied to the abnormality diagnosis of the axle bearing of a railway vehicle. Even if there is a small separation on the bearing, it will not progress at a stretch if there is sufficient lubrication, sealing, etc., and the risk of affecting the running of the railway vehicle is small, and the impact on the running of the railway vehicle is small. This is because the occurrence of this abnormality is usually detected by another means such as a thermal fuse.

  As described above, in the abnormality diagnosis device of the present embodiment, the vibration of the axle bearing 130 or the wheel 101 is detected by the vibration sensor 111, the output signal of the vibration sensor 111 is sampled by the sensor signal processing unit 150A, and the vibration data is obtained. Based on this, the diagnosis processing unit (MPU) 150B performs abnormality diagnosis of the axle bearing 130 and the wheel 101. At that time, the diagnostic processing unit (MPU) 150B continuously captures the vibration data from the sensor signal processing unit 150A and divides it into sections of a fixed period, and uses the vibration data for one section as vibration data for bearing diagnosis. In addition to processing, the vibration data for wheel diagnosis is processed by adding the last predetermined time data of the previous section to the head of vibration data for one section. In this way, the vibration data for bearing diagnosis and the vibration data for wheel diagnosis are processed separately to identify whether the abnormal vibration is due to the flatness of the wheel 101 or the axle bearing 130 and perform an accurate diagnosis. Can be implemented.

  Further, in the abnormality diagnosis device of the present embodiment, all the four channels of sensor signals from the four vibration sensors 111 attached to the four rotation support devices 110 of each chassis are captured simultaneously (substantially simultaneously). Since the real-time processing for completing the diagnostic data processing within the data acquisition time is performed for these channels, there is no data loss, and an extremely reliable abnormality diagnosis can be performed.

(Second Embodiment)
FIG. 7 is a block diagram of the second embodiment (modified example of the first embodiment described above). The abnormality diagnosis apparatus 150 uses an MPU including a multiplexer (MUX) and an AD converter (ADC) as the diagnosis processing unit 150B. That is, the MPU has a part of the function of the sensor signal processing unit 150A. According to this configuration, the circuit in the abnormality diagnosis device 150 can be simplified, and cooperation with other MPU built-in circuits such as the DMA controller (DMAC) 157 can be easily realized by software, so the configuration of the first embodiment More efficient software control is possible.

(Third embodiment)
FIG. 8 is a block diagram of the third embodiment (modified example of the second embodiment described above). This abnormality diagnosis device 150 includes a static random access memory (SRAM) 162 having a backup battery (Batt) 161 as a storage element in addition to the configuration of FIG. Further, by adopting a hardware configuration in which an MPU built-in calendar clock circuit (RTC) 163 is made effective, data at the time of abnormality can be stored.

  FIG. 9 shows the contents of the wheel / bearing diagnosis data processing of the diagnosis processing unit 150B in the third embodiment. The diagnosis processing unit 150B performs bearing diagnosis processing (that is, S310) and wheel diagnosis processing (that is, S320) every time the vibration data for four channels is updated (that is, S201). Then, it is determined whether or not the spectral intensity of the envelope waveform of the bearing vibration obtained by the bearing diagnosis process (ie, S310) is greater than or equal to the reference value (ie, S311). If it is less than the reference value (ie, false in S311). ), The result of the bearing diagnosis process (i.e., S310) is stored and held for aggregation of N times (i.e., S204). Further, it is determined whether or not the count number of the event exceeding the vibration level threshold obtained by the wheel diagnosis process (that is, S320) coincides with the rotation number of the wheel 101 (that is, S321). In the case (that is, False in S321), the data is stored and held for N times of aggregation (that is, S204).

  On the other hand, if the spectrum intensity is equal to or greater than the reference value (ie, True in S311), the spectrum intensity of the bearing vibration envelope waveform is stored in the SRAM 162 together with the date / time information read from the calendar clock circuit (RTC) 163 (ie, S330). . Further, when the count number of the event exceeding the vibration level threshold matches the rotation number of the wheel 101 (that is, True in S321), the date and time when the time waveform data in the wheel diagnosis is read from the calendar clock circuit (RTC) 163. The information is stored in the SRAM 162 together with the information (ie, S330). When the stored data amount reaches the allowable amount of the SRAM 162, the oldest data is deleted (ie, S331).

