Rotating machine fault diagnosis method, system, device and program

Abstract

The invention discloses a rotating machinery fault diagnosis method, system, equipment and program. The method comprises the following steps: acquiring sampling data; the sampling data comprises a rotating speed pulse signal and a fault performance signal; re-sampling the sampling data into a rotating speed pulse signal and a fault expression signal which are arranged at equal angle intervals; performing rotating speed fitting on the pulse signals at equal angle intervals to obtain rotating speed signals; fault representation signals at equal angle intervals are selected as reference axis analysis data based on the rotating speed signals; performing interpolation on the reference axis analysis data to obtain non-reference axis analysis data based on the rotating speed signal and the rotating speed ratio of the reference axis to the non-reference axis; and performing synchronous channel analysis, mixed channel analysis and synchronous channel analysis of non-reference axis analysis data on the reference axis analysis data to obtain the position of a gear where a fault occurs. Through the processing scheme disclosed by the invention, the position of the gear with the fault can be accurately identified.

Description

Technical Field

[0001] The present invention relates to the technical field of digital signal processing, and in particular to a rotating machinery fault diagnosis method, system, equipment and program. Background Art

[0002] Engines, motors, gearboxes, drive shafts and other mechanical structures that rotate continuously during operation are all rotating machinery. Vehicles, ships, aerospace vehicles, etc. all include a large number of rotating machinery, and rotating machinery as a power source is often one of the core components of these products. Therefore, fault diagnosis of rotating machinery is of great significance in product offline detection and real-time status monitoring.

[0003] Rotating machinery fault diagnosis requires the collection of signals during its operation. Vibration signals, noise signals, speed signals, torque signals, temperature signals, etc. are all commonly used signal types for fault diagnosis. Different from general machinery, the characteristic of rotating machinery is that its fault manifestation is often related to the speed.

[0004] The traditional method of rotating machinery fault diagnosis is to synchronously test the speed signal and the fault manifestation signal, which can be different types of signals such as acceleration, sound, temperature, etc. Then, the fault manifestation signal is subjected to order spectrum analysis or ColorMap analysis.

[0005] Among them, the order spectrum analysis adopts the equal angle sampling method, that is, the time for the rotating machinery to rotate through a fixed angle is calculated through the speed signal, so as to resample the fault performance signal. In the time between every two values ​​of the fault performance signal, the rotating machinery rotates through the same angle. The order spectrum curve can be obtained by Fourier transform analysis of the signal. By finding the order where the peak occurs and corresponding to the structural characteristics of the rotating machinery, the fault of the rotating machinery can be diagnosed.

[0006] ColorMap analysis does not require equal-angle sampling. Instead, the fault manifestation signal is divided into small segments, each segment is short-time Fourier transformed, and the average speed of the rotating machinery during the time period is calculated. The speed label, frequency label and short-time Fourier transform result are plotted on a graph, which is the ColorMap of the signal. The order characteristics related to the speed are shown as a straight line with a slope greater than 0 on the ColorMap graph. By observing the ColorMap graph, finding the speed, frequency and order corresponding to the fault signal, and the corresponding rotating machinery structural characteristics, the fault of the rotating machinery can be diagnosed.

[0007] However, the above traditional rotating machinery fault diagnosis methods have the disadvantage of being unable to locate the fault, that is, they cannot automatically diagnose the fault location. After obtaining the problem order, the traditional analysis method needs to correspond to the structural characteristics of the rotating machinery itself to identify the fault location. If the rotating machinery structural parameters are unknown or the fault generation mechanism is unclear, the fault location diagnosis cannot be performed.

[0008] It can be seen that the above existing fault diagnosis methods are still inconvenient and defective in use, and are in urgent need of further improvement. How to create a new rotating machinery fault diagnosis method has become a goal that the industry urgently needs to improve. Summary of the invention

[0009] In view of this, an embodiment of the present disclosure provides a rotating machinery fault diagnosis method, which at least partially solves the problems existing in the prior art.

[0010] In a first aspect, an embodiment of the present disclosure provides a method for diagnosing a fault in a rotating machine, the method comprising the following steps: Acquiring sampling data; the sampling data includes a speed pulse signal and a fault performance signal; Resampling the sampled data into speed pulse signals and fault manifestation signals at equal angle intervals; Performing speed fitting on the pulse signals with equal angle intervals to obtain a speed signal; Selecting fault manifestation signals with equal angle intervals as reference shaft analysis data based on the rotation speed signal; Based on the speed signal and the speed ratio of the reference shaft to the non-reference shaft, interpolating the reference shaft analysis data to obtain the non-reference shaft analysis data; The gear position where the fault occurs is obtained by performing synchronous channel analysis, mixed channel analysis and synchronous channel analysis on the reference axis analysis data and non-reference axis analysis data.

