A method and apparatus for diagnosing a fault of a rotating electromechanical device

Abstract

A fault diagnosis method and device of a rotating electromechanical device, the method comprising: acquiring a vibration signal collected by a vibration sensor of the rotating electromechanical device; acquiring a rotation signal collected by a rotation sensor of the rotating electromechanical device; determining a time sequence according to the rotation signal, wherein the time sequence comprises a plurality of time instants, and the rotation angle of the rotating electromechanical device between any two adjacent time instants is the same; resampling the vibration signal according to the time sequence to obtain a plurality of sampling signals; and determining a fault diagnosis result of the rotating electromechanical device according to the plurality of sampling signals. The above method can achieve accurate equiangular sampling under high speed and variable working conditions, and improve the accuracy of fault diagnosis of the rotating electromechanical device.

Description

Technical Field

[0001] This application relates to the field of motor fault diagnosis, and more particularly to a fault diagnosis method and apparatus for rotating electromechanical equipment. Background Technology

[0002] Rotating electromechanical equipment is widely used in various fields of production and daily life. Real-time health monitoring of rotating electromechanical equipment is a necessary step to ensure the normal operation of production.

[0003] During operation, the various components of rotating machinery generate dynamic excitation forces that act on the machinery's casing, causing vibrations. Therefore, the health information of these components is implicit in the vibration signals, and fault diagnosis can be achieved by analyzing these signals. However, existing fault diagnosis algorithms (e.g., order analysis methods) suffer from low diagnostic accuracy under varying operating conditions. These varying operating conditions can specifically refer to drastic changes in rotational speed. Summary of the Invention

[0004] This application provides a fault diagnosis method and apparatus for rotating electromechanical equipment, which addresses the problem that existing fault diagnosis algorithms have low accuracy under varying operating conditions.

[0005] In a first aspect, this application provides a fault diagnosis method for rotating electromechanical equipment. The method is applied to scenarios where the rotating electromechanical equipment rotates at a non-uniform speed. The method includes: acquiring vibration signals collected by vibration sensors of the rotating electromechanical equipment; acquiring rotation signals collected by rotation sensors of the rotating electromechanical equipment; determining a time series based on the rotation signals, wherein the time series includes multiple moments, and the rotation angle of the rotating electromechanical equipment is the same between any two adjacent moments; resampling the vibration signals based on the time series to obtain multiple sampled signals; and determining a fault diagnosis result for the rotating electromechanical equipment based on the multiple sampled signals.

[0006] The above method can achieve accurate equal-angle sampling under high speed and variable operating conditions, thereby improving the accuracy of fault diagnosis of rotating electromechanical equipment.

[0007] In one possible design, when determining the time series based on the rotation signal, a discrete signal corresponding to the rotation process of the rotating electromechanical equipment is determined based on the rotation signal; wherein the discrete signal includes at least one of angular velocity signal, angular acceleration signal, or angle signal; and the time series is determined based on the discrete signal.

[0008] Using the above design, time series can be obtained from discrete signals.

[0009] In one possible design, when determining the time series based on the discrete signal, the rotation angle of the rotating electromechanical equipment is determined based on the discrete signal; and the time series is determined based on the rotation angle of the rotating electromechanical equipment.

[0010] In one possible design, when determining the discrete signal based on the rotation signal, the discrete signal is determined using a Luneburger observer based on the rotation signal.

[0011] In one possible design, when determining the time series based on the discrete signal, the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment is determined based on the discrete signal; the time series is determined based on the instantaneous angle curve and a preset angle.

[0012] In one possible design, when determining the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment based on the discrete signal, if the discrete signal includes an angular velocity signal, the angular velocity signal is curve-fitted to obtain an instantaneous angular velocity curve, and the instantaneous angle curve is obtained by integrating the instantaneous angular velocity curve.

[0013] In one possible design, when determining the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment based on the discrete signal, if the discrete signal includes an angular velocity signal and an angle signal, the angle signal is curve-fitted, and the instantaneous angle curve is determined based on the slope constraint of the angular velocity signal.

[0014] In one possible design, when determining the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment based on the discrete signal, if the discrete signal includes an angular velocity signal, an angular acceleration signal, and an angle signal, the instantaneous angle curve is obtained by interpolation fitting based on the angular velocity signal, the angle signal, and the angular acceleration signal.

[0015] In one possible design, the rotation sensor is a rotary transformer or an coded disk.

[0016] In one possible design, the method is applied to scenarios where the rotating electromechanical equipment rotates at a non-uniform speed.

[0017] In one possible design, when determining the fault diagnosis result of the rotating electromechanical equipment based on the plurality of sampled signals, the plurality of sampled signals are used as input signals of a preset fault diagnosis algorithm to obtain the output result of the preset fault diagnosis algorithm, and the output result is used as the fault diagnosis result of the rotating electromechanical equipment.

[0018] Secondly, this application provides a fault diagnosis device for rotating electromechanical equipment. The device is applied to scenarios where the rotating electromechanical equipment rotates at a non-uniform speed. The device includes: a communication module and a processing module; the communication module is used to acquire vibration signals collected by vibration sensors of the rotating electromechanical equipment; and to acquire rotation signals collected by rotation sensors of the rotating electromechanical equipment; the processing module is used to determine a time series based on the rotation signals, wherein the time series includes multiple moments, and the rotation angle of the rotating electromechanical equipment is the same between any two adjacent moments; to resample the vibration signals based on the time series to obtain multiple sampled signals; and to determine the fault diagnosis result of the rotating electromechanical equipment based on the multiple sampled signals.

