Diagnostic device and diagnostic method

Abstract

A diagnostic device (1) comprises a first data generation unit (11), a protrusion amount estimation unit (12), a rotation speed acquisition unit (13), and a diagnosis unit (14). The first data generation unit (11) generates, from a vibration sensor signal output by a vibration sensor (92) for measuring the vibration magnitude of target equipment, spectrum data indicating the distribution of the vibration magnitude of the target equipment for each frequency. The protrusion amount estimation unit (12) detects peaks and dips of the vibration magnitude in the spectrum data, and determines the peak protrusion amount for each peak. The rotation speed acquisition unit (13) selects at least one peak in descending order of the peak protrusion amount, acquires the frequency of the selected at least one peak as an approximate rotation speed, which is an approximate value of the rotation speed of a rotation body, and determines the rotation speed of the rotation body from the at least one approximate rotation speed. The diagnosis unit (14) diagnoses the state of the target equipment on the basis of the peak value of peaks in the spectrum data in a frequency band including a diagnostic target frequency in accordance with the rotation speed.

Description

Diagnostic device and diagnostic method

[0001] The present disclosure relates to diagnostic devices and methods.

[0002] In equipment having a rotating body, such as an electric motor, bearings, gear devices, etc., contact between components as the rotating body rotates can cause wear or damage. Therefore, the condition of equipment having a rotating body is diagnosed. One example of a diagnostic method is to analyze vibration data of the rotating body based on the rotation speed of the rotating body. This diagnostic method requires rotation speed data for each rotating body, so a rotation sensor must be provided near each rotating body. To prevent the installation of rotation sensors near each rotating body from complicating the equipment to be diagnosed or the diagnostic device, Patent Document 1 discloses an example of a diagnostic device that diagnoses the condition of equipment without using a rotation sensor.

[0003] The diagnostic monitoring device disclosed in Patent Document 1 detects multiple peaks in vibration magnitude from frequency analysis data based on the detection results of vibration acceleration sensors that individually detect vibrations in two orthogonal directions of a rotating machine, and detects the frequencies of the detected multiple peaks.The diagnostic monitoring device determines the rotation speed of the rotating machine from the frequencies of the multiple peaks, and determines the vibration state of the rotating machine based on the rotation speed.

[0004] JP 2010-216938 A

[0005] The diagnostic monitoring device disclosed in Patent Document 1 determines a spectral value to be a peak value when, among spectral values ​​equal to or greater than a threshold, the peak occupancy rate, which indicates the degree to which the spectral value protrudes from surrounding frequency components, is equal to or greater than a threshold. Since the peak occupancy rate of equipment mounted on a mobile object decreases when the mobile object is subjected to vibrations while traveling, it is difficult for the diagnostic monitoring device disclosed in Patent Document 1 to accurately determine the vibration state of a rotating machine.

[0006] The present disclosure has been made in consideration of the above circumstances, and aims to provide a diagnostic device and a diagnostic method that are capable of accurately diagnosing the state of a target device with a simple configuration.

[0007] To achieve the above object, a diagnostic device disclosed herein is a diagnostic device for diagnosing the condition of a target device mounted on a mobile object, the diagnostic device having a rotor that rotates around a rotation axis, and including a first data generation unit, a protrusion amount estimation unit, a rotation speed acquisition unit, and a diagnosis unit. The first data generation unit generates spectral data indicating a distribution of the vibration magnitude of the target device for each frequency from a vibration sensor signal output by a vibration sensor that measures the vibration magnitude of the target device. The protrusion amount estimation unit detects peaks and dips in the vibration magnitude of the spectral data, and for each peak, calculates a peak protrusion amount from the peak value and the dip value of a dip adjacent to the peak. The rotation speed acquisition unit selects at least one peak in descending order of peak protrusion amount, acquires the frequency of the at least one selected peak as an approximate rotation speed that is an estimate of the rotation speed of the rotor, and calculates the rotation speed of the rotor from the at least one approximate rotation speed. The diagnosis unit detects a peak in the vibration magnitude of the spectrum data in a frequency band including the frequency to be diagnosed, using the frequency to be diagnosed that is determined based on the rotation speed of the rotating body obtained by the rotation speed acquisition unit, and diagnoses the condition of the target equipment based on the peak value of the peak detected in the frequency band including the frequency to be diagnosed.

[0008] A diagnostic device according to the present disclosure selects at least one peak from spectral data in descending order of peak prominence, acquires the frequency of the at least one selected peak as an estimated rotation speed, and calculates the rotation speed of a rotating body from the at least one estimated rotation speed. The diagnostic device diagnoses the condition of a target device from peak values ​​of peaks in the spectral data in a frequency band including a diagnosis target frequency determined based on the rotation speed. The diagnostic device calculates an estimated rotation speed, which is an approximate value of the rotation speed of the rotating body, and calculates the rotation speed of the rotating body from the estimated rotation speed. Therefore, the diagnostic device does not require a rotation sensor provided for each rotating body, and can accurately diagnose the condition of the target device with a simple configuration while reducing the influence of vibrations caused by the moving body when it is traveling.

[0009] FIG. 1 is a diagram showing an example of mounting an electric motor, which is a diagnosis target of the diagnostic device according to embodiment 1, on a railway vehicle. FIG. 2 is a block diagram of the diagnostic device according to embodiment 1. FIG. 3 is a diagram showing a hardware configuration of the diagnostic device according to embodiment 1. FIG. 4 is a flowchart showing an example of diagnostic processing performed by the diagnostic device according to embodiment 1. FIG. 5 is a diagram showing an example of spectrum data in embodiment 1. FIG. 6 is a flowchart showing an example of diagnostic processing performed by the diagnostic device according to embodiment 2. FIG. 7 is a diagram showing an example of change over time in approximate rotation speed in embodiment 2. FIG. 8 is a block diagram of the diagnostic device according to embodiment 3. FIG. 9 is a diagram showing an example of mounting a vibration sensor on a railway vehicle according to embodiment 3. FIG. 10 is a diagram showing an example of arrangement of vibration sensors in embodiment 3. FIG. 11 is a flowchart showing an example of diagnostic processing performed by the diagnostic device according to embodiment 3. FIG. 12 is a diagram showing an example of phase difference for each frequency obtained from cross-spectral data in embodiment 3. FIG. 13 is a block diagram of the diagnostic device according to embodiment 4.

[0010] A diagnostic device and a diagnostic method according to an embodiment of the present disclosure will be described in detail below with reference to the drawings, in which the same or equivalent parts are designated by the same reference numerals.

[0011] (Embodiment 1) A diagnostic device according to embodiment 1 will be described using, as an example of a target device mounted on a moving body, a diagnostic device that diagnoses the state of an electric motor mounted on a railway vehicle and generates propulsive force for the railway vehicle. Two bogies 41 shown in Fig. 1 are attached to each carriage of the railway vehicle, for example, to support the car body. In Fig. 1, the X-axis direction indicates the width direction of the carriage. The Y-axis direction indicates the direction of travel of the railway vehicle. The Z-axis is perpendicular to both the X-axis and the Y-axis. When the railway vehicle is positioned horizontally, the Z-axis direction indicates the vertical direction.