[0011] According to a specific implementation of the embodiment of the present disclosure, resampling the sampled data into a speed pulse signal and a fault manifestation signal with equal angle intervals includes: The host computer reads the sampled data one by one in real time; the time when the first sampled data is read is set to 0, and the time interval between any two adjacent sampling points is , f s is the sampling frequency; Each time a speed pulse signal is read, it is determined whether the current speed pulse signal sampling moment is the pulse excitation moment; wherein, when the current speed pulse signal value is greater than the preset value and the previous pulse signal value is less than the preset value, it is determined that the current speed pulse signal sampling moment is at the pulse excitation moment, the moment corresponding to the sampling point is recorded, and the current speed pulse signal sampling moment is written into the time series of the reference axis equiangular resampling; the fault manifestation signal data corresponding to the moment is written into the fault manifestation signal sequence of the reference axis equiangular resampling.

[0012] According to a specific implementation of the embodiment of the present disclosure, the speed fitting of the pulse signal with equal angle intervals includes the following steps: The speed fitting is based on the following formula: ; in, rpm is the speed signal; PPR is the circular resolution; Function represents the offset subtraction function; The time series corresponding to the speed of this fitting T slices.

[0013] According to a specific implementation of the embodiment of the present disclosure, selecting fault manifestation signals with equal angle intervals as reference shaft analysis data based on the speed signal includes: when rpm When the preset minimum speed is reached, the fault performance signals at equal angle intervals are collected as reference axis analysis data; rpm When the preset maximum speed is reached, the acquisition ends.

[0014] According to a specific implementation of the embodiment of the present disclosure, based on the speed signal and the speed ratio of the reference axis to the non-reference axis, the reference axis analysis data is interpolated to obtain the non-reference axis analysis data, including: After each resampling of the reference axis, the number of resamplings of the non-reference axis between the current resampling point and the previous resampling point of the reference axis is calculated in combination with the speed ratio; Perform linear interpolation on the two resampling point times of the reference axis to obtain the resampling time of the non-reference axis; select the sampling point closest to the resampling time of the non-reference axis from the sampling data as the non-reference axis analysis data; The non-reference axis resampling points are calculated based on the following formula: ; ; in, is the sequence number of the non-reference axis resampling; The first resampled non-reference axis points; Indicates the serial number of the signal collected by the current signal collection device; is the coefficient of the recursive relation for non-reference axis resampling; is the speed ratio of the non-reference shaft to the reference shaft.

[0015] According to a specific implementation of the embodiment of the present disclosure, the gear position where the fault occurs is obtained by performing synchronous channel analysis, mixed channel analysis and synchronous channel analysis of non-reference axis analysis data on the reference axis analysis data, including: The synchronous channel analysis includes: The reference axis analysis data and the non-reference axis analysis data are divided into different slice data, each slice reflecting the rotation of the axis within the time period of collecting the slice data; Calculate the average value of multiple slice data of the rotation axis; The averaged slice data is subjected to fast Fourier transformation, and then the autospectral linear spectrum is calculated to obtain the synchronous channel analysis results; The mixed channel analysis comprises: The reference axis analysis data is divided into different slice data, which are consistent with the reference axis slice data of the synchronous channel; Perform fast Fourier transform on all current slice data, calculate the self-spectrum linear spectrum, and take the maximum value of all self-spectra as the mixed channel analysis result.

[0016] According to a specific implementation of the embodiment of the present disclosure, the average value of multiple slice data of the rotating axis in the synchronous channel analysis is calculated based on the following formula: : ; in, For the Signal segment individual data; Indicates the total number of signal segments; The averaged slice data is fast Fourier transformed based on the following formula : ; in, After Fourier transformation frequency components; In the time domain The signal mean of the sampling points; is the phase relationship of different frequency components; is the frequency domain frequency components, 0≤k≤ ; is the total length of the signal.

[0017] According to a specific implementation of the embodiment of the present disclosure, the mixed channel analysis includes: The synchronous channel analysis results are calculated based on the following formula: ; in, It is the result of synchronous channel analysis; The mixed channel analysis comprises: Based on the calculation of the autospectral linear spectrum of all current signal segment data: ; in, For the After Fourier transformation of the signal segment frequency components; For the Signal segment individual data; is the phase relationship of different frequency components; is the first frequency components, 0≤k≤ ; is the total length of the signal; Perform mixed channel calculation on the autospectral linear spectrum calculation result: ; in, is the analysis result of the mixed channel; is the total number of signal segments.