[0019] In one possible design, the processing module is configured to determine a discrete signal corresponding to the rotation process of the rotating electromechanical equipment based on the rotation signal when determining the time series based on the rotation signal; wherein the discrete signal includes at least one of angular velocity signal, angular acceleration signal, or angle signal; and determine the time series based on the discrete signal.

[0020] In one possible design, the processing module is configured to determine the rotation angle of the rotating electromechanical equipment based on the discrete signal when determining the time series based on the discrete signal; and to determine the time series based on the rotation angle of the rotating electromechanical equipment.

[0021] In one possible design, the processing module is configured to determine the discrete signal using a Luneburger observer based on the rotation signal when determining the discrete signal based on the rotation signal.

[0022] In one possible design, the processing module is configured to, when determining the time series based on the discrete signal, determine the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment based on the discrete signal; and determine the time series based on the instantaneous angle curve and a preset angle.

[0023] In one possible design, the processing module is used to, when determining the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment based on the discrete signal, if the discrete signal includes an angular velocity signal, perform curve fitting on the angular velocity signal to obtain an instantaneous angular velocity curve, and integrate the instantaneous angular velocity curve to obtain the instantaneous angle curve;

[0024] In one possible design, the processing module is used to determine the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment based on the discrete signal, if the discrete signal includes an angular velocity signal and an angle signal, to perform curve fitting on the angle signal, and to determine the instantaneous angle curve based on the slope constraint of the angular velocity signal.

[0025] In one possible design, the processing module is configured to, when determining the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment based on the discrete signal, if the discrete signal includes an angular velocity signal, an angular acceleration signal, and an angle signal, perform interpolation fitting based on the angular velocity signal, the angle signal, and the angular acceleration signal to obtain the instantaneous angle curve.

[0026] In one possible design, the rotation sensor is a rotary transformer or an coded disk.

[0027] In one possible design, the device is applied to scenarios where the rotating electromechanical equipment rotates at a non-uniform speed.

[0028] In one possible design, the processing module is configured to, when determining the fault diagnosis result of the rotating electromechanical equipment based on the plurality of sampled signals, use the plurality of sampled signals as input signals of a preset fault diagnosis algorithm, obtain the output result of the preset fault diagnosis algorithm, and use the output result as the fault diagnosis result of the rotating electromechanical equipment.

[0029] The technical effects of the second aspect mentioned above can be referenced from the corresponding technical effects of the first aspect.

[0030] Thirdly, this application also provides an apparatus. This apparatus can perform the above-described method design. The apparatus may be a chip or circuit capable of performing the functions corresponding to the above-described method, or a device including the chip or circuit.

[0031] In one possible implementation, the device includes: a memory for storing computer-executable program code; and a processor coupled to the memory. The program code stored in the memory includes instructions that, when executed by the processor, cause the device or a device equipped with the device to perform any of the methods described above.

[0032] The device may also include a communication interface, which may be a transceiver, or, if the device is a chip or circuit, the communication interface may be the chip's input / output interface, such as input / output pins.

[0033] In one possible design, the device includes corresponding functional units, each used to implement the steps in the above method. The functions can be implemented in hardware or by hardware executing corresponding software. The hardware or software includes one or more units corresponding to the functions described above.

[0034] Fourthly, this application provides a fault diagnosis device for rotating electromechanical equipment, the device comprising: a communication module and a processing module; the communication module being used to communicate with a vibration sensor of the rotating electromechanical equipment and acquire vibration signals from the vibration sensor; the communication module being used to communicate with a rotation sensor of the rotating electromechanical equipment and acquire rotation signals from the rotation sensor; the processing module being used to execute the first aspect and various possible designs of the first aspect.

[0035] Fifthly, this application provides a rotating electromechanical device, which includes a vibration sensor, a rotation sensor, and a fault diagnosis device as described in the fourth aspect.

[0036] In a sixth aspect, this application provides an electric vehicle, including a vehicle body, wheels, and rotating electromechanical equipment as described in the fifth aspect.

[0037] In a seventh aspect, this application provides a fault diagnosis device for rotating electromechanical equipment, including a processor coupled to a memory: the processor is configured to execute computer instructions stored in the memory to cause the fault diagnosis device to perform as described in the first aspect and various possible designs of the first aspect.

[0038] Eighthly, this application provides a computer-readable storage medium storing a computer program that, when run on a device, performs the method described in any of the possible designs above.

[0039] Ninthly, this application provides a computer program product comprising a computer program that, when run on a device, executes the method in any of the above possible designs. Attached Figure Description

[0040] Figure 1 This is a flowchart outlining the fault diagnosis method for rotating electromechanical equipment in this application;

[0041] Figure 2 This is a schematic diagram of the angle signal in the discrete signal in this application;

[0042] Figure 3 This is a schematic diagram of the instantaneous angle curve in this application;

[0043] Figure 4 This is a schematic diagram of the instantaneous angle curve determined by the objective function in this application;

[0044] Figure 5 This is a schematic diagram of the fault diagnosis process in this application;

[0045] Figure 6This is a schematic diagram of resampling vibration signals when the noise-free angular velocity is 0 in this application;

[0046] Figure 7A This is a schematic diagram of the Gaussian noise signal in this application;

[0047] Figure 7B This is a schematic diagram of the superposition of Gaussian noise signal and vibration signal in this application;

[0048] Figure 8 This is a schematic diagram of the vibration signal resampled based on the assumption that the angular velocity remains constant, including Gaussian noise signal, in this application.

[0049] Figure 9A This is a schematic diagram illustrating the resampling of vibration signals, including Gaussian noise signals, in an acceleration scenario based on the assumption that the angular velocity remains constant, as described in this application.