  According to this embodiment, the abnormality determination result is transmitted to the alarm device to perform alarm processing, and the spectrum content and the like are read out from the data stored in the SRAM 162 and transmitted to the maintenance computer. It can be used as vehicle maintenance information.

(Fourth embodiment)
FIG. 10 is a block diagram of the fourth embodiment (a modification of the above-described third embodiment). The abnormality diagnosis apparatus 150 includes two sets of multiplexers (MUX) 152 and AD converters (ADC) 153 in the MPU, thereby enabling real-time diagnosis using 8-channel sensor signals with one module. Such multi-channel sensor signal input is possible for any number of channels by increasing the number of AD converters or using AD converters and multiplexers with a high conversion speed if the MPU's calculation capability permits. . In the example of FIG. 10, the calendar clock circuit (RTC) 163 is not built in the MPU, and the one with a backup battery (Batt) is externally attached to the MPU.

(Fifth embodiment)
FIG. 11 is a block diagram of the fifth embodiment (modified example of the second embodiment described above). This abnormality diagnosis apparatus 150 is obtained by adding a one-rotation signal frequency dividing circuit 165 to the structure of the abnormality diagnosis apparatus described with reference to FIG. The output of the waveform shaping circuit 155 is input to the diagnostic processing unit (MPU) 150B and the one rotation signal frequency dividing circuit 165. The 1-rotation signal dividing circuit 165 divides the rotation speed proportional sine wave shaped by the waveform shaping circuit 155, and gives a rotation synchronization signal of 1 pulse per rotation to the diagnosis processing unit (MPU) 150B. The diagnosis processing unit (MPU) 150B performs data sampling by using the rotation synchronization signal as a trigger in a section of a constant speed, and performs abnormality averaging by averaging the data. A component that is synchronized with the rotation of the wheel 101 is canceled by averaging the data sampled with the rotation synchronization signal generated every time the wheel 101 rotates as a trigger, so that components other than the signal synchronized with the rotation of the wheel 101 are canceled. Therefore, it is possible to accurately detect the flatness of the wheel 101 by the determination based on the threshold of the impact level.

(Sixth embodiment)
FIG. 12 is a block diagram of the sixth embodiment. The abnormality diagnosis apparatus 150 includes a sensor signal processing unit 150A and a diagnosis processing unit (MPU) 150B. The sensor signal processing unit 150 </ b> A includes one amplifier (Amp) 171 and one filter (LPF) 172. The output signals (analog signals) of the four vibration sensors 111 are input to one amplifier (Amp) 171 and amplified, and then input to one filter (LPF) 172. That is, in this embodiment, the amplifier (Amp) 171 and the filter (LPF) 172 are used to amplify and filter the output signals of the four channels from the four vibration sensors 111. The analog signal amplified and filtered by the amplifier (Amp) 171 and the filter (LPF) 172 is taken into the diagnostic processing unit (MPU) 150B, and the AD converter (ADC) 153 in the diagnostic processing unit (MPU) 150B. Is converted to a digital signal. On the other hand, the rotational speed pulse signal from the rotational speed sensor is shaped by the waveform shaping circuit 155, and then taken into the diagnostic processing unit (MPU) 150B, and is received by the timer counter (TCNT) 173 in the diagnostic processing unit (MPU) 150B. The number of pulses per unit time is counted, and the value is processed as a rotation speed signal. The diagnosis processing unit (MPU) 150B performs abnormality diagnosis based on the vibration waveform detected by the vibration sensor 111 and the rotation speed signal detected by the rotation speed sensor. The diagnosis result by the diagnosis processing unit (MPU) 150B is output to the communication line 120 (see FIG. 1) via the line driver (LD) 156. The communication line 120 is connected to an alarm device, and an appropriate alarm operation is performed when an abnormality such as a flatness of the wheel 101 occurs.

  Abnormalities that can be detected from the output signal of the vibration sensor 111 are peeling of the axle bearing 130 and flatness (wear) of the wheel 101. Both can be detected as vibration signals in the frequency band up to around 1 kHz. Therefore, in the sixth embodiment, an amplifier (Amp) 171 and a filter (LPF) 172 are used to amplify and filter the output signal of the vibration sensor 111. Then, the data filtered by the filter (LPF) 172 and converted into the digital signal by the AD converter (ADC) 153 is separated into the axle bearing diagnosis and the wheel diagnosis by software processing, and both abnormality diagnosis is performed. To do.