[0018] In a second aspect, an embodiment of the present disclosure provides a rotating machinery fault diagnosis system, the system comprising: A data acquisition module is configured to acquire sampled data; the sampled data includes a speed pulse signal and a fault manifestation signal; A resampling module, configured to resample the sampled data into a rotation speed pulse signal and a fault manifestation signal at equal angle intervals; A fitting module is configured to perform speed fitting on the pulse signals with equal angle intervals to obtain a speed signal; an analysis data module configured to select fault manifestation signals with equal angle intervals as reference axis analysis data based on the rotation speed signal; and interpolate the reference axis analysis data to obtain non-reference axis analysis data based on the rotation speed signal and the rotation speed ratio of the reference axis to the non-reference axis; The analysis module is configured to obtain the gear position where the fault occurs by performing synchronous channel analysis, mixed channel analysis and synchronous channel analysis on the reference shaft analysis data and the non-reference shaft analysis data.

[0019] In a third aspect, an embodiment of the present disclosure further provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, which, when executed by a computer, cause the computer to execute a rotating machinery fault diagnosis method in the aforementioned first aspect or any implementation of the first aspect.

[0020] A rotating machinery fault diagnosis method in an embodiment of the present invention divides the original time domain signal into different intervals according to the rotational speed of different shafts of the rotating machinery. Each interval can accurately reflect a single rotation of the shaft within a certain period of time. The single rotation signal is averaged through synchronous speed analysis, and signal components unrelated to the rotating shaft will be suppressed, thereby obtaining a fault signal on the rotating shaft. For faults unrelated to the speed, the hybrid channel is not averaged, and fault signals unrelated to each rotating shaft can be obtained from the hybrid channel results.

[0021] The present invention has the following advantages: (1) Accurately locate the faulty axis based on the synchronization channel. By comparing the synchronization channels of the reference axis and the non-reference axis, the axis where the fault is located is diagnosed according to the fault order. This method utilizes the correlation between the shaft synchronization signals, can effectively focus on the faults related to shaft synchronization, and accurately determine which axis the fault occurred on. For multi-axis equipment such as mechanical systems, quickly and accurately locating the faulty axis can greatly reduce maintenance time and cost, and avoid blindly checking multiple components.

[0022] (2) The synchronous channel effectively eliminates irrelevant noise interference. In the synchronous channel analysis, noise that is not synchronized with the shaft (such as bearing noise) will be ignored. This allows the diagnosis process to focus on the key signals synchronized with the shaft, avoiding the interference of irrelevant noise on fault diagnosis, thereby improving the accuracy of fault diagnosis synchronized with the shaft. In a complex mechanical operation environment, there are various kinds of noise, and this ability to eliminate interference in a targeted manner is very critical.

[0023] (3) Hybrid channels supplement the diagnosis of asynchronous faults. In addition to being able to diagnose faults that are synchronous with the shaft, hybrid channels are also introduced to diagnose faults that are not synchronous with the shaft. Since the noise generated by some faults is not synchronous with the shaft, these faults may be missed by relying solely on synchronous channel analysis. The use of hybrid channels just makes up for this shortcoming, making the diagnostic method more comprehensive and able to cover different types of faults. Faults that are synchronous or asynchronous with the shaft can be detected.

[0024] (4) The hybrid channel slice data features are fully preserved. The hybrid channel uses the same slice data as the synchronous channel, but the processing method does not take the average value. Instead, the noise characteristics of all slice data are retained by taking the maximum value. This data processing method can retain the information related to asynchronous faults to the greatest extent, and will not lose some key features that may indicate faults due to operations such as averaging. This helps to more keenly capture fault signals that are not synchronized with the shaft, further improving the accuracy of asynchronous fault diagnosis.