[0050] Figure 9B This is a schematic diagram illustrating the resampling of vibration signals, including Gaussian noise signals, in an accelerated scenario based on the time requirements provided in this application.

[0051] Figure 10A This is a schematic diagram illustrating the resampling of vibration signals, including Gaussian noise signals, in a deceleration scenario based on the assumption that the angular velocity remains constant, as described in this application.

[0052] Figure 10B This is a schematic diagram illustrating the resampling of vibration signals, including Gaussian noise signals, in a deceleration scenario based on the time requirements provided in this application.

[0053] Figure 11 This is a schematic diagram of the structure of one of the devices in this application;

[0054] Figure 12 This is a schematic diagram of another device in this application. Detailed Implementation

[0055] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. The terms "first," "second," and corresponding reference numerals in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate; this is merely a way of distinguishing objects with the same attributes in the embodiments of this application. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, so that a process, method, system, product, or apparatus that comprises a series of units is not necessarily limited to those units, but may include other units not explicitly listed or inherent to these processes, methods, products, or apparatuses.

[0056] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in this application is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Furthermore, in the description of this application, "at least one" refers to one or more items, and "multiple" refers to two or more items. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or multiple items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0057] The rotary electromechanical equipment in the embodiments of this application can be applied to variable speed machine tools, precision machine tools, hybrid electric vehicles or electric vehicles, etc., and this application does not limit it.

[0058] Currently, in order to diagnose faults in rotating electromechanical equipment, it is necessary to first obtain the vibration signals collected by the vibration sensors and the rotation signals collected by the rotation sensors of the rotating electromechanical equipment. Then, the angle signals of the rotating electromechanical equipment can be determined based on the rotation signals.

[0059] Furthermore, based on the assumption that the angular velocity of the rotating electromechanical equipment remains constant during one revolution, the time series for equal-angle sampling is solved.

[0060] Finally, the vibration signal is resampled based on the time series used for equal-angle sampling, and the resampled signal is used as the input signal of the preset fault diagnosis algorithm to obtain the fault diagnosis result output by the preset fault diagnosis algorithm.

[0061] For example, the following scheme is used to solve time series samples with equal angles.

[0062] Assuming the initial angle is θ0, the initial timestamp is t0, the angle signal is θ(k), the timestamp corresponding to the angle signal is t(k), and the set mechanical angle is θe, that is, the time series used for equal angle sampling is the time series sampled for each θe.

[0063] If θ(k)-θ0≥θe, update t e .

[0064]

[0065] The time series used for isoangular sampling is:

[0066]

[0067] Where n = 0, 1, 2, ..., M × O⁻¹. O is the order of the vibration signal order analysis, and M is the sampling coefficient, which needs to satisfy the Nyquist sampling law >= 2, and is generally taken as 3 to 4 in engineering.

[0068] For example, in scenarios where rotating machinery is rotating at a constant speed, or assuming the angular velocity of the rotating machinery remains constant, the time required to rotate 10 degrees is the same, for example, denoted as t. x Assuming the start time (or initial timestamp) of the rotation signal acquisition is t0, then the time series used for equal-angle sampling (i.e., the time series sampled every 10 degrees) is t0 + t x ,t0+2t x ..., t0+36t x Among them, 360 / 10 = 36, the time series used for equal angle sampling includes a total of 36 values.

[0069] However, in the above method, when the rotating machinery is rotating at a non-uniform speed, the time series obtained using the above assumption for isotropic sampling is not a true isotropic sampling time series. For example, in the scenario where the rotating machinery is rotating at a non-uniform speed, the time required for each 10-degree rotation is not the same. Therefore, the diagnostic accuracy of the fault diagnosis results output by the preset fault diagnosis algorithm is not high.

[0070] Based on this, this application provides a fault diagnosis method for rotating electromechanical equipment to solve the problem that existing fault diagnosis algorithms have low diagnostic accuracy under varying operating conditions.

[0071] The device executing the following method can be a fault diagnosis device for rotating machinery and equipment, such as a chip, chip system, or processor. The device includes a communication module through which it can communicate with the rotating machinery and equipment to obtain the vibration signal in S101 and the rotation signal in S102. This device can be deployed in the end-side microcontroller (MCU) of the rotating machinery and equipment or in the cloud; this application does not limit its deployment in this regard.

[0072] like Figure 1 As shown, the method includes:

[0073] S101. Obtain the vibration signal collected by the vibration sensor of the rotating electromechanical equipment.

[0074] For example, the vibration sensor may acquire vibration signals at a first frequency and transmit them to a fault diagnosis device.

[0075] S102. Obtain the rotation signal from the vibration sensor of the rotating electromechanical equipment.

[0076] For example, the rotation sensor can acquire rotation signals at a second frequency and transmit them to the fault diagnosis device. The first frequency is higher than the second frequency. Both the first and second frequencies are preset frequencies; the first frequency can be determined based on the configuration of the vibration sensor, and the second frequency can be determined based on the configuration of the rotation sensor.

[0077] The rotary sensor can be a rotary transformer or an encoder disk, etc., and this application does not limit it.

[0078] It should be noted that this application does not limit whether the vibration sensor and the rotation sensor start signal acquisition simultaneously or whether they end signal acquisition simultaneously. However, the method provided in this application is used to determine the fault diagnosis result of rotating electromechanical equipment based on the vibration signal and rotation signal acquired when the vibration sensor and the rotation sensor are both in operation.

[0079] S103. Determine the time series based on the rotation signal, wherein the time series includes multiple moments, and the rotation angle of the rotating electromechanical equipment is the same between any two adjacent moments.