  Of the abnormalities occurring in the axle bearing 130, the outer ring raceway of the stationary ring is most likely to peel off. Therefore, for the axle bearing 130, the separation of the outer ring raceway of the stationary wheel is a detection target.

  The separation of the axle bearing 130 and the flatness of the wheel 101 differ by about 10 times in the frequency band of defects. The rotational speed (r / s) of the wheel 101 is equal to the fundamental frequency of the wheel flat. The range of the rotational speed to be diagnosed is 4 to 10 r / s (fundamental frequency: 4 to 10 Hz). On the other hand, when the outer ring track of the stationary ring of the axle bearing 130 is defective, the fundamental frequency of the defect is 33 to 83 Hz even in the same rotational speed range (4 to 40 r / s). In both cases, when examining harmonics up to the fourth order, 4 to 40 Hz for the wheel 101 and 33 to 330 Hz for the axle bearing 130 are the required frequency analysis ranges by DFT. A frequency resolution of 1.0 Hz is sufficient for diagnosis of the axle bearing 130. However, in the diagnosis of the axle bearing 130, the resolution is insufficient at 1.0 Hz, and it is easily affected by the DC component in the FFT low band due to the offset.

  Therefore, in the sixth embodiment, the data converted (sampled) into a digital signal by the AD converter (ADC) 153 is used for analyzing the axle outer ring raceway separation (for axle bearing diagnosis) and for wheel flat analysis (for wheel diagnosis). ) Are converted into two types of data having different sampling frequencies.

  FIG. 13 shows an operation flow of the diagnostic processing unit (MPU) 150B in the sixth embodiment. The diagnostic processing unit (MPU) 150B outputs sensor signals output from the four vibration sensors 111 and sent through the amplifier (Amp) 171 and the filter (LPF) 172 to the AD converter (ADC) 153. Sampling and conversion into a digital signal (ie, S401). Then, a decimation process (that is, S402) is performed on the output signal of the AD converter (ADC) 153 by FIR low-pass filtering realized by software. In this example, sampling in the AD converter (ADC) 153 is performed in units of 3 seconds at a frequency of 8 kHz. In the decimation process (that is, S402), the decimation rate M is set to 4 and the number of data is reduced to ¼ in order to reduce the sampling frequency fs to 2 kHz.

  The diagnosis processing unit (MPU) 150B uses the data subjected to the decimation processing (ie, S402) for analysis of the axle outer ring raceway separation (hereinafter referred to as “for bearing”) and for wheel flat analysis (hereinafter referred to as “for wheel”). Are converted into two types of data having different sampling frequencies (see FIG. 14A).

  The bearing data is obtained by dividing the data that has undergone the decimation process (ie, S402) into four and divided into data intervals of 0.75 seconds (ie, S411). The obtained data is sequentially subjected to absolute value processing (ie, S412) and AC processing (ie, S413). Further, by adding 0 for about 0.25 seconds (sec) per section, the data section length is about 1 second (ie, S414), and the frequency resolution is about 1.0 Hz and FFT is performed ( That is, S415). The number of input data of FFT is 2048. A Hanning window process is performed before the FFT. After the FFT, the outer ring defect frequency Zfc is obtained from the vehicle speed and bearing specification data, and peaks from the fundamental wave to the fourth order are detected (ie, S416). Then, the outer ring defect frequency Zfc is compared with the frequency peak, and the degree of coincidence between them is calculated (ie, S417). Abnormality determination of the axle bearing 130 is performed based on the total degree of coincidence score obtained by repeating this process a predetermined number of times.

  The data for the wheel is the data with the sampling frequency fs of 2 kHz that has undergone the decimation process (i.e., S402), the absolute value process (i.e., S421), and then the decimation process (LFP) with the decimation rate M as 4. That is, it is obtained by reducing the sampling frequency fs to 250 Hz by performing S422). The number of data at this point is 750, but zero padding interpolation (ie, S424) is performed to obtain data for about 4 seconds, so that the frequency resolution is about 0.25 Hz and FFT is performed (ie, , S425). A Hanning window process is performed before the FFT. After the FFT, peak detection is performed (that is, S426). Then, the higher order components from the fundamental frequency of the wheel flat to the fourth order are compared with the frequency peak, and the degree of coincidence between them is calculated (ie, S427). The abnormality determination of the wheel 101 is performed based on the total matching score obtained by repeating this process a predetermined number of times. The basic frequency of the wheel flat is obtained by counting the number of pulses per unit time of the rotation speed pulse signal with a timer counter (TCNT) 173.