[0012] At least one of the vehicles is an electric vehicle. Fig. 1 shows a bogie 41 that supports the body of the electric vehicle. Two electric motors 91 are attached to the bogie 41. The electric motors 91 rotate by receiving a supply of electric power from a power conversion device (not shown). A vibration sensor 92 is provided near a shaft 91a, which is an example of a rotating body, of each electric motor 91. The vibration sensor 92 is an acceleration sensor, a displacement sensor, a speed sensor, or the like.

[0013] The bogie 41 includes a joint 43 connected to the shaft 91 a of each electric motor 91, a gear device 44 that transmits the rotational force transmitted from the electric motor 91 via the joint 43 to the axle 45, the axle 45, and wheels 42 attached to both ends of the axle 45. When each electric motor 91 receives a supply of electric power from the power conversion device and operates, the shaft 91 a of each electric motor 91 rotates, and the rotational force of the shaft 91 a is transmitted to the axle 45 via the joint 43 and the gear device 44. Then, as the axle 45 rotates, the wheels 42 attached to both ends of the axle 45 rotate, generating propulsive force for the railway vehicle.

[0014] As shown in FIG. 2 , a diagnostic device 1 for diagnosing the condition of a target device having a rotating body, for example, an electric motor 91 having a rotating shaft 91 a, includes a first data generation unit 11 for generating spectral data indicating the distribution of the vibration magnitude of the electric motor 91 for each frequency from the measurement values ​​of a vibration sensor 92, a protrusion amount estimation unit 12 for calculating the peak protrusion amount of the spectral data, a rotation speed acquisition unit 13 for calculating the rotation speed of the shaft 91 a from the estimated rotation speed corresponding to the frequency of the peak with a large peak protrusion amount, and a diagnostic unit 14 for diagnosing the condition of the electric motor 91 from the rotation speed and the spectral data.

[0015] The hardware configuration of the diagnostic device 1 having the above configuration is shown in Fig. 3. The diagnostic device 1 includes a processor 81, a memory 82, and an interface 83. The processor 81, the memory 82, and the interface 83 are connected to one another via a bus 80. The processor 81 includes any electronic circuit including a transistor, and is considered to be a circuit or a processor circuit.

[0016] The functions of the diagnostic device 1 are realized by software, firmware, which is software built into an electronic device, or a combination of software and firmware. The software is written as a program and stored in the memory 82. The processor 81 reads and executes the program stored in the memory 82, thereby realizing the functions of the above-mentioned parts. In other words, the memory 82 stores a program for executing the processing of the diagnostic device 1.

[0017] The memory 82 includes, for example, non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read-Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), EEPROM (Electrically Erasable and Programmable Read-Only Memory), magnetic disk, flexible disk, optical disk, compact disk, mini disk, DVD (Digital Versatile Disc), etc.

[0018] The diagnostic device 1 is connected to the vibration sensor 92 via an interface 83. The interface 83 has an interface module that complies with one or more standards depending on the connected device.

[0019] The diagnostic device 1 having the above configuration continuously performs the diagnostic process shown in Fig. 4 during operation. The first data generator 11 included in the diagnostic device 1 generates spectrum data indicating the distribution of the vibration magnitude of the target device from the vibration sensor signal acquired from the vibration sensor 92 (step S11). The first data generator 11 shown in Fig. 2 acquires, from the vibration sensor 92, the vibration sensor signal, which is an analog data signal whose amplitude changes depending on the magnitude of the vibration of the measurement target. The first data generator 11 generates spectrum data, which is frequency domain data, from the vibration sensor signal, which is a time domain signal.

[0020] Specifically, the first data generator 11 generates spectral data for each target period by performing a Fast Fourier Transform (FFT) on digital data obtained by sampling the vibration sensor signal output by the vibration sensor 92 over the target period. The target period is, for example, one second. The first data generator 11 sends the generated spectral data to the protrusion amount estimator 12, the rotation speed acquirer 13, and the diagnostic unit 14.

[0021] As shown in Fig. 4, the protrusion amount estimation unit 12 calculates the protrusion amount of the peaks in the spectrum data (step S12). In detail, the protrusion amount estimation unit 12 detects peaks and dips in the vibration magnitude of the spectrum data, and calculates the peak protrusion amount for each detected peak from the peak value and the dip value of the dip adjacent to the peak. Fig. 5 shows an example of spectrum data. The horizontal axis of Fig. 5 represents frequency (unit: Hz), and the vertical axis represents the vibration magnitude in terms of acceleration amplitude (unit: m / s ２ ) is expressed as

[0022] The protrusion amount estimation unit 12 detects peaks indicated by black circles and dips indicated by white circles in Fig. 5. Peaks are the maximum values ​​of the spectrum, and dips are the minimum values ​​of the spectrum. Fig. 5 shows three peaks P1, P2, and P3 with large peak values. Dips D1 and D1' are adjacent to peak P1, dips D2 and D2' are adjacent to peak P2, and dips D1' and D3 are adjacent to peak P3.

[0023] The protrusion amount estimation unit 12 calculates the peak protrusion amount for each peak. The method for calculating the peak protrusion amount will be described using peaks P1, P2, and P3 as examples. The protrusion amount estimation unit 12 calculates the peak protrusion amount of peak P1 from the peak value, which is the spectrum value of peak P1, and the dip values, which are the spectrum values ​​of dips D1 and D1'. In detail, the protrusion amount estimation unit 12 linearly combines the differences between the peak value of peak P1 and the dip values ​​of dips D1 and D1' using coefficients that are each greater than or equal to 0 and less than or equal to 1 and whose sum is 1, and acquires the value as the peak protrusion amount.

[0024] As an example, the protrusion amount estimation unit 12 calculates a difference ΔDF1 between the peak value of peak P1 and the dip value of dip D1, and a difference ΔDF1' between the peak value of peak P1 and the dip value of dip D1'. The protrusion amount estimation unit 12 obtains a value obtained by linearly combining the difference ΔDF1 and the difference ΔDF1', for example, the average value, maximum value, or minimum value of the difference ΔDF1 and the difference ΔDF1', as the peak protrusion amount. As an example, the protrusion amount estimation unit 12 obtains the maximum value of the difference ΔDF1 and the difference ΔDF1' as the peak protrusion amount ΔP1. The peak protrusion amount may be expressed as either a linear value or a logarithmic value.

[0025] Similarly, the protrusion amount estimation unit 12 calculates the peak protrusion amount ΔP2 of peak P2 from the peak value of peak P2 and the dip values ​​of dips D2 and D2', and calculates the peak protrusion amount ΔP3 of peak P3 from the peak value of peak P3 and the dip values ​​of dips D1' and D3. As described above, the protrusion amount estimation unit 12 calculates the peak protrusion amount for each peak and sends the calculated peak protrusion amount and the frequency corresponding to the peak to the rotation speed acquisition unit 13. For example, the protrusion amount estimation unit 12 sends the peak protrusion amounts ΔP1, ΔP2, and ΔP3 and the peak frequencies F1, F2, and F3 for peaks P1, P2, and P3 to the rotation speed acquisition unit 13.