[0080] The preset angle can also be referred to as the interval for equal-angle sampling. For example, the number of rotations of the rotating electromechanical equipment corresponding to the time series can also be set. For instance, a time series can be determined every K rotations of the rotating electromechanical equipment, where K is a preset positive integer.

[0081] For example, when determining a time series based on a rotation signal, a discrete signal corresponding to the rotation process of a rotating electromechanical device can be determined based on the rotation signal, and further, a time series can be determined based on the discrete signal.

[0082] For example, a discrete signal can be determined using a Luneburger observer based on a rotating signal. Other methods can also be used to determine discrete signals, and this application does not limit the scope of these methods.

[0083] For example, the discrete signal may include at least one of an angular velocity signal, an angular acceleration signal, or an angle signal. For instance, the angular velocity signal includes the angular velocity at several moments during the rotation of the rotating machinery. The angular acceleration signal includes the angular acceleration at several moments during the rotation of the rotating machinery. The angle signal includes the angle at several moments during the rotation of the rotating machinery.

[0084] like Figure 2 As shown, during the rotation of the rotating electromechanical equipment, the rotation sensor can collect an angular velocity value every preset time interval T, for a total of k angular velocity values. That is, the angular velocity signal includes k angular velocity values, for example, w(1), w(2), w(3), ..., w(k). Where k is a positive integer, w(1) represents the angular velocity at time T, w(2) represents the angular velocity at time 2T, ..., w(k) represents the angular velocity at time kT, where T is the preset time interval, which can be determined by the second frequency.

[0085] In one possible implementation, when determining the time series based on the discrete signal, the rotation angle of the rotating electromechanical equipment is determined based on the discrete signal, and the time series is determined based on the rotation angle of the rotating electromechanical equipment.

[0086] In one possible implementation, when determining the time series based on the discrete signal, the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment can be determined first based on the discrete signal, and then the time series can be determined based on the instantaneous angle curve and the preset angle.

[0087] The instantaneous angle curve can be understood as a continuous signal of the angle during the rotation of the rotating electromechanical equipment.

[0088] like Figure 3 The figure shows an instantaneous angle curve, where the y-axis represents angle θ and the x-axis represents time t, based on a preset angle θ. e 360 / θ can be determined from 0 degrees to 360 degrees. e These angle values ​​can be determined from the instantaneous angle curve. e The time series is obtained by taking the time corresponding to each angle value.

[0089] Assuming a preset angle θe =18 degrees, then a total of 20 angle values ​​can be determined for one revolution of the rotating electromechanical equipment. Among them, 360 / 18 = 20, specifically including: 0 degrees, 18 degrees, 36 degrees, 54 degrees, 72 degrees, ..., 342 degrees, 360 degrees. Based on the above 20 angle values ​​and instantaneous angle curves, the time corresponding to each of the 20 angle values ​​is determined as a time series.

[0090] Similarly, assuming a preset angle θ e =18 degrees, then the rotating electromechanical equipment can be determined to have 40 angle values ​​in two rotations, of which 720 / 18 = 40. Based on the above 40 angle values ​​and instantaneous angle curves, the time corresponding to each of the 40 angle values ​​is determined as a time series.

[0091] The time series here can also be called a time series for isoangular sampling, or isoangular time series, etc., and this application does not limit it.

[0092] Among them, determining the instantaneous angle curve corresponding to the rotation process of rotating electromechanical equipment based on discrete signals can be achieved using, but is not limited to, the following methods:

[0093] Method 1: If the discrete signal includes an angular velocity signal, the angular velocity signal can be curve-fitted to obtain an instantaneous angular velocity curve, and the instantaneous angle curve can be obtained by integrating the instantaneous angular velocity curve.

[0094] Specifically, since the angular velocity signal includes multiple angular velocity values, an instantaneous angular velocity curve can be obtained by fitting it using existing tools. Then, integrating the instantaneous angular velocity curve yields the instantaneous angle curve. For example, through... Figure 2 Can obtain Figure 3 .

[0095] Method 2: If the discrete signal includes angular velocity signal and angle signal, perform curve fitting on the angle signal, and determine the instantaneous angle signal based on the slope constraint of the angular velocity signal.

[0096] Specifically, since the angle signal includes multiple angle values, the instantaneous angle curve can be obtained by fitting with existing tools. Furthermore, the angular velocity signal is used as a slope constraint to adjust the instantaneous angle curve, thereby obtaining the final instantaneous velocity curve.

[0097] For example, the objective function is:

[0098] J=∑(θ * (k)-θ(k)) 2 +q(w * (k)-w(k)) 2

[0099] Where, θ *(k) represents the angle at time kT determined based on the instantaneous angle curve, θ(k) represents the angle at time kT, determined by the angle signal in the discrete signal, w * (k) represents the slope at time kT determined based on the instantaneous angle curve, and w(k) represents the angular velocity at time kT, determined by the angular velocity signal in the discrete signal. q is the weighting factor for the slope. The instantaneous angle curve corresponding to the minimum value of J is the final determined instantaneous angle curve.

[0100] refer to Figure 4 As shown, the smaller the difference between the derivative of the instantaneous angle curve at 2T and w(2), the better. Similarly, the smaller the difference between the derivative of the instantaneous angle curve at 5T and w(5), the better.

[0101] Method 3: If the discrete signal includes angular velocity signal, angle signal and angular acceleration signal, interpolation fitting is performed based on the angular velocity signal, angle signal and angular acceleration signal to obtain the instantaneous angle curve.

[0102] For example, the fitting method here can be linear fitting, Lagrange fitting, and sinc fitting, etc.