[0025] (5) Potential for standardization and batch diagnosis. This diagnostic method provides a relatively standardized process for product quality inspection and fault diagnosis. Whether it is quality inspection of newly produced products on the product production line or troubleshooting of multiple devices of the same model during equipment maintenance, the same diagnostic steps and comparison standards can be used. This enables fault diagnosis to be carried out in batches, improves diagnostic efficiency, and also helps to ensure the consistency and reliability of diagnostic results. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The above is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0027] Figure 1 A schematic diagram of a rotating machinery fault diagnosis method provided by an embodiment of the present disclosure; Figure 2 A flowchart of a rotating machinery fault diagnosis method provided by an embodiment of the present disclosure; Figure 3 A schematic diagram of a pulse signal and a vibration signal and a resampling rule provided in an embodiment of the present disclosure; Figure 4 A pulse signal schematic diagram provided for an embodiment of the present disclosure; Figure 5 A schematic diagram of a speed signal after pulse fitting provided by an embodiment of the present disclosure; Figure 6 A schematic diagram of an image of synchronous channel angle domain analysis of a reference axis and a non-reference axis provided in an embodiment of the present disclosure; Figure 7 A schematic diagram of the structure of a rotating machinery fault diagnosis system provided by an embodiment of the present disclosure; Figure 8 A schematic diagram of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0029] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0030] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement a device and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0031] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0032] The embodiment of the present invention provides a method for diagnosing faults of rotating machinery by synchronously sampling the speed pulse and the original time domain signal of the fault manifestation. The time domain signal data with equal time intervals collected by the front end is resampled to obtain the time domain signal data with equal angle intervals. The speed corresponding to the sampling moment can be calculated according to the time interval difference between the time domain signals with equal angle intervals; the resampled fault manifestation signal of the reference axis (the reference axis is the rotating axis for collecting pulse signals) can be obtained according to the resampled data with equal angle intervals; the resampled fault manifestation signal of the non-reference axis (the non-reference axis is the gear axis with a fixed speed ratio with the reference axis) can be calculated according to the resampled data of the reference axis and the speed ratio. By performing synchronous channel analysis and mixed channel analysis on the resampled fault manifestation signals of the reference axis and the non-reference axis, the gear position where the fault occurs can be comprehensively diagnosed.

[0033] Figure 1 A schematic diagram of a rotating machinery fault diagnosis method process provided in an embodiment of the present disclosure.

[0034] Figure 2 For Figure 1 A corresponding flow chart of a rotating machinery fault diagnosis method.

[0035] like Figure 1 As shown, in step S110, sampling data is acquired; the sampling data includes a speed pulse signal and a fault manifestation signal.

[0036] More specifically, the rotation speed sensor for collecting pulse signals, the sensor for collecting fault manifestation signals and the front end of the data acquisition device are connected and debugged, and the relevant parameters are set.

[0037] The parameters to be set include: different shaft speed ratios , sampling frequency , frequency resolution , Window Function Circumferential resolution , Angle domain analysis frequency , Angular resolution , Angle domain analysis average rate , speed fitting time interval Zero reference value with pulse signal . The speed ratio refers to the proportional relationship between the speeds of the non-reference axis and the reference axis; the sampling frequency refers to the number of samples per second; the frequency resolution refers to the minimum interval that can distinguish two different frequency components in the frequency domain; the average rate refers to the number of slice starting points contained in the sample per second when the signal is sliced; the window function is to reduce the discontinuity of the signal at both ends of the slice by weighting the signal slices, thereby avoiding problems such as spectrum leakage and harmonic interference; the speed fitting time interval is the manually defined time interval length. Every time the sampling time passes a specified time interval, the pulse data sampled within the time interval will be speed fitted once; the pulse signal zero position reference value is the threshold value for pulse excitation. When the pulse signal passes this value, a pulse is considered to have occurred.

[0038] More specifically, the process proceeds to step S120.

[0039] In step S120, the sampled data is resampled into rotation speed pulse signals and fault manifestation signals with equal angle intervals.

[0040] In an embodiment of the present invention, the step of resampling the sampled data into a rotation speed pulse signal and a fault manifestation signal at equal angle intervals includes: The host computer reads the sampled data one by one in real time; the time when the first sampled data is read is set to 0, and the time interval between any two adjacent sampling points is , f s is the sampling frequency; Each time a speed pulse signal is read, determine whether the current speed pulse signal sampling moment is the pulse excitation moment; wherein, when the current speed pulse signal value is greater than the preset value, and the previous pulse signal value is less than the preset value, determine that the current speed pulse signal sampling moment is at the pulse excitation moment, record the corresponding moment of the sampling point, and write the current speed pulse signal sampling moment into the reference axis equal angle resampling time series; write the fault manifestation signal data corresponding to the moment into the reference axis equal angle resampling fault manifestation signal sequence T .

[0041] More specifically, the time domain signals with equal time intervals collected by the front end of the data acquisition device are processed into time domain signals with equal angle intervals.

[0042] like Figure 3 As shown in the figure, the pulses and fault performance signals collected by the front end of the data acquisition device are read one by one in real time by the host computer. The time of reading the first data is set to 0, and the time interval between any two adjacent sampling points is , f s is the sampling frequency, that is, the number of samples collected in 1 second, usually 12800, 25600 or 51200, That is, the time interval for collecting data. Each time a pulse data is read, according to the set pulse signal zero reference value , it can be determined whether the current pulse data sampling moment is the pulse excitation moment. If the current pulse signal value is greater than , and the previous pulse signal value is less than , then it is the pulse excitation time, then the sampling point corresponds to the time Record and write the time series of equal angle resampling of the reference axis At the same time, the fault performance signal data corresponding to the moment is recorded and written into the fault performance signal sequence resampled by the reference axis and equal angles. .