[0026] As shown in Fig. 4, the rotation speed acquisition unit 13 obtains an approximate rotation speed, which is an approximate value of the rotation speed of the shaft 91a, from the frequency of the peak (step S13). Specifically, the rotation speed acquisition unit 13 selects at least one peak in descending order of peak prominence, and acquires the frequency of the selected at least one peak as at least one approximate rotation speed. As an example, as shown in Fig. 5, the rotation speed acquisition unit 13 selects three peaks P1, P2, and P3 with large peak prominence. 1 Hz = 1 s －１ = 1 rps (revolutions per second), the rotation speed acquisition unit 13 acquires the frequencies F1, F2, F3 of the peaks P1, P2, P3 as approximate rotation speeds F1, F2, F3 (unit: rps).

[0027] 4, the rotation speed acquisition unit 13 obtains the rotation speed of the shaft 91a from the estimated rotation speed (step S14). Specifically, the rotation speed acquisition unit 13 obtains the estimated rotation speed obtained from peak P1, which has the largest peak protrusion amount, as the rotation speed of the shaft 91a. The rotation speed acquisition unit 13 sends the rotation speed of the shaft 91a to the diagnosis unit 14.

[0028] As shown in FIG. 4 , the diagnoser 14 determines a diagnosis target frequency from the rotation speed calculated in step S14 and detects a spectrum value representing the magnitude of vibration corresponding to the diagnosis target frequency from the spectrum data (step S15). The diagnosis target frequency is determined based on the rotation speed of the shaft 91a. Specifically, for example, vibrations occur in a bearing included in the electric motor 91 and rotatably supporting the shaft 91a due to whirling of a cage of the bearing, a vertical drop, or damage to the bearing. These vibrations are larger than those caused by the running of a railway vehicle and occur periodically. The period of the vibration depends on the rotation speed of the electric motor 91. In other words, a peak occurs in the spectrum data when the frequency coincides with the reciprocal of the vibration period. Therefore, the diagnoser 14 detects the spectrum value of the diagnosis target frequency by setting the reciprocal of the vibration period as the diagnosis target frequency.

[0029] As an example, the frequency to be diagnosed is the inner ring rolling element passing frequency Ft expressed by the following formula (1), specifically, the frequency of vibration that occurs periodically when a flaw occurs in the inner ring of the electric motor 91. In the following formula (1), Z indicates the number of rolling elements of the bearing that rotatably supports the shaft 91a, Fe indicates the rotation speed of the shaft 91a, and D ｐｗ indicates the pitch diameter of the rolling element, d indicates the diameter of the rolling element, and α indicates the contact angle of the bearing. ｐｗ , the diameter d of the rolling element, and the contact angle α of the bearing.

[0030]

[0031] The diagnosing unit 14 diagnoses the state of the electric motor 91 based on the peak value of the detected peak (step S16). For example, the diagnosing unit 14 determines whether the peak value is equal to or greater than an amplitude threshold, and if the peak value is equal to or greater than the amplitude threshold, determines that an abnormality has occurred in the electric motor 91. The amplitude threshold may be set to a value between the spectrum value of the electric motor 91 when it is normal and the spectrum value of the electric motor 91 when it is abnormal, based on simulations, test data, etc., for each frequency corresponding to the type of abnormality in the electric motor 91. The diagnosing unit 14 outputs the diagnosis result to an external device, for example, a display device provided in the driver's cab of the railway vehicle. After completing the process of step S16, the diagnosing device 1 repeats the above-described process from step S11.

[0032] As described above, the diagnostic device 1 according to the first embodiment selects at least one peak in the spectral data based on the vibration sensor signal in descending order of peak prominence, and calculates the rotation speed of the shaft 91a, which is a rotating body of the electric motor 91, which is the equipment to be diagnosed, from at least one estimated rotation speed, which is the frequency of the at least one selected peak. The diagnostic device 1 diagnoses the condition of the electric motor 91 based on the spectral value at the diagnosis target frequency corresponding to the rotation speed. The diagnostic device 1 does not require a rotation sensor for detecting the rotation speed of the shaft 91a, and therefore the configuration of the diagnostic device 1 is simple. By calculating the rotation speed of the shaft 91a according to the peak prominence of the spectral data, the diagnostic device 1 reduces false detections and is less susceptible to the influence of external vibration disturbances in the process of calculating the rotation speed of the shaft 91a. As a result, the diagnostic device 1 can accurately calculate the rotation speed of the shaft 91a. This enables the diagnostic device 1 to accurately diagnose the condition of the electric motor 91 with a simple configuration.

[0033] (Embodiment 2) The method of determining the rotation speed of the shaft 91a, which is an example of a rotating body of the target device, is not limited to the above example. In embodiment 2, a diagnostic device that determines the rotation speed of the shaft 91a in a method different from embodiment 1 will be described, focusing on the differences from embodiment 1.

[0034] The configuration of the diagnostic device 1 according to the second embodiment is similar to the configuration of the diagnostic device 1 shown in Fig. 2. The hardware configuration of the diagnostic device 1 according to the second embodiment is similar to the hardware configuration of the diagnostic device 1 shown in Fig. 3.

[0035] The diagnostic device 1 according to the second embodiment continuously performs the diagnostic process shown in Fig. 6 during operation. The processes of steps S11 to S13 are the same as the processes performed by the diagnostic device 1 according to the first embodiment shown in Fig. 4. The rotation speed acquisition unit 13 included in the diagnostic device 1 obtains a first rotation speed range, which is a range of values ​​that the rotation speed of the shaft 91a can take, from the most recently obtained approximate rotation speed (step S21).

[0036] In detail, the rotation speed acquisition unit 13 determines the first rotation speed range from the estimated rotation speed based on the peak of the spectrum data generated from the vibration sensor signal output over the immediately preceding target period. FIG. 7 shows an example of how the estimated rotation speed changes over time. The horizontal axis of FIG. 7 represents time, and the vertical axis of FIG. 7 represents the estimated rotation speed (unit: rps). As described above, the diagnostic device 1 repeats the diagnostic process shown in FIG. 6. The timings at which the diagnostic device 1 performs the diagnostic process are times T1, T2, and T3. The interval ΔT between time T1 and time T2 and the interval ΔT between time T2 and time T3 are assumed to coincide with the target period.

[0037] At time T1, approximate rotational speeds, which are frequencies corresponding to three peaks with large peak protrusion amounts, are designated F1_1, F2_1, and F3_1. Note that F2_1<F1_1<F3_1 holds. At time T2, approximate rotational speeds, which are frequencies corresponding to three peaks with large peak protrusion amounts, are designated F1_2, F2_2, and F3_2. Note that F2_2<F1_2<F3_2 holds. At time T3, approximate rotational speeds, which are frequencies corresponding to three peaks with large peak protrusion amounts, are designated F1_3, F2_3, and F3_3. Note that F2_3<F1_3<F3_3 holds.