[0103] Taking linear fitting as an example:

[0104] θ(t)=θ(k-1)+w(k-1)×(t-(k-1))+0.5a(k-1)×(t-(k-1)) 2

[0105] Among them, (k-1)T s ≤t≤kT s Among them, T s It can be determined by the second frequency.

[0106] Method 4: If the discrete signal includes an angular velocity signal, take the average of two adjacent angular velocities as the angular velocity within the time period corresponding to these two angular velocities, fit the instantaneous angular velocity curve, and further integrate the instantaneous angular velocity curve to obtain the instantaneous angle curve.

[0107] Method 5: If the discrete signal includes an angular velocity signal, take the angular velocity closest to the preset angle value in the angles corresponding to two adjacent angular velocities as the angular velocity in the time period corresponding to these two angular velocities, fit the instantaneous angular velocity curve, and further integrate the instantaneous angular velocity curve to obtain the instantaneous angle curve.

[0108] For example, the angles corresponding to each pair of adjacent angular velocities are determined based on these two adjacent angular velocities. Further, the angular velocity closest to a preset angle value among the angles corresponding to these two adjacent angular velocities is selected as the angular velocity within the time period corresponding to these two angular velocities. If the difference between the angles corresponding to these two adjacent angular velocities and the preset angle value is equal, then any one of the angular velocities can be selected as the angular velocity within the time period corresponding to these two angular velocities.

[0109] For example, suppose the interval for equal-angle sampling is θ. e =18 degrees, then the rotating electromechanical equipment can be determined to have 20 angle values ​​for one revolution, of which 360 / 18 = 20, specifically including: 0 degrees, 18 degrees, 36 degrees, 54 degrees, 72 degrees, ..., 342 degrees, 360 degrees. These 20 angle values ​​are all preset angle values.

[0110] For any pair of two adjacent angular velocities, assuming that the angles corresponding to these two adjacent angular velocities are 33 degrees and 45 degrees respectively, it can be seen from the above preset angle value that 33 degrees and 45 degrees are closer to the preset angle value of 36 degrees. Among them, 33 degrees is closer to 36 degrees. Therefore, the angular velocity corresponding to 33 degrees is selected as the angular velocity in the time period corresponding to these two angular velocities.

[0111] It is understood that the above method for determining the instantaneous angle curve is merely an example and is not intended to limit this application.

[0112] S104. Resample the vibration signal according to the time series to obtain multiple sampled signals.

[0113] For example, the vibration signal is interpolated and resampled based on the time series, which is equivalent to equal-angle sampling, to obtain equal-angle sampling results, i.e., multiple sampled signals. Therefore, the sampled signals here can also be called equal-angle sampling points.

[0114] For example, if the time series includes the time corresponding to each 18-degree rotation, then the time series of one rotation of the rotating electromechanical equipment includes 20 time points, where 360 / 18 = 20. Further, based on this time series, the vibration signal obtained by S101 is collected, and the signals corresponding to these 20 time points are obtained, i.e., 20 sampled signals are acquired. It is understood that if the vibration signal is missing signals corresponding to one or more of these 20 time points, existing signal fitting methods can be applied to obtain the signals corresponding to those time points; this will not be elaborated upon here.

[0115] For example, a time series can be defined every K revolutions of the rotating machinery, where K is a positive integer. The vibration signal can then be resampled based on this time series to obtain multiple sampled signals. It can be understood that the vibration signal at this point is the vibration signal within the time period corresponding to the K revolutions of the rotating machinery.

[0116] For example, if the rotating electromechanical equipment rotates K revolutions from time A to time B, a time series is determined based on the rotation signal collected by the rotation sensor from time A to time B (the specific process can be referred to in S103 above). The vibration signal is then resampled based on this time series to obtain multiple sampled signals. The vibration signal at this point is the vibration signal collected by the vibration sensor from time A to time B. The number of these multiple sampled signals is 360K / θ. e θ e This is a preset angle.

[0117] S105. Determine the fault diagnosis results of rotating electromechanical equipment based on multiple sampled signals.

[0118] For example, multiple sampled signals are used as input signals to a preset fault diagnosis algorithm to obtain the output result of the preset fault diagnosis algorithm, and the output result is used as the fault diagnosis result of the preset fault diagnosis algorithm.

[0119] It is understood that the preset fault diagnosis algorithm here can be an existing fault diagnosis model or a fault diagnosis analysis process, etc., and this application does not limit it.

[0120] For example, multiple sampled signals can be processed using signal processing methods such as Fourier transform to obtain an order ratio spectrum. Furthermore, order analysis can be performed based on the order ratio spectrum to obtain fault diagnosis results, such as... Figure 5 As shown. In addition, multiple sampled signals can also be used as input signals for other fault diagnosis algorithms (e.g., artificial intelligence algorithms for fault diagnosis, etc.), which is not limited in this application.

[0121] The above method can achieve accurate equal-angle sampling under high speed and variable operating conditions, thereby improving the accuracy of fault diagnosis of rotating electromechanical equipment.

[0122] The above method will be explained below with reference to specific simulation results:

[0123] The simulation object is a vehicle rotary motor. The rotation sensor is a rotary transformer. The rotation sensor acquires the rotation signal at a frequency of 10 kHz (i.e., the second frequency), and the vibration sensor acquires the vibration signal at a frequency of 50 kHz (i.e., the first frequency). The maximum amplitude of the vibration sensor is set to 10.

[0124] Figure 6The results are obtained by sampling at equal angles in a scenario where Gaussian white noise is not present and the vehicle's rotary motor rotates at a constant speed.