[0043] Next, go to step S130.

[0044] In step S130, the rotation speed fitting is performed on the pulse signals with equal angle intervals to obtain a rotation speed signal.

[0045] In an embodiment of the present invention, performing speed fitting on the pulse signals with equal angle intervals comprises the following steps: The speed fitting is based on the following formula: ……Formula 1 in, rpm is the speed signal; PPR is the circular resolution; Function represents the offset subtraction function; The time series corresponding to the speed of this fitting T slices.

[0046] More specifically, the obtained reference axis iso-angle resampled time series Perform speed fitting calculation.

[0047] Time Series Each time a new data is added, it is determined whether the time interval between the new time data and the last speed fitting time reaches the fitting time interval. If it reaches the value, the speed fitting is performed and the current speed fitting time is recorded. The speed fitting calculation is to convert the current sampling time before The time data within the time range is offset and subtracted to obtain According to the speed calculation formula, the current sampling time before The speed at each sampling time within the time range is taken as the average value before the current sampling time. Fitted speed in time range rpm .

[0048] The calculation method of the fitting speed is: ; Where PPR represents the circumferential resolution, The function represents the offset subtraction function, Indicates the time series slice corresponding to the fitting speed.

[0049] Next, go to step S140.

[0050] In step S140, fault manifestation signals with equal angle intervals are selected as reference axis analysis data based on the rotation speed signal.

[0051] In an embodiment of the present invention, selecting fault manifestation signals at equal angle intervals as reference axis analysis data based on the speed signal includes: when rpm When the preset minimum speed is reached, the fault performance signals at equal angle intervals are collected as reference axis analysis data; rpm When the preset maximum speed is reached, the acquisition ends.

[0052] More specifically, if Figure 4 As shown, if rpm Meet the minimum speed requirement for acquisition rpm min , then start collecting the reference axis analysis data of step S120. rpm Reach the highest speed of acquisition rpm max , then the collection ends.

[0053] Next, go to step S150.

[0054] In step S150, based on the rotation speed signal and the rotation speed ratio between the reference axis and the non-reference axis, the reference axis analysis data is interpolated to obtain the non-reference axis analysis data.

[0055] In an embodiment of the present invention, based on the speed signal and the speed ratio of the reference axis to the non-reference axis, the reference axis analysis data is interpolated to obtain the non-reference axis analysis data, including: After each resampling of the reference axis, the number of resamplings of the non-reference axis between the current resampling point and the previous resampling point of the reference axis is calculated in combination with the speed ratio; Perform linear interpolation on the two resampling point times of the reference axis to obtain the resampling time of the non-reference axis; select the sampling point closest to the resampling time of the non-reference axis from the sampling data as the non-reference axis analysis data; The non-reference axis resampling points are calculated based on the following formula: ……Formula 2 ……Formula 3 in, is the sequence number of the non-reference axis resampling; The first resampled non-reference axis points; Indicates the serial number of the signal collected by the current signal collection device; is the coefficient of the recursive relation for non-reference axis resampling; is the speed ratio of the non-reference shaft to the reference shaft.

[0056] More specifically, the reference axis iso-angular resampling is performed (from When selecting points, non-reference axis equal angle resampling is performed.

[0057] Because the non-reference axis has no corresponding pulse signal, the resampling of the non-reference axis fault performance signal should be based on the reference axis resampling data. Therefore, after each resampling of the reference axis, it is necessary to combine the speed ratio Calculate the number of times the non-reference axis needs to be resampled between the current resampling point and the previous resampling point of the reference axis. Perform linear interpolation on the two resampling point times of the reference axis to obtain the resampling time of the non-reference axis, and select the sampling point closest to the resampling time of the non-reference axis from the sampling data as the resampling data of the non-reference axis.

[0058] The calculation method of non-reference axis resampling points is: ; ; In the formula, Indicates the sequence number of the non-reference axis resampling, Indicates the non-reference axis resampling points; Indicates the serial number of the signal collected by the current data acquisition device. is a recursive relation coefficient for non-reference axis resampling is the speed ratio of the non-reference shaft to the reference shaft. Multiply by 1 / fs to get the resampling point time.

[0059] Next, go to step S160.

[0060] In step S160, the gear position where the fault occurs is obtained by performing synchronous channel analysis, mixed channel analysis and synchronous channel analysis on the reference shaft analysis data and the non-reference shaft analysis data.