[0038] As described above, the rotation speed acquisition unit 13 calculates the first rotation speed range from the estimated rotation speed based on the peaks of the spectrum data generated from the vibration sensor signal output over the immediately preceding target period. As an example, at time T2, the rotation speed acquisition unit 13 calculates the first rotation speed range from the estimated rotation speeds F1_1, F2_1, and F3_1 calculated at time T1. The first rotation speed range is centered around each of the estimated rotation speeds F1_1, F2_1, and F3_1 and includes multiple ranges of values ​​that the rotation speed of the rotor can take. Specifically, the first rotation speed range includes a range from a lower limit value Fl1 to an upper limit value Fu1, a range from a lower limit value Fl2 to an upper limit value Fu2, and a range from a lower limit value Fl3 to an upper limit value Fl3.

[0039] The lower limit values ​​Fl1, Fl2, Fl3 and the upper limit values ​​Fu1, Fu2, Fu3 are each determined according to the maximum value of the rate of change of the rotation speed of the rotating body. The maximum value of the rate of change of the rotation speed of the rotating body is determined, for example, according to the maximum absolute value of the acceleration / deceleration of the railway vehicle. The maximum absolute value of the acceleration / deceleration of the railway vehicle is the absolute value of the maximum acceleration during powering and the maximum deceleration during service braking. It is assumed that the rotation speed acquisition unit 13 previously stores information about the maximum rate of change of the rotation speed of the rotating body.

[0040] The rotation speed acquisition unit 13 determines whether the estimated rotation speed F1_2 is in a range equal to or greater than a lower limit value Fl1 and equal to or less than an upper limit value Fu1. Similarly, the rotation speed acquisition unit 13 determines whether the estimated rotation speed F2_2 is in a range equal to or greater than a lower limit value Fl2 and equal to or less than an upper limit value Fu2. Similarly, the rotation speed acquisition unit 13 determines whether the estimated rotation speed F3_2 is in a range equal to or greater than a lower limit value Fl3 and equal to or less than an upper limit value Fu3. At time T2, the estimated rotation speeds F1_2, F2_2, and F3_2 are all within the first rotation speed range. Therefore, as shown in FIG. 6 , the rotation speed acquisition unit 13 calculates the rotation speed of the shaft 91a from the estimated rotation speeds F1_2, F2_2, and F3_2 (step S22). As an example, the rotation speed obtaining unit 13 obtains, from among the estimated rotation speeds F1_2, F2_2, and F3_2, the approximate rotation speed at which the peak protrusion amount of the corresponding peak is greatest, as the rotation speed of the shaft 91a.

[0041] At time T3 shown in FIG. 7 , the rotation speed acquisition unit 13 calculates a first rotation speed range from the estimated rotation speeds F1_2, F2_2, and F3_2 calculated at time T2. The first rotation speed range is centered around each of the estimated rotation speeds F1_2, F2_2, and F3_2 and includes multiple ranges of values ​​that the rotation speed of the rotor can take. Specifically, the first rotation speed range includes a range equal to or greater than a lower limit value Fl1′ and equal to or less than an upper limit value Fu1′, a range equal to or greater than a lower limit value Fl2′ and equal to or less than an upper limit value Fu2′, and a range equal to or greater than a lower limit value Fl3′ and equal to or less than an upper limit value Fl3′. The lower limits Fl1′, Fl2′, and Fl3′ and the upper limits Fu1′, Fu2′, and Fu3′ ​​are calculated in the same manner as in the example described above.

[0042] At time T3, the estimated rotation speeds F1_3 and F2_3 are included in the first rotation speed range, but the estimated rotation speed F3_3 is not included in the first rotation speed range. Therefore, at time T3, the rotation speed acquisition unit 13 calculates the rotation speed of the shaft 91a from the estimated rotation speeds F1_3 and F2_3.

[0043] When none of the estimated rotation speeds is included in the first rotation speed range, the rotation speed acquisition unit 13 acquires the estimated rotation speed that is closest to the first rotation speed range as the rotation speed of the shaft 91a.

[0044] The processes in steps S15 and S16 in FIG. 6 are similar to the processes performed by the diagnostic device 1 according to the first embodiment shown in FIG.

[0045] As described above, the diagnostic device 1 according to the second embodiment calculates the rotation speed of the shaft 91a from an estimated rotation speed falling within a first rotation speed range based on an estimated rotation speed corresponding to the immediately preceding target period. By calculating the rotation speed of the shaft 91a from an estimated rotation speed falling within a first rotation speed range corresponding to the maximum value of the rate of change of the rotation speed of the shaft 91a, the calculated rotation speed of the shaft 91a approaches the actual rotation speed. By continuing to perform the process of FIG. 6 , the calculated rotation speed of the shaft 91a further approaches the actual rotation speed. As described above, the diagnostic device 1 according to the second embodiment reduces false detections and is less susceptible to the influence of external vibrations in the process of calculating the rotation speed of the shaft 91a. As a result, the diagnostic device 1 according to the second embodiment can accurately calculate the rotation speed of the shaft 91a. Therefore, the diagnostic device 1 according to the second embodiment can accurately diagnose the condition of the electric motor 91 with a simple configuration.

[0046] (Embodiment 3) The method of determining the rotation speed of the shaft 91a, which is an example of a rotating body of the target device, is not limited to the above example. A diagnostic device that determines the rotation speed of the shaft 91a in a method different from that of Embodiments 1 and 2 will be described in Embodiment 3, focusing on the differences from Embodiment 1.

[0047] The diagnostic device 2 according to the third embodiment shown in FIG. 8 calculates the rotation speed of the shaft 91a from multiple vibration sensor signals corresponding to multiple measurement axes that are perpendicular to the rotation axis AX1 of the shaft 91a and extend in different directions. The first data generator 11 included in the diagnostic device 2 acquires vibration sensor signals from vibration sensors 92 and 93. The vibration sensor 92 is an acceleration sensor that measures static acceleration and dynamic vibration. The vibration sensor 92 is installed so that one of its measurement axes indicates the direction of travel of the moving object on which the target device is mounted, i.e., the railway vehicle. As an example, the vibration sensor 92, which is a single-axis acceleration sensor, is installed so that its measurement axis coincides with the Y-axis. By installing the vibration sensor 92 as described above, the vibration sensor 92 can measure changes in acceleration corresponding to the acceleration and deceleration of the railway vehicle. The vibration sensor 93, which is a single-axis acceleration sensor, is installed so that its measurement axis coincides with the Z-axis.

[0048] In addition to the configuration of the diagnostic device 1, the diagnostic device 2 further includes a second data generator 15 that generates cross-spectral data from spectral data generated from the vibration sensor signals output by the vibration sensors 92 and 93. The hardware configuration of the diagnostic device 2 is similar to the hardware configuration of the diagnostic device 1 according to the first embodiment.