[0125] The sine wave is a schematic diagram of the vibration signal of an automotive rotary motor. One sine wave represents one complete rotation of the motor, from 0 degrees to 360 degrees. The black dots represent isotropic sampling points. Therefore, 360 / 18 = 20 isotropic sampling points are needed in one rotation cycle (i.e., within one complete rotation of the motor), forming 20 sampled signals. Multiple isotropic lines can be determined based on these sampling points, such as... Figure 6 As shown by the horizontal line in the image.

[0126] like Figure 6 As shown, assuming θ e =18 degrees. Since the angular velocity of the automotive rotary motor is constant, the time taken for the automotive rotary motor to rotate from 0 degrees to 18 degrees, from 18 degrees to 36 degrees, from 36 degrees to 54 degrees, and so on, and from 342 degrees to 360 degrees, is all the same. In other words, the time taken for the automotive rotary motor to rotate the preset angle is always the same.

[0127] Figure 7A This is a schematic diagram of a Gaussian noise signal. Figure 7B This is a schematic diagram of the superposition of vibration and Gaussian noise signals in a scenario where angular acceleration is 0 (angular velocity remains constant). The maximum amplitude of the vibration sensor is set to 10, and the amplitude of the Gaussian white noise is set to (0.2, 0.12). Here, 0.2 represents the mean, and 0.12 represents the variance.

[0128] In a scenario where angular acceleration is 0 (angular velocity remains constant), based on the assumption that the angular velocity of the rotating electromechanical equipment remains constant during rotation, a time series for equal-angle sampling is solved, and the vibration signal (e.g., ...) is analyzed according to this time series. Figure 7B As shown, resampling is performed to obtain multiple sampled signals. In this case, none of the sampled signals lie at the intersection of the isoangular line and the vibration signal. Multiple sampled signals are as follows: Figure 8 As shown by the black dots, it can be seen that the introduction of Gaussian noise signal leads to an increase in the error of equal-angle sampling. For example, the root mean square error reaches 0.20558906.

[0129] In scenarios where angular acceleration is not zero, assuming the angular velocity varies from 3000 to 12000 rpm (i.e., frequency 50 to 200 Hz, angular velocity variation range 100 pi to 400 pi rad / s) within 0.3 s, the angular acceleration is 1000 pi rad / s. 2Furthermore, the angular acceleration is variable. Here, rpm is an abbreviation for revolutions per minute, representing the number of rotations per minute of the vehicle's rotary motor. For example... Figure 9A As shown, the sinusoidal signal changes from sparse to dense. If, based on the assumption that the angular velocity of the rotating electromechanical equipment remains constant during rotation, the time series used for equal-angle sampling is solved, and the vibration information is resampled according to this time series to obtain multiple sampled signals. At this point, the sampled signals are not located at the intersection of the equal-angle lines and the vibration signal. Figure 9A The degree to which each sampled signal deviates from the isotropic line is compared to Figure 8 The deviation of each sampled signal from the isoangular line is greater, and the isoangular sampling error is further increased; for example, the root mean square error reaches 0.82490429.

[0130] And adopting such Figure 1 The method shown obtains a time series, and the vibration signal is resampled based on the time series to obtain multiple sampled signals. This improves the accuracy of equal-angle sampling. Figure 9B As shown, for example, the root mean square error was reduced to 0.25859148.

[0131] For example, assuming θ e =18 degrees. Because the angular velocity of the automotive rotary motor is not constant, the time taken for the motor to rotate from 0 degrees to 18 degrees, from 18 degrees to 36 degrees, from 36 degrees to 54 degrees, and so on, and from 342 degrees to 360 degrees, are not exactly the same. These times may be completely different, or some times the same, and some different. In other words, the time taken for the automotive rotary motor to rotate the preset angle is not exactly the same, and the sampling signal will not be at the intersection of the isoangular line and the vibration signal.

[0132] In scenarios where angular acceleration is not zero, assuming the angular velocity changes from 12000 rpm to 3000 rpm within 0.3 seconds, the angular acceleration is -1000 pi rad / s. 2 And the angular acceleration is variable. For example... Figure 10A As shown, the sinusoidal signal changes from dense to sparse. If we assume that the angular velocity of the rotating electromechanical equipment remains constant during the rotation process, we solve the time series for equal-angle sampling and resample the vibration signal according to the time series to obtain multiple sampled signals. At this time, the equal-angle sampling error increases. For example, the root mean square error reaches 0.40127064.

[0133] And adopting such Figure 1 The method shown obtains a time series and resamples the vibration signal based on the time series to obtain multiple sampled signals. This improves the accuracy of angle sampling, such as... Figure 10B As shown. For example, the root mean square error was reduced to 0.21200022.

[0134] Figure 11 This diagram illustrates a possible exemplary block diagram of a communication device according to an embodiment of this application. The device 1100 includes a transceiver module 1120 and a processing module 1110. The transceiver module 1120 may include a receiving unit and a sending unit. The processing module 1110 is used to control and manage the operation of the device 1100. The transceiver module 1120 is used to support communication between the device 1100 and other network entities. Optionally, the device 1100 may further include a storage unit for storing program code and data of the device 1100.

[0135] Optionally, each module in the device 1100 can be implemented by software.

[0136] Optionally, the processing module 1110 may be a processor or controller, such as a general-purpose central processing unit (CPU), a general-purpose processor, a digital signal processing unit (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the embodiments of this application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The transceiver module 1120 may be a communication interface, a transceiver, or a transceiver circuit, etc., wherein the communication interface is a general term, and in a specific implementation, the communication interface may include multiple interfaces, and the storage unit may be a memory.