As described above, each of the four vibration sensors 111 includes one set of an amplifier (Amp) 171 and a filter (LPF) 172, and is connected to an AD converter (ADC) 153 via a multiplexer (MUX) 152. By converting the data converted into digital signals (sampling) into two types of data with different sampling frequencies for bearings and wheels, and performing processing including FFT in two systems, bearing and wheel abnormality diagnosis Can be performed with high accuracy and high efficiency. On the other hand, when the frequency range of both the bearing and the wheel whose frequency regions are largely different by one FFT is examined, the accuracy (resolution) corresponding to the calculation cost cannot be realized (see FIG. 14B).
In the above example, most of the digital processing is performed by software, but part or all of the digital processing may be realized by hardware such as an FPGA (Field Programmable Gate Array).

(Seventh embodiment)
FIG. 15 shows an operation flow of the diagnostic processing unit (MPU) 150B in the seventh embodiment. In this example, sampling in the AD converter (ADC) 153 is performed in units of 3 seconds at a frequency of 16 kHz (ie, S501). In the decimation process (ie, S502), the decimation rate M is set to 4 and the number of data is reduced to ¼ in order to reduce the sampling frequency fs to 4 kHz.

  The diagnosis processing unit (MPU) 150B converts the data that has undergone the decimation process (ie, S502) into two types of data having different sampling frequencies for bearings and wheels (see FIG. 14A).

  The bearing data is obtained by dividing the data that has undergone the decimation process (ie, S502) into three and dividing it into data sections every 1.0 seconds (ie, S511). The obtained data is sequentially subjected to absolute value processing (ie, S512) and AC processing (ie, S513). Then, 0 interpolation processing is completely omitted or 0 interpolation processing is performed to interpolate 96 zeros to 4000 data, for example, with a small fraction, and FFT is performed with a frequency resolution of about 1.0 Hz ( That is, S514). A Hanning window process is performed before the FFT. After the FFT, the outer ring defect frequency Zfc is obtained from the vehicle speed and bearing specification data, and peaks from the fundamental wave to the fourth order are detected (ie, S515). Then, the outer ring defect frequency Zfc is compared with the frequency peak, and the degree of coincidence between them is calculated (ie, S516). Abnormality determination of the axle bearing 130 is performed based on the total degree of coincidence score obtained by repeating this process a predetermined number of times.

  For the wheel data, after the decimation process (ie, S502) and the sampling frequency fs of 4 kHz, the absolute value process (ie, S521) is performed, and then the filter (LFP) is further decimated (ie, S522). Thus, the sampling frequency fs is obtained by dropping it down to 500 Hz. AC processing (that is, S523) is sequentially performed on the obtained data. Thereafter, zero padding interpolation (that is, S524) is performed to obtain data for about 4 seconds, so that the frequency resolution is about 0.25 Hz and FFT is performed (that is, S525). A Hanning window process is performed before the FFT. After FFT, peak detection is performed (ie, S526). Then, the higher order components from the fundamental frequency of the wheel flat to the fourth order are compared with the frequency peak, and the degree of coincidence between them is calculated (ie, S527). The abnormality determination of the wheel 101 is performed based on the total matching score obtained by repeating this process a predetermined number of times.

  As in the seventh embodiment, in the bearing data processing, the number of FFT processing can be reduced by performing the zero interpolation processing such that the zero interpolation processing is completely omitted or only a small fraction is interpolated. it can. That is, in comparison with the sixth embodiment, the number of FFT processes executed simultaneously is reduced from 4 times to 3 times. However, it is possible to increase the number of FFT intervals in which rail noise and the like can be avoided by performing 0 interpolation processing and dividing the FFT interval into shorter times as in the sixth embodiment.