[0049] 9 and 10, which is a view of electric motor 91a located on the negative Y-axis side in FIG. 9 as viewed in the negative X-axis direction, vibration sensors 92 and 93 are provided to measure the magnitude of vibration of electric motor 91. Measurement axis AX2 of vibration sensor 92 and measurement axis AX3 of vibration sensor 93 are orthogonal to rotation axis AX1 of shaft 91a and extend in different directions. As an example, measurement axis AX2 of vibration sensor 92 and measurement axis AX3 of vibration sensor 93 are orthogonal to each other in the YZ plane.

[0050] The diagnostic device 2 having the above configuration continuously performs the diagnostic processing shown in FIG. 11 during operation. The processing of steps S11 to S13 is the same as the processing performed by the diagnostic device 1 according to the first embodiment shown in FIG. 4. In step S11, the first data generation unit 11 generates spectral data for each of the vibration sensors 92 and 93 from the vibration sensor signals output by the vibration sensors 92 and 93. The first data generation unit 11 outputs the spectral data based on the vibration sensor signal output by the vibration sensor 92 to the protrusion amount estimation unit 12, the rotation speed acquisition unit 13, and the diagnostic unit 14. The first data generation unit 11 outputs the spectral data generated for each of the vibration sensors 92 and 93 to the second data generation unit 15.

[0051] The second data generator 15 included in the diagnostic device 2 generates cross-spectral data from the spectral data generated for each of the vibration sensors 92 and 93 (step S31). An example of the phase difference for each frequency obtained from the cross-spectral data is shown in FIG. 12 . The horizontal axis of FIG. 12 represents frequency (unit: Hz), and the vertical axis represents phase difference (unit: degrees). As shown in FIG. 12 , the cross-spectral data is the product of the results of performing an FFT on digital data obtained by sampling the vibration sensor signals output by the vibration sensors 92 and 93. The phase of the cross-spectral data indicates the phase difference for each frequency between the spectral data corresponding to the vibration sensors 92 and 93. The second data generator 15 outputs the cross-spectral data to the rotation speed acquirer 13.

[0052] The rotation number obtaining unit 13 obtains the approximate rotation number in the same manner as in the first embodiment. As shown in FIG. 11, the rotation number obtaining unit 13 obtains the phase difference corresponding to the approximate rotation number based on the cross-spectrum data (step S32). FIG. 12 shows frequencies F1, F2, and F3 corresponding to the approximate rotation number. Peaks corresponding to frequencies F1, F2, and F3 are designated as peaks P1, P2, and P3, respectively. The rotation number obtaining unit 13 obtains phase differences θ1, θ2, and θ3 corresponding to frequencies F1, F2, and F3.

[0053] As shown in FIG. 11 , the rotation speed acquisition unit 13 acquires, as the rotation speed of the shaft 91a, the phase difference calculated in step S32 that falls within a phase range including the angle formed by the measurement axis (step S33). The rotation speed acquisition unit 13 is assumed to previously store information on the angle formed by the measurement axis, i.e., the angle between the measurement axis in the YZ plane, which is equal to or smaller than two right angles. As an example, in FIG. 10 , when the shaft 91a is rotating clockwise as viewed in the negative X-axis direction, the phase difference between the measurement axes AX2 and AX3 is +90°. On the other hand, when the shaft 91a is rotating counterclockwise as viewed in the negative X-axis direction, the phase difference between the measurement axes AX2 and AX3 is −90°. In other words, the phase range is determined based on the angle formed by the rotation direction of the shaft 91a and the measurement axes AX2 and AX3.

[0054] The rotation speed acquisition unit 13 identifies the rotation direction of the shaft 91a and identifies a phase range depending on the rotation direction of the shaft 91a. Specifically, the rotation speed acquisition unit 13 extracts a DC component indicating static acceleration from a vibration sensor signal acquired from a vibration sensor 92 whose measurement axis AX2 coincides with the Y axis indicating the direction of travel of the railway vehicle, and identifies the rotation direction of the shaft 91a from the extracted DC component. For example, the rotation speed acquisition unit 13 includes an LPF (Low Pass Filter). The cutoff frequency of the LPF is set to a frequency sufficiently low enough to extract the DC component from the vibration sensor signal, for example, 1 Hz.

[0055] 10, if the shaft 91a is rotating clockwise when viewed in the negative direction of the X-axis, the rotation speed acquisition unit 13 sets the phase range to a range that includes 90°, for example, a range that is equal to or greater than 85° and equal to or less than 95°.If the shaft 91a is rotating counterclockwise when viewed in the negative direction of the X-axis, the rotation speed acquisition unit 13 sets the phase range to a range that includes −90°, for example, a range that is equal to or greater than −95° and equal to or less than −85°.

[0056] The rotation speed acquisition unit 13 acquires, as the rotation speed of the shaft 91a, the approximate rotation speed for which the corresponding phase difference falls within the phase range. Of the frequencies F1, F2, and F3, only the frequency F1 has a phase difference obtained from the cross-spectral data that falls within the phase range Δθ. The rotation speed acquisition unit 13 acquires the frequency F1 as the rotation speed of the shaft 91a.

[0057] When none of the phase differences corresponding to the estimated rotation speeds are included in the phase range, the rotation speed acquisition unit 13 acquires the estimated rotation speed corresponding to the peak with the largest peak protrusion amount as the rotation speed of the shaft 91a.

[0058] The processes in steps S15 and S16 in FIG. 11 are similar to the processes performed by the diagnostic device 1 according to the first embodiment shown in FIG.

[0059] As described above, the diagnostic device 2 according to the third embodiment determines the rotation speed of the shaft 91a from the approximate rotation speed based on the phase difference between the spectral data of the vibration sensors 92 and 93. By using the phase difference between the spectral data of the vibration sensors 92 and 93, the process of determining the rotation speed of the shaft 91a is less susceptible to false detections and less susceptible to the influence of external vibration disturbances. As a result, the diagnostic device 2 according to the third embodiment can accurately determine the rotation speed of the shaft 91a. Therefore, the diagnostic device 2 according to the third embodiment can accurately diagnose the condition of the electric motor 91 with a simple configuration.

[0060] (Fourth Embodiment) The method of calculating the rotation speed of the shaft 91a from the estimated rotation speed based on the phase difference of the spectral data is not limited to the example of the third embodiment. A diagnostic device 3 that calculates the rotation speed of the shaft 91a from the estimated rotation speed based on the phase difference of the spectral data using a method different from that of the third embodiment will be described in the fourth embodiment. The configuration of the diagnostic device 3 shown in FIG. 13 is the same as that of the diagnostic device 2. The first data generator 11 acquires vibration sensor signals for each measurement axis from the vibration sensor 92 and generates spectral data from each vibration sensor signal. The rotation speed acquirer 13 acquires running information including the direction of travel of the railcar from a train control device that controls the operation of the railcar and identifies the rotation direction of the shaft 91a based on the running information. The hardware configuration of the diagnostic device 3 is the same as that of the diagnostic device 1 according to the first embodiment.