[0061] In an embodiment of the present invention, obtaining the gear position where the fault occurs by performing synchronous channel analysis, mixed channel analysis, and synchronous channel analysis on the reference shaft analysis data, includes: The synchronous channel analysis includes: The reference axis analysis data and the non-reference axis analysis data are divided into different slice data, each slice reflects the rotation of the axis within the time period of collecting the slice data; the slice length is several PPR , that is, the slice data is the data over several complete revolutions of the axis; for example, the slice length is a multiple of PPR, generally 2, 4 or 8 times.

[0062] Calculate the average value of multiple slice data of the rotation axis; The averaged slice data is subjected to fast Fourier transformation, and then the autospectral linear spectrum is calculated to obtain the synchronous channel analysis results; The mixed channel analysis comprises: The reference axis analysis data is divided into different slice data, which are consistent with the reference axis slice data of the synchronous channel; After taking the maximum value of all current slice data, perform fast Fourier transform, calculate the self-spectrum linear spectrum, and take the maximum value of all self-spectra as the mixed channel analysis result.

[0063] In the embodiment of the present invention, the average value of multiple slice data of the rotating axis in the synchronous channel analysis is calculated based on the following formula: : ……Formula 4 in, For the Signal segment individual data; Indicates the total number of signal segments; The averaged slice data is fast Fourier transformed based on the following formula : ……Formula 5 in, After Fourier transformation frequency components; In the time domain The signal mean of the sampling points; is the phase relationship of different frequency components; is the first frequency components, 0≤k≤ ; is the total length of the signal.

[0064] In an embodiment of the present invention, the mixed channel analysis includes: The synchronous channel analysis results are calculated based on the following formula: ……Formula 6 in, The synchronous channel analysis results are obtained by calculating the synchronous channel analysis results of the reference axis and the non-reference axis. The mixed channel analysis comprises: Based on the calculation of the autospectral linear spectrum of all current signal segment data: ……Formula 7 in, For the After Fourier transformation of the signal segment frequency components; For the Signal segment individual data; is the phase relationship of different frequency components; is the first frequency components, 0≤k≤ ; is the total length of the signal; Perform mixed channel calculation on the autospectral linear spectrum calculation result: ……Formula 8 in, is the analysis result of the mixed channel; is the total number of signal segments.

[0065] More specifically, if Figure 5 , Figure 6 As shown, the reference axis (from The synchronous channel analysis and mixed channel analysis of the reference axis and the non-reference axis are performed respectively by resampling the fault manifestation signal H of the reference axis and the non-reference axis.

[0066] Analyzing frequency based on the angle domain , Angular resolution Available analysis bandwidth Each time a new data is added to the fault performance signal, it is determined whether the difference between the length of the fault performance signal and the length of the last synchronous channel analysis (same as mixed channel analysis) reaches (If this is the first time to perform synchronous channel analysis (or mixed channel analysis), determine whether the length has reached ), if it reaches, the current sampling point and the previous sampling point will be added together. Synchronous channel analysis and mixed channel analysis are performed on slices of data (non-reference axes only perform synchronous channel analysis, not mixed channel analysis).

[0067] Furthermore, the obtained resampled slices of the reference axis and non-reference axis data represent the rotation of the shaft within the sampling time range of the slice, and the analyzed slice data represent the rotation of the shaft within the acquisition time range of the slice, that is, the average of all current slice data is taken and then FFT (Fast Fourier Transform) processing is performed to calculate its effective value as the synchronous channel analysis result.

[0068] Mixed channel analysis is to take the maximum value of all current slice data and perform FFT processing, and calculate its effective value as the mixed channel analysis result. Taking the average of the synchronous channel can eliminate the interference caused by other channels; taking the maximum value of the mixed channel and retaining the signal characteristics of all channels through peak hold.

[0069] The data resampled signal of each shaft is divided into different slice data. Each slice contains one or more rotations of the reference shaft. Each slice can accurately reflect the rotation of the shaft within the time period of the acquisition point and contains all noise components. By calculating the average value of multiple slice data of the shaft, the noise components that are not synchronized with the shaft are suppressed. By performing FFT processing on the averaged slice data, the synchronous channel analysis results can be obtained. By comparing the synchronous channels of the reference shaft and the non-reference shaft, the shaft where the fault is located can be diagnosed according to the fault order. Some noise is not synchronized with the shaft (such as bearing noise). When using synchronous channel analysis, such noise will be ignored. Therefore, a hybrid channel is used to diagnose noise that is not synchronized with the shaft. The slice data used in the hybrid channel is the same as the synchronous channel, but the average value is not taken. The noise characteristics of all slice data are retained by taking the maximum value. By comparing the hybrid channels of the reference shafts of different products of the same model, it is possible to diagnose whether there is a fault.