[0137] The processing module 1110 in device 1100 can support device 1100 in performing... Figure 1 S102, S103, S104, and S105.

[0138] The transceiver module 1120 can support communication between the device 1100 and other modules. For example, the transceiver module 1120 can support the device 1100 in performing... Figure 1 S101 and S102 in the example.

[0139] For example, the transceiver module 1120 is used to acquire vibration signals collected by the vibration sensor of the rotating electromechanical equipment; acquire rotation signals collected by the rotation sensor of the rotating electromechanical equipment; the processing module 1110 is used to determine a time series based on the rotation signals, wherein the time series includes multiple moments, and the rotation angle of the rotating electromechanical equipment is the same between any two adjacent moments; resample the vibration signals based on the time series to obtain multiple sampled signals; and determine the fault diagnosis result of the rotating electromechanical equipment based on the multiple sampled signals.

[0140] In one possible design, the processing module 1110 is configured to determine a discrete signal corresponding to the rotation process of the rotating electromechanical equipment based on the rotation signal when determining the time series based on the rotation signal; wherein the discrete signal includes at least one of angular velocity signal, angular acceleration signal or angle signal; and determine the time series based on the discrete signal.

[0141] In one possible design, the processing module 1110 is configured to determine the rotation angle of the rotating electromechanical equipment based on the discrete signal when determining the time series based on the discrete signal, and to determine the time series based on the rotation angle of the rotating electromechanical equipment.

[0142] In one possible design, the processing module 1110 is used to determine the discrete signal using a Luneburger observer based on the rotation signal when determining the discrete signal based on the rotation signal.

[0143] In one possible design, the processing module 1110 is used to determine the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment based on the discrete signal when determining the time series based on the discrete signal; and to determine the time series based on the instantaneous angle curve and a preset angle.

[0144] In one possible design, the processing module 1110 is used to, when determining the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment based on the discrete signal, if the discrete signal includes an angular velocity signal, perform curve fitting on the angular velocity signal to obtain an instantaneous angular velocity curve, and integrate the instantaneous angular velocity curve to obtain the instantaneous angle curve.

[0145] In one possible design, the processing module 1110 is used to determine the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment based on the discrete signal, if the discrete signal includes an angular velocity signal and an angle signal, to perform curve fitting on the angle signal, and to determine the instantaneous angle curve based on the slope constraint of the instantaneous angle curve using the angular velocity signal as the slope constraint of the instantaneous angle curve.

[0146] In one possible design, the processing module 1110 is used to, when determining the instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment based on the discrete signal, if the discrete signal includes an angular velocity signal, an angular acceleration signal, and an angle signal, perform interpolation fitting based on the angular velocity signal, the angle signal, and the angular acceleration signal to obtain the instantaneous angle curve.

[0147] In one possible design, the rotation sensor is a rotary transformer or an coded disk.

[0148] In one possible design, the device is applied to scenarios where the rotating electromechanical equipment rotates at a non-uniform speed.

[0149] In one possible design, the processing module 1110 is used to, when determining the fault diagnosis result of the rotating electromechanical equipment based on the plurality of sampled signals, use the plurality of sampled signals as input signals of a preset fault diagnosis algorithm, obtain the output result of the preset fault diagnosis algorithm, and use the output result as the fault diagnosis result of the rotating electromechanical equipment.

[0150] It should be understood that the device 1100 according to the embodiments of this application can correspond to the fault diagnosis device in the foregoing method embodiments, and the operation and / or function of each module in the device 1100 are respectively to implement the corresponding steps of the method of the fault diagnosis device in the foregoing method embodiments. Therefore, the beneficial effects in the foregoing method embodiments can also be achieved. For the sake of brevity, it will not be described in detail here.

[0151] Figure 12 A schematic structural diagram of a communication device 1200 according to an embodiment of this application is shown. Figure 12 As shown, the device 1200 includes a processor 1201.

[0152] When device 1200 is a fault diagnosis device or a chip in a fault diagnosis device, in one possible implementation, when processor 1201 is used to call an interface to perform the following actions:

[0153] The vibration signal collected by the vibration sensor of the rotating electromechanical equipment is acquired; the rotation signal collected by the rotation sensor of the rotating electromechanical equipment is acquired; a time series is determined based on the rotation signal, wherein the time series includes multiple moments, and the rotation angle of the rotating electromechanical equipment is the same between any two adjacent moments; the vibration signal is resampled based on the time series to obtain multiple sampled signals; and the fault diagnosis result of the rotating electromechanical equipment is determined based on the multiple sampled signals.

[0154] It should be understood that the device 1200 can also be used to perform other steps and / or operations on the fault diagnosis device side in the previous embodiments, which will not be described in detail here for the sake of brevity.

[0155] It should be understood that the processor 1201 can call an interface to perform the above-mentioned sending and receiving operations. The called interface can be a logical interface or a physical interface, and there is no limitation on this. Optionally, the physical interface can be implemented using a transceiver. Optionally, the device 1200 further includes a transceiver 1203.

[0156] Optionally, the device 1200 further includes a memory 1202, which may store the program code in the above method embodiments for the processor 1201 to call.

[0157] Specifically, if the device 1200 includes a processor 1201, a memory 1202, and a transceiver 1203, the processor 1201, memory 1202, and transceiver 1203 communicate with each other through internal connection paths to transmit control and / or data signals. In one possible design, the processor 1201, memory 1202, and transceiver 1203 can be implemented using chips. The processor 1201, memory 1202, and transceiver 1203 may be implemented in the same chip, or they may be implemented in different chips, or any two of their functions may be combined into one chip. The memory 1202 can store program code, and the processor 1201 calls the program code stored in the memory 1202 to implement the corresponding functions of the device 1200.