[0061] The vibration sensor 92 is an acceleration sensor, a displacement sensor, a velocity sensor, or the like. The vibration sensor 92 is provided with multiple measurement axes oriented perpendicular to the rotation axis AX1 of the shaft 91a. As an example, the vibration sensor 92, which is a two-axis acceleration sensor, is provided with each measurement axis oriented to coincide with the Y axis and the Z axis. By providing the vibration sensor 92 as described above, the vibration sensor 92 has two measurement axes oriented perpendicular to the rotation axis AX1 of the shaft 91a. The vibration sensor 92 outputs vibration sensor signals for each of the measurement axes parallel to the Y axis and the Z axis.

[0062] The diagnostic device 3 having the above configuration continuously performs the diagnostic processing shown in FIG. 14 during operation. The processing of steps S11 to S13 is the same as the processing performed by the diagnostic device 1 according to the first embodiment shown in FIG. 4. In step S11, the first data generation unit 11 generates spectral data for each measurement axis from the vibration sensor signal output by the vibration sensor 92 for each measurement axis. The first data generation unit 11 outputs spectral data based on the vibration sensor signal corresponding to the measurement axis parallel to the Y axis to the protrusion amount estimation unit 12, the rotation speed acquisition unit 13, and the diagnostic unit 14. The first data generation unit 11 outputs the spectral data generated for each measurement axis to the second data generation unit 15.

[0063] The process of step S31 is the same as the process performed by the diagnostic device 2 according to the third embodiment shown in Fig. 11. In step S31, the second data generator 15 generates cross-spectral data from the spectral data generated for each measurement axis of the vibration sensor 92.

[0064] As shown in FIG. 14 , the rotation speed acquisition unit 13 determines a second rotation speed range indicating the range of rotation speeds of the rotating body corresponding to the phase range based on the cross-spectral data (step S41). An example of the phase difference for each frequency obtained from the cross-spectral data is shown in FIG. 15 . The horizontal axis of FIG. 15 represents frequency (unit: Hz), and the vertical axis represents phase difference (unit: degrees). As shown in FIG. 15 , the cross-spectral data is the product of the results of performing an FFT on digital data obtained by sampling the vibration sensor signal corresponding to each measurement axis of the vibration sensor 92. The phase of the cross-spectral data indicates the phase difference of the spectral data corresponding to each measurement axis of the vibration sensor 92 for each frequency.

[0065] The rotation speed acquisition unit 13 identifies the rotation direction of the shaft 91 a and identifies a phase range according to the rotation direction of the shaft 91 a. Specifically, the rotation speed acquisition unit 13 acquires running information including the direction of travel of the railway vehicle from a train control device that controls the operation of the railway vehicle, and identifies the rotation direction of the shaft 91 a according to the running information. The rotation speed acquisition unit 13 is assumed to previously store information on the angle formed by each measurement axis of the vibration sensor 92, i.e., the angle between the measurement axes in the YZ plane, which is the size of an angle of two right angles or less.

[0066] As in the third embodiment, if the shaft 91a is rotating clockwise when viewed in the negative X-axis direction, the rotation speed acquisition unit 13 sets the phase range to a range that includes 90°, for example, a range that is equal to or greater than 85° and equal to or less than 95°. If the shaft 91a is rotating counterclockwise when viewed in the negative X-axis direction, the rotation speed acquisition unit 13 sets the phase range to a range that includes −90°, for example, a range that is equal to or greater than −95° and equal to or less than −85°.

[0067] The rotation speed acquisition unit 13 determines the frequency range in which the spectral value falls within the phase range Δθ. In the example of Fig. 15, the frequency range corresponding to the phase range Δθ is the range from f1 to f2 inclusive. The frequency range (unit: Hz) from f1 to f2 inclusive corresponds to the approximate rotation speed range (unit: rps) from f1 to f2 inclusive.

[0068] 14, the rotation speed obtaining unit 13 obtains the rotation speed of the shaft 91a from the estimated rotation speeds included in the second rotation speed range obtained in step S41 (step S42). As an example, the rotation speed obtaining unit 13 obtains, as the rotation speed of the shaft 91a, the estimated rotation speed corresponding to the peak with the largest peak protrusion amount included in the second rotation speed range among the estimated rotation speeds.

[0069] When none of the estimated rotation speeds is included in the second rotation speed range, the rotation speed acquisition unit 13 acquires the estimated rotation speed corresponding to the peak with the largest peak protrusion amount as the rotation speed of the shaft 91a.

[0070] The processes in steps S15 and S16 in FIG. 14 are similar to the processes performed by the diagnostic device 1 according to the first embodiment shown in FIG.

[0071] As described above, the diagnostic device 3 according to the fourth embodiment determines the rotation speed of the shaft 91a from the approximate rotation speed based on the phase difference of the spectrum data of each measurement axis of the vibration sensor 92. By using the phase difference of the spectrum data for each measurement axis of the vibration sensor 92, the process of determining the rotation speed of the shaft 91a is less susceptible to false detections and less susceptible to the influence of disturbance vibrations. As a result, the diagnostic device 3 according to the fourth embodiment can accurately determine the rotation speed of the shaft 91a. Therefore, the diagnostic device 3 according to the fourth embodiment can accurately diagnose the condition of the electric motor 91 with a simple configuration.

[0072] The present disclosure is not limited to the above-described exemplary embodiments. The above-described exemplary embodiments can be combined in any manner. As an example, the rotation speed acquisition unit 13 included in the diagnostic device 3 according to the fourth exemplary embodiment may acquire, as the rotation speed of the shaft 91a, an approximate rotation speed that is included in both the first rotation speed range and the second rotation speed range, as in the second exemplary embodiment. As another example, the diagnostic device 2 according to the third exemplary embodiment may acquire vibration sensor signals for each measurement axis from a vibration sensor 92 having multiple measurement axes. As another example, the diagnostic device 3 according to the fourth exemplary embodiment may acquire vibration sensor signals from vibration sensors 92 and 93 whose measurement axes are oriented in different directions.

[0073] The first data generator 11 is not limited to the above example, and may perform any signal processing necessary to generate spectral data. As an example, the first data generator 11 included in the diagnostic device 3 may generate spectral data from a vibration sensor signal passed through a high pass filter (HPF). The cutoff frequency of the HPF is set to a frequency, for example, 1 Hz, that is sufficiently low to separate the DC component from the AC component of the vibration sensor signal. As another example, the data generator 11 may perform envelope processing to detect the envelope of the vibration sensor signal.

[0074] The first data generator 11 may generate spectrum data for each measurement axis from the vibration sensor signals generated for three or more measurement axes.

[0075] The protrusion amount estimation unit 12 may calculate the peak protrusion amount for each peak as described above, or may calculate the peak protrusion amount for peaks detected in a specified frequency range. As an example, the peak protrusion amount estimation unit 12 may calculate the peak protrusion amount for peaks detected in a range equal to or less than a maximum frequency corresponding to an upper limit value of the rotation speed of a rotating body specified in the operating specifications of the target device.