[0070] The calculation method for synchronous channel analysis is: The time window used by FFT is the Hanning window, and the coefficients multiplied by each element of the signal can be expressed by the formula Calculated, where Indicates the length of the signal segment, Indicates the signal The coefficient by which the elements are multiplied; Synchronous channel signal mean Calculation method: ; In the formula, For the Signal segment individual data; Indicates the total number of signal segments.

[0071] The Fourier transform must be calculated for each slice. The calculation method is as follows: ; In the formula, Indicates After Fourier transformation, the signal segment frequency components; It is the first The signal mean of the sampling points; Describes the phase relationship of different frequency components; Indicates the frequency domain frequency components, 0≤k≤ ; Indicates the total length of the signal.

[0072] The hybrid channel analysis includes: calculating the synchronous channel analysis result based on the following formula: ; in, It is the result of synchronous channel analysis; The mixed channel analysis includes: performing autospectral linear spectrum calculation on all current signal segment data: ; in, For the After Fourier transformation of the signal segment frequency components; For the Signal segment individual data; is the phase relationship of different frequency components; is the frequency domain frequency components, 0≤k≤ ; is the total length of the signal; Perform mixed channel calculation on the autospectral linear spectrum calculation result: ; in, is the analysis result of the mixed channel; is the total number of signal segments.

[0073] Diagnostic method: By comparing the synchronous channels of the reference axis and the non-reference axis, the axis where the fault is located can be diagnosed according to the fault order. Some noises are not synchronized with the rotating shaft (such as bearing noise). When using synchronous channel analysis, such noises will be ignored. Therefore, the hybrid channel is used to diagnose noises that are not synchronized with the rotating shaft. The slice data used by the hybrid channel is the same as that of the synchronous channel, but the average value is not taken. The noise characteristics of all slice data are retained by taking the maximum value. By comparing the hybrid channels of the reference axes of different products of the same model, it is possible to diagnose whether there is a fault that is not synchronized with the rotating shaft.

[0074] Compared with the traditional angle domain analysis method, the synchronous channel and hybrid channel angle domain analysis technology method of the present invention has the following advantages: (1) The present invention divides the original time domain signal into different intervals according to the rotation speed of different shafts of the rotating machinery. Each interval can accurately reflect the single rotation of the shaft within a certain period of time. The single rotation signal is averaged by synchronous speed analysis. The signal components irrelevant to the rotating shaft will be suppressed, thereby obtaining the fault signal on the rotating shaft. (2) For faults that are not related to the rotation speed, the mixed channel is not averaged, and a fault signal that is not related to each rotating shaft can be obtained from the mixed channel results.

[0075] Figure 7 A rotating machinery fault diagnosis system 700 provided by the present invention is shown, comprising a data acquisition module 710 , a resampling module 720 , a fitting module 730 , a data analysis module 740 and an analysis module 750 .

[0076] The data acquisition module 710 is used to acquire sampled data; the sampled data includes a speed pulse signal and a fault performance signal; The resampling module 720 is used to resample the sampled data into speed pulse signals and fault manifestation signals with equal angle intervals; The fitting module 730 is used to perform speed fitting on the pulse signal with equal angle intervals to obtain a speed signal; The analysis data module 740 is used to select fault manifestation signals with equal angle intervals as reference axis analysis data based on the speed signal; and interpolate the reference axis analysis data to obtain non-reference axis analysis data based on the speed signal and the speed ratio of the reference axis to the non-reference axis; The analysis module 750 is used to obtain the gear position where the fault occurs by performing synchronous channel analysis, mixed channel analysis and synchronous channel analysis on the reference shaft analysis data and the non-reference shaft analysis data.

[0077] See also Figure 8 The embodiment of the present disclosure further provides an electronic device 80, the electronic device comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute a rotating machinery fault diagnosis method in the aforementioned method embodiment.

[0078] The embodiment of the present disclosure also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute a rotating machinery fault diagnosis method in the aforementioned method embodiment.

[0079] An embodiment of the present disclosure also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, which, when executed by a computer, enable the computer to execute a rotating machinery fault diagnosis method in the aforementioned method embodiment.

[0080] Reference below Figure 8 , which shows a schematic diagram of the structure of an electronic device 80 suitable for implementing the embodiment of the present disclosure. The electronic device in the embodiment of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 8 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0081] like Figure 8As shown, the electronic device 80 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage device 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 80 are also stored. The processing device 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0082] Typically, the following devices may be connected to the I / O interface 805: input devices 806 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 808 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 809. The communication device 809 may allow the electronic device 80 to communicate with other devices wirelessly or by wire to exchange data. Although the electronic device 80 having various devices is shown in the figure, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0083] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device 809, or installed from a storage device 808, or installed from a ROM 802. When the computer program is executed by the processing device 801, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0084] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0085] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0086] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: obtains at least two Internet Protocol addresses; sends a node evaluation request including the at least two Internet Protocol addresses to a node evaluation device, wherein the node evaluation device selects an Internet Protocol address from the at least two Internet Protocol addresses and returns it; receives the Internet Protocol address returned by the node evaluation device; wherein the obtained Internet Protocol address indicates an edge node in a content distribution network.