(Eighth embodiment)
FIGS. 16A and 16B are partial block diagrams of a diagnostic processing unit (MPU) 150B in the eighth embodiment. In FIG. 16A, in the hardware configuration of FIG. 12, an absolute value circuit (ABS) 181 is provided at the front stage (input side) of the AD converter (ADC) 153 in the diagnostic processing unit (MPU) 150B, and then A low pass filter (LPF) 182 is provided in the stage. In FIG. 16 (b), in the hardware configuration of FIG. 12, an envelope circuit (ENV) 191 is provided at the front stage (input side) of the AD converter (ADC) 153 in the diagnostic processing unit (MPU) 150B, and the front stage thereof. A high pass filter (HPF) 192 is provided.

  FIG. 17 shows an operation flow of the diagnostic processing unit (MPU) 150B in the eighth embodiment. The diagnostic processing unit (MPU) 150B outputs sensor signals output from the four vibration sensors 111 and sent through the amplifier (Amp) 171 and the filter (LPF) 172 to the AD converter (ADC) 153. Sampling and conversion into a digital signal (ie, S601). Then, the output signal of the AD converter (ADC) 153 is subjected to decimation processing by the low-pass filter 182 in the diagnostic processing unit (MPU) 150B. In this example, sampling in the AD converter (ADC) 153 is performed at a frequency of 2 kHz in units of 3 seconds. Further, the low-pass filter 182 reduces the number of data to 1/4 by setting the decimation rate M to 4.

  The bearing data is obtained by dividing the data that has undergone the decimation processing by the low-pass filter 182 into four and dividing it into data intervals of 0.75 seconds (ie, S611). The obtained data is subjected to absolute value processing by the absolute value circuit 181 and then subjected to AC processing (that is, S612). Further, by adding 0 for about 0.25 seconds (sec) per section, the data section length is about 1 second (ie, S613: zero-filled interpolation), and the frequency resolution is about 1.0 Hz. FFT is performed (ie, S614). A Hanning window process is performed before the FFT. After the FFT, the outer ring defect frequency Zfc is obtained from the vehicle speed and bearing specification data, and peaks from the fundamental wave to the fourth order are detected (ie, S615). Then, the outer ring defect frequency Zfc is compared with the frequency peak, and the degree of coincidence between them is calculated (ie, S616). Abnormality determination of the axle bearing 130 is performed based on the total degree of coincidence score obtained by repeating this process a predetermined number of times.

  The wheel data is obtained by subjecting the sensor signal output from the vibration sensor 111 to absolute value processing by the absolute value circuit 181, sampling at 2 kHz, and then decimating the decimation rate M as 8 (ie, S 621). It is obtained by reducing the sampling frequency fs to 250 Hz. The obtained data is subjected to AC processing (ie, S622). Further, zero padding interpolation (ie, S623) is performed to obtain data for about 4 seconds, so that the frequency resolution is about 0.25 Hz and FFT is performed (ie, S624). A Hanning window process is performed before the FFT. After the FFT, peak detection of higher order components from the fundamental wave to the fourth order is performed (ie, S625). Then, the higher order components from the fundamental frequency of the wheel flat to the fourth order are compared with the frequency peak, and the degree of coincidence between them is calculated (ie, S626). The abnormality determination of the wheel 101 is performed based on the total matching score obtained by repeating this process a predetermined number of times.

  In the eighth embodiment, the decimation process (that is, S402) and the absolute value process (that is, S412), which have been performed by software in FIG. The signal processing by wear is simplified. Even if the sampling frequency fs in the AD converter (ADC) 153 is decreased from 8 kHz in the case of FIG. 13 to 2 kHz, which is a quarter of the sampling frequency fs, it is possible to perform abnormality determination with high accuracy and high efficiency.