[0076] The method by which the protrusion amount estimating unit 12 calculates the peak protrusion amount is not limited to the above example. As an example, the protrusion amount estimating unit 12 may acquire, for each peak, the difference between the peak value and the average value of the dip values ​​of two dips adjacent to the peak as the peak protrusion amount. In the example of Fig. 5, the protrusion amount estimating unit 12 may acquire, for peak P1, the difference between the peak value of peak P1 and the average value of dips D1 and D1' as the peak protrusion amount ΔP1.

[0077] The method by which the rotation speed acquisition unit 13 calculates the rotation speed of the rotating body is not limited to the above example. As an example, the rotation speed acquisition unit 13 included in the diagnosis device 1 according to the first embodiment may select any number of peaks when calculating the approximate rotation speed. For example, the rotation speed acquisition unit 13 may select 10 peaks in descending order of peak prominence amount, and acquire the frequencies corresponding to each of the selected 10 peaks as the approximate rotation speed.

[0078] As another example, the rotation speed acquisition unit 13 included in the diagnosis device 1 according to the second embodiment may determine the first rotation speed range in any manner as long as the first rotation speed range indicates a range of values ​​that the rotation speed of the rotating body can take. For example, the rotation speed acquisition unit 13 may determine the first rotation speed range in accordance with the maximum and minimum values ​​of the estimated rotation speed acquired based on spectrum data generated from the vibration sensor signal output over the immediately preceding target period, and the maximum value of the rate of change of the rotation speed of the rotating body.

[0079] 16 shows an example of the first rotation speed range. For example, at time T2, the rotation speed acquisition unit 13 calculates the first rotation speed range from the minimum value F2_1 of the estimated rotation speed and the maximum value F3_1 of the estimated rotation speed calculated at time T1. Specifically, the rotation speed acquisition unit 13 calculates a lower limit value Fl from the minimum value F2_1 and the maximum absolute value of the deceleration of the railway vehicle, and calculates an upper limit value Fu from the maximum value F3_1 and the maximum absolute value of the acceleration of the railway vehicle. Similarly, at time T3, the rotation speed acquisition unit 13 calculates the first rotation speed range from the minimum value F2_2 of the estimated rotation speed and the maximum value F3_2 of the estimated rotation speed calculated at time T2.

[0080] As another example, the rotation speed acquisition unit 13 provided in the diagnostic device 1 according to the second embodiment may determine whether the estimated rotation speed is within the first rotation speed range in descending order of peak protrusion amount, and acquire the estimated rotation speed that is first determined to be within the first rotation speed range as the rotation speed of the shaft 91a.

[0081] As another example, when none of the phase differences corresponding to the estimated rotation speeds are included in the phase range, the rotation speed acquisition unit 13 provided in the diagnostic device 2 according to the third embodiment may acquire the approximate rotation speed corresponding to the phase difference that is closest to the phase range as the rotation speed of the shaft 91 a.

[0082] As another example, when none of the estimated rotation speeds fall within the second rotation speed range, the rotation speed acquisition 13 provided in the diagnostic device 3 according to embodiment 4 may acquire the estimated rotation speed closest to the second rotation speed range as the rotation speed of the shaft 91a.

[0083] As another example, the phase range obtained by the rotation speed obtaining unit 13 included in the diagnosis devices 2 and 3 is not limited to the above example and may be any range that includes the angle formed by the measurement axis. For example, the rotation speed obtaining unit 13 may determine the phase range to be a range of ±10° centered on the angle formed by the measurement axis.

[0084] The diagnostic device 1-3 is not limited to diagnosing the condition of the electric motor 91, but can also diagnose the condition of any device having a rotating body, specifically, the wheel 42, the joint 43, the gear device 44, the axle 45, etc. In this case, the vibration sensor 92 is preferably provided near the device to be diagnosed. As an example, when the diagnostic device 1 diagnoses the condition of the gear device 44, the vibration sensor 92 may be provided on the outer surface of the housing of the gear device 44. When the frequencies at which spectrum peaks occur in the event of an abnormality differ for each of the components of the gear device 44, such as the gear wheel, the pinion, the axle bearing in which the gear wheel is fitted, and the shaft bearing in which the pinion gear is fitted, it becomes possible to determine the presence or absence of an abnormality in each component of the gear device 44 from the vibration sensor signal output by the vibration sensor 92 attached to the gear device 44.

[0085] The diagnostic device 1-3 may be mounted on a railway vehicle and implemented as one function of a train information management system that controls the operation of the railway vehicle. The diagnostic device 1-3 is not limited to being mounted on a railway vehicle, but may also be mounted on a moving body other than a railway vehicle, such as an automobile, a trolleybus, an airplane, a ship, etc.

[0086] The structure and installation positions of the vibration sensors 92, 93 are not limited to the above example, and any structure and installation positions may be used as long as they are capable of measuring the magnitude of vibration of the device being diagnosed. As an example, when the device being diagnosed is rotating, the vibration sensor 92, which is a non-contact vibration sensor, is preferably installed in a position close to the device but away from it.

[0087] The angle formed by the measurement axes of vibration sensors 92 and 93 in embodiment 3 and the angle formed by each measurement axis of vibration sensor 92 in embodiment 4 are not limited to 90° and may be any angle. The measurement axis of vibration sensor 92 in embodiment 3 may be offset from the traveling direction of the moving object. In this case, by correcting the vibration sensor signal based on the angle formed between the measurement axis of vibration sensor 92 and the traveling direction of the moving object, the acceleration of the moving object can be obtained from the corrected vibration sensor signal.

[0088] The above hardware configuration and flowchart are merely examples and can be changed or modified as desired. For example, in Figures 11 and 14, the process of step S11 and the process of step S31 may be performed in parallel.

[0089] As another example, a modified example of the hardware configuration of the diagnostic device 1 is shown in Fig. 17. The diagnostic device 1 may be realized by a processing circuit 84 as shown in Fig. 17. The same applies to the diagnostic devices 2 and 3. The processing circuit 84 included in the diagnostic device 1 shown in Fig. 17 is connected to a vibration sensor 92 via an interface circuit 85.

[0090] When the processing circuitry 84 is dedicated hardware, the processing circuitry 84 includes, for example, a single circuit, a composite circuit, a processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination thereof. Each unit of the diagnostic device 1 may be realized by an individual processing circuit 84, or each unit of the diagnostic device 1 may be realized by a common processing circuit 84.

[0091] Some of the functions of the diagnostic device 1 may be realized by dedicated hardware, and other functions may be realized by software or firmware. For example, in the diagnostic device 1, the first data generation unit 11 may be realized by a processing circuit 84 shown in Fig. 17, and the protrusion amount estimation unit 12, the rotation speed acquisition unit 13, and the diagnosis unit 14 may be realized by a processor 81 shown in Fig. 3 reading and executing programs stored in a memory 82.

[0092] The present disclosure allows various embodiments and modifications without departing from the broad spirit and scope of the present disclosure. Furthermore, the above-described embodiments are intended to illustrate the present disclosure and do not limit the scope of the present disclosure. That is, the scope of the present disclosure is defined by the claims, not the embodiments. Various modifications made within the scope of the claims and the meaning of equivalent disclosures are considered to be within the scope of the present disclosure.