[0158] The methods disclosed in the embodiments of this application can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, a system-on-a-chip (SoC), a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0159] It is understood that the memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0160] Based on the above embodiments, a fault diagnosis device for rotating electromechanical equipment is provided. The device includes: a communication module and a processing module; the communication module is used to communicate with the vibration sensor of the rotating electromechanical equipment and acquire vibration signals from the vibration sensor; the communication module is used to communicate with the rotation sensor of the rotating electromechanical equipment and acquire rotation signals from the rotation sensor; the processing module is used to execute the method provided in the above embodiments.

[0161] Based on the above embodiments, this application also provides a rotating electromechanical device, which includes a vibration sensor, a rotation sensor, and a fault diagnosis device for performing the methods provided in the above embodiments.

[0162] Based on the above embodiments, this application also provides an electric vehicle, which includes a vehicle body, wheels, and a rotating electromechanical device for performing the methods provided in the above embodiments.

[0163] It should be understood that in the embodiments of this application, the designations "first", "second", etc. are only for distinguishing different objects, such as for distinguishing different parameter information or messages, and do not constitute a limitation on the scope of the embodiments of this application. The embodiments of this application are not limited thereto.

[0164] It should also be understood that, in the various embodiments of this application, the sequence numbers of the above processes do not imply the order of execution; the execution order of each process should be determined by its function and internal logic. The various numerical numbers or sequence numbers involved in the above processes are merely for descriptive convenience and should not constitute any limitation on the implementation process of the embodiments of this application.

[0165] It should also be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0166] In this application, expressions such as "the item includes one or more of the following: A, B, and C" generally mean, unless otherwise specified, that the item can be any one of the following: A; B; C; A and B; A and C; B and C; A, B and C; A and A; A, A and A; A, A and B; A, A and C, A, B and B; A, C and C; B and B, B, B and B, B, B and C, C and C; C, C and C, and other combinations of A, B, and C. The above example uses three elements, A, B, and C, to illustrate the possible entries for the item. When expressed as "the item includes at least one of the following: A, B, ..., and X," that is, when the expression contains more elements, then the applicable entries for the item can also be obtained according to the aforementioned rules.

[0167] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0168] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0169] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0170] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0171] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0172] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A fault diagnosis method for rotating electromechanical equipment, characterized in that, The method includes: Acquire vibration signals collected by vibration sensors of rotating electromechanical equipment; Acquire the rotation signal collected by the rotation sensor of the rotating electromechanical equipment; A time series is determined based on the rotation signal collected from time A to time B. The time series includes the time corresponding to each preset rotation angle of the rotating electromechanical equipment. The time series includes multiple times, and the rotation angle of the rotating electromechanical equipment is the same between any two adjacent times. The vibration signal from time A to time B is resampled according to the time series to obtain multiple sampled signals. The rotating electromechanical equipment rotates K revolutions from time A to time B, and the number of the multiple sampled signals is 360K / θ. e , θ e The preset angle; The fault diagnosis result of the rotating electromechanical equipment is determined based on the multiple sampled signals.

2. The method as described in claim 1, characterized in that, Determining the time series based on the rotation signal includes: The discrete signal corresponding to the rotation process of the rotating electromechanical equipment is determined based on the rotation signal; wherein the discrete signal includes at least one of angular velocity signal, angular acceleration signal, or angle signal; The time series is determined based on the discrete signal.

3. The method as described in claim 2, characterized in that, Determining discrete signals based on the rotation signal includes: The discrete signal is determined using a Luneburger observer based on the rotation signal.

4. The method as described in claim 2 or 3, characterized in that, Determining the time series based on the discrete signal includes: The rotation angle of the rotating electromechanical equipment is determined based on the discrete signal; The time series is determined based on the rotation angle of the rotating electromechanical equipment.

5. The method as described in claim 2 or 3, characterized in that, Determining the time series based on the discrete signal includes: The instantaneous angle curve corresponding to the rotation process of the rotating electromechanical equipment is determined based on the discrete signal; The time series is determined based on the instantaneous angle curve and the preset angle.

6. The method according to any one of claims 1-3, characterized in that, The method is applied to scenarios where the rotating electromechanical equipment rotates at a non-uniform speed.

7. The method according to any one of claims 1-3, characterized in that, Determining the fault diagnosis result of the rotating electromechanical equipment based on the multiple sampled signals includes: The multiple sampled signals are used as input signals to a preset fault diagnosis algorithm to obtain the output result of the preset fault diagnosis algorithm, and the output result is used as the fault diagnosis result of the rotating electromechanical equipment.

8. A fault diagnosis device for rotating electromechanical equipment, characterized in that, The device includes: a communication module and a processing module; The communication module is used to communicate with the vibration sensor of the rotating electromechanical equipment and acquire vibration signals from the vibration sensor; The communication module is used to communicate with the rotation sensor of the rotating electromechanical equipment and acquire rotation signals from the rotation sensor; The processing module is configured to perform the method as described in any one of claims 1-7.

9. A rotating electromechanical device, characterized in that, The rotating electromechanical equipment includes a vibration sensor, a rotation sensor, and a fault diagnosis device as described in claim 8.

10. An electric vehicle, characterized in that, Includes the vehicle body, wheels, and rotating electromechanical equipment as described in claim 9.

11. A fault diagnosis device for rotating electromechanical equipment, characterized in that, Includes a processor, which is coupled to memory: The processor is configured to execute computer instructions stored in the memory to cause the fault diagnosis device to perform the method as described in any one of claims 1-7.

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