(A) is a schematic plan view of the railway vehicle carrying the abnormality diagnosis device of the present invention, and (b) is a schematic side view of the railway vehicle. It is the schematic which illustrates the positional relationship of an axle shaft bearing and a vibration sensor. 1 is a block diagram of a first embodiment of an abnormality diagnosis apparatus according to the present invention. It is a time chart of the acquisition of the vibration data for 4 channels by an abnormality diagnosis apparatus, and a data analysis. It is a flowchart which shows the operation | movement content of the diagnostic process part of FIG. It is a figure which shows the relationship between the site | part of the damage of an axle shaft bearing, and the vibration generation frequency which generate | occur | produces resulting from a damage | wound. It is a block diagram of 2nd Embodiment of the abnormality diagnosis apparatus which concerns on this invention. It is a block diagram of 3rd Embodiment of the abnormality diagnosis apparatus which concerns on this invention. It is a flowchart which shows the operation | movement content of the diagnostic process part of FIG. It is a block diagram of 4th Embodiment of the abnormality diagnosis apparatus which concerns on this invention. It is a block diagram of 5th Embodiment of the abnormality diagnosis apparatus which concerns on this invention. It is a block diagram of 6th Embodiment of the abnormality diagnosis apparatus which concerns on this invention. It is a flowchart which shows the operation | movement content of the diagnostic process part in 6th Embodiment of the abnormality diagnosis apparatus which concerns on this invention. (A) is the conceptual diagram which shows the relationship on the time-frequency plane of the bearing peeling diagnostic data and the wheel flat diagnostic data, and (b) is a conceptual diagram which shows the relationship between the frequency range of a bearing and a wheel. It is a flowchart which shows the operation | movement content of the diagnostic process part in 7th Embodiment of the abnormality diagnosis apparatus which concerns on this invention. (A) And (b) is a partial block diagram of the diagnostic process part in 8th Embodiment. It is a flowchart which shows the operation | movement content of the diagnostic process part in 8th Embodiment of the abnormality diagnosis apparatus which concerns on this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 100 Rail vehicle 101 Wheel 110 Rotation support apparatus 111 Vibration sensor 130 Axle bearing 150 Abnormality diagnosis apparatus 150A Sensor signal processing part 150B Diagnosis processing part 152 Multiplexer 162 SRAM
165 1-rotation signal frequency dividing circuit 171 Amplifier 172 Filter

Claims (7)

An abnormality diagnosis device for a mechanical device having a plurality of parts having different vibration characteristics,
A sensor signal processing unit that samples an output signal of a vibration sensor that detects vibration of the mechanical device;
A diagnostic processing unit for performing abnormality diagnosis based on vibration data sampled by the sensor signal processing unit;
With
The diagnosis processing unit
While continuously acquiring the vibration data from the sensor signal processing means, it is divided into intervals of a fixed period, the vibration data for one interval is processed as vibration data for component diagnosis of the first vibration characteristic, and 1 interval An abnormality diagnosing device characterized in that data obtained by adding the last predetermined time data of the previous section to the head of vibration data for one minute is processed as vibration data for component diagnosis of the second vibration characteristic. An abnormality diagnosis device for an axle bearing and a wheel of a railway vehicle,
A sensor signal processing unit that samples an output signal of a vibration sensor that detects vibrations of an axle bearing and a wheel;
A diagnostic processing unit for diagnosing an abnormality in an axle bearing and a wheel based on vibration data sampled by the sensor signal processing unit;
With
The diagnosis processing unit
While continuously acquiring the vibration data from the sensor signal processing means, it is divided into intervals of a fixed period, the vibration data for one section is processed as vibration data for bearing diagnosis, and the head of the vibration data for one section is processed. An abnormality diagnosing device characterized in that the data obtained by adding the data for the last predetermined time in the previous section is processed as vibration data for wheel diagnosis.   The diagnostic processing unit detects an abnormality of the axle bearing based on the rotational speed of the axle bearing and the frequency peak obtained by processing the envelope waveform of the vibration, and the level of vibration generated in synchronization with the rotation of the wheel has a threshold value. The abnormality diagnosis device according to claim 2, wherein abnormality of the wheel is detected based on the frequency of exceeding, and abnormality diagnosis is performed based on a detection result of each abnormality. The signal processing means is
4. The abnormality diagnosis apparatus according to claim 2, wherein output signals from a plurality of vibration sensors are sampled by switching one channel at a time.   5. An abnormality diagnosis of an axle bearing and a wheel is performed based on vibration data obtained by sampling an output signal of a vibration sensor in synchronization with wheel rotation and performing an averaging process. The abnormality diagnosis device described. The abnormality diagnosis device according to any one of claims 3 to 5, wherein abnormality detection is performed a plurality of times for each of the axle bearing and the wheel, and the abnormality diagnosis is statistically performed based on a total value for each of the plurality of times . The abnormality diagnosis apparatus according to claim 1, wherein the abnormality diagnosis apparatus has a function of storing data used when an abnormality is detected .

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