[0093] 1, 2, 3 Diagnostic device, 11 First data generation unit, 12 Protrusion amount estimation unit, 13 Rotation speed acquisition unit, 14 Diagnosis unit, 15 Second data generation unit, 41 Bogie, 42 Wheel, 43 Joint, 44 Gear device, 45 Axle, 80 Bus, 81 Processor, 82 Memory, 83 Interface, 84 Processing circuit, 85 Interface circuit, 91 Electric motor, 91a Shaft, 92, 93 Vibration sensor, AX1 Rotation axis, AX2, AX3 Measurement axis.

Claims

1. A diagnostic device for diagnosing the condition of a target device having a rotor that rotates around a rotation axis and is mounted on a mobile object, comprising: a first data generation unit that generates spectral data indicating a distribution of the vibration magnitude of the target device for each frequency from a vibration sensor signal output by a vibration sensor that measures the vibration magnitude of the target device; a protrusion estimation unit that detects peaks and dips in the vibration magnitude of the spectral data and, for each of the peaks, determines a peak protrusion amount from the peak value and the dip value of the dip adjacent to the peak; a rotation speed acquisition unit that selects at least one peak in descending order of peak protrusion amount, acquires the frequency of the at least one selected peak as an approximate rotation speed that is an approximate value of the rotation speed of the rotor, and determines the rotation speed of the rotor from the at least one approximate rotation speed; and a diagnostic unit that uses a diagnosis target frequency that is determined based on the rotation speed of the rotor determined by the rotation speed acquisition unit to detect peaks of the vibration magnitude of the spectral data in a frequency band that includes the diagnosis target frequency, and diagnoses the condition of the target device based on the peak values ​​of the peaks detected in the frequency band that includes the diagnosis target frequency.

2. The diagnostic device according to claim 1, wherein the rotation speed acquisition unit determines a first rotation speed range, which is a range of values ​​that the rotation speed of the rotating body can take, from the most recently determined approximate rotation speed, and determines the rotation speed of the rotating body from the approximate rotation speed included in the first rotation speed range among at least one selected approximate rotation speed.

3. The diagnostic device according to claim 2, wherein the rotation speed acquisition unit acquires, from among the estimated rotation speeds included in the first rotation speed range, the approximate rotation speed that corresponds to the largest peak protrusion amount as the rotation speed of the rotating body.

4. A diagnostic device according to any one of claims 1 to 3, wherein the first data generation unit generates the spectral data for each of a plurality of measurement axes from a plurality of vibration sensor signals corresponding to a plurality of measurement axes that are orthogonal to the rotation axis and extend in different directions from each other, and further comprises a second data generation unit that generates cross-spectral data indicating a phase difference for each frequency of the spectral data corresponding to two of the plurality of measurement axes from a cross-spectrum of the spectral data corresponding to two of the measurement axes, and the rotation speed acquisition unit determines the rotation speed of the rotating body from at least one selected estimated rotation speed based on the phase difference for each frequency obtained from the cross-spectral data and the angle formed by the two measurement axes.

5. The diagnostic device according to claim 4, wherein the first data generation unit generates the spectral data for each of the vibration sensors from the vibration sensor signals output by the plurality of vibration sensors, each of which is arranged with its measurement axis perpendicular to the rotation axis and extending in different directions.

6. The diagnostic device according to claim 4, wherein the first data generation unit generates the spectrum data for each measurement axis from the vibration sensor signal output by the vibration sensor having multiple measurement axes for each measurement axis.

7. A diagnostic device according to any one of claims 4 to 6, wherein the rotation speed acquisition unit determines a phase difference corresponding to each of the estimated rotation speeds based on the cross-spectral data, and determines the rotation speed of the rotating body from the estimated rotation speed that includes a phase difference corresponding to a phase range including the angle formed by the two measurement axes from among at least one selected estimated rotation speed.

8. A diagnostic device according to any one of claims 4 to 6, wherein the rotation speed acquisition unit determines a second rotation speed range indicating a range of rotation speeds of the rotating body corresponding to a phase range including the angle formed by the two measurement axes based on the cross-spectral data, and determines the rotation speed of the rotating body from the estimated rotation speed included in the second rotation speed range among at least one selected estimated rotation speed.

9. A diagnostic device as described in claim 7 or 8, wherein the data generation unit generates the spectrum data from the vibration sensor signal output by the vibration sensor, which is an acceleration sensor that measures static acceleration and dynamic vibration, and the rotation speed acquisition unit identifies the rotation direction of the rotating body from the static acceleration indicated by the vibration sensor signal, and identifies the phase range depending on the rotation direction of the rotating body.

10. A diagnostic device as described in claim 7 or 8, wherein the rotation speed acquisition unit acquires driving information including the direction of travel of the moving body from a control device that controls the operation of the moving body, identifies the rotation direction of the rotating body according to the driving information, and identifies the phase range according to the rotation direction of the rotating body.

11. A diagnostic device according to any one of claims 1 to 10, wherein the protrusion amount estimation unit detects peaks and dips in the spectral data, and for each of the peaks, obtains as the peak protrusion amount a value obtained by linearly combining the differences between the peak value and each of the dip values ​​of two dips located adjacent to the peak using coefficients that are each greater than or equal to 0 and less than or equal to 1 and that add up to 1.

12. A diagnostic device according to any one of claims 1 to 10, wherein the protrusion amount estimation unit detects peaks and dips in the spectral data, and for each peak, obtains the peak protrusion amount as the difference between the peak value and the average value of the dip values ​​of two dips located adjacent to the peak.

13. A diagnostic device as described in any one of claims 1 to 12, wherein the first data generation unit generates the spectrum data from the vibration sensor signal output by the vibration sensor that is mounted on a railway vehicle and measures the magnitude of vibration of the target equipment, which is an electric motor that generates the propulsive force of the railway vehicle or a rotating mechanism that rotates by receiving rotational force from the electric motor.

14. A diagnostic method performed by a diagnostic device that has a rotor that rotates around an axis of rotation and that diagnoses the condition of target equipment mounted on a moving object, the diagnostic method comprising: generating spectral data indicating the distribution of the vibration magnitude of the target equipment for each frequency from a vibration sensor signal output by a vibration sensor that measures the vibration magnitude of the target equipment; detecting peaks and dips in the vibration magnitude of the spectral data; determining the peak prominence for each of the peaks from the peak value and the dip value of the dip adjacent to the peak; selecting at least one peak in descending order of peak prominence; obtaining the frequency of the at least one selected peak as an approximate rotation speed that is an approximation of the rotation speed of the rotor; determining the rotation speed of the rotor from the at least one approximate rotation speed; using a diagnosis target frequency determined based on the determined rotation speed of the rotor, detecting peaks in the vibration magnitude of the spectral data in a frequency band that includes the diagnosis target frequency; and diagnosing the condition of the target equipment based on the peak values ​​of the peaks detected in the frequency band that includes the diagnosis target frequency.

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