[0087] Alternatively, the computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device: receives a node evaluation request including at least two Internet Protocol addresses; selects an Internet Protocol address from the at least two Internet Protocol addresses; and returns the selected Internet Protocol address; wherein the received Internet Protocol address indicates an edge node in a content distribution network.

[0088] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0089] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0090] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0091] It should be understood that various parts of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

[0092] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for diagnosing a rotating machinery fault, characterized in that: The method comprises the following steps: Acquiring sampling data; the sampling data includes a speed pulse signal and a fault performance signal; Resampling the sampled data into speed pulse signals and fault manifestation signals at equal angle intervals; Performing speed fitting on the pulse signals with equal angle intervals to obtain a speed signal; Selecting fault manifestation signals with equal angle intervals as reference shaft analysis data based on the rotation speed signal; Based on the speed signal and the speed ratio of the reference shaft to the non-reference shaft, interpolating the reference shaft analysis data to obtain the non-reference shaft analysis data; The gear position where the fault occurs is obtained by performing synchronous channel analysis, mixed channel analysis and synchronous channel analysis on the reference axis analysis data and non-reference axis analysis data.

2. A rotating machinery fault diagnosis method according to claim 1, characterized in that: The step of resampling the sampled data into speed pulse signals and fault manifestation signals at equal angle intervals includes: The host computer reads the sampled data one by one in real time; the time when the first sampled data is read is set to 0, and the time interval between any two adjacent sampling points is , f s is the sampling frequency; Each time a speed pulse signal is read, it is determined whether the current speed pulse signal sampling moment is the pulse excitation moment; wherein, when the current speed pulse signal value is greater than the preset value and the previous pulse signal value is less than the preset value, it is determined that the current speed pulse signal sampling moment is at the pulse excitation moment, the moment corresponding to the sampling point is recorded, and the current speed pulse signal sampling moment is written into the time series of the reference axis equiangular resampling; the fault manifestation signal data corresponding to the moment is written into the fault manifestation signal sequence of the reference axis equiangular resampling.

3. A rotating machinery fault diagnosis method according to claim 1, characterized in that: The speed fitting of the pulse signals at equal angle intervals comprises the following steps: The speed fitting is based on the following formula: ； in, rpm is the speed signal; PPR is the circular resolution; Function represents the offset subtraction function; The time series corresponding to the speed of this fitting T slices.

4. A rotating machinery fault diagnosis method according to claim 2, characterized in that: Selecting fault manifestation signals with equal angle intervals as reference axis analysis data based on the speed signal includes: when rpm When the preset minimum speed is reached, the fault performance signals at equal angle intervals are collected as reference axis analysis data; rpm When the preset maximum speed is reached, the acquisition ends.

5. A rotating machinery fault diagnosis method according to claim 1, characterized in that: Based on the speed signal and the speed ratio of the reference shaft to the non-reference shaft, the reference shaft analysis data is interpolated to obtain the non-reference shaft analysis data, including: After each resampling of the reference axis, the number of resamplings of the non-reference axis between the current resampling point and the previous resampling point of the reference axis is calculated in combination with the speed ratio; Perform linear interpolation on the two resampling point times of the reference axis to obtain the resampling time of the non-reference axis; select the sampling point closest to the resampling time of the non-reference axis from the sampling data as the non-reference axis analysis data; The non-reference axis resampling points are calculated based on the following formula: ； ； in, is the sequence number of the non-reference axis resampling; The first resampled non-reference axis points; Indicates the serial number of the signal collected by the current signal collection device; is the coefficient of the recursive relation for non-reference axis resampling; is the speed ratio of the non-reference shaft to the reference shaft.

6. A rotating machinery fault diagnosis method according to claim 1, characterized in that: The method of obtaining the gear position where the fault occurs by performing synchronous channel analysis, mixed channel analysis and synchronous channel analysis on the reference shaft analysis data, includes: The synchronous channel analysis includes: The reference axis analysis data and the non-reference axis analysis data are divided into different slice data, each slice reflecting the rotation of the axis within the time period of collecting the slice data; Calculate the average value of multiple slice data of the rotation axis; The averaged slice data is subjected to fast Fourier transformation, and then the autospectral linear spectrum is calculated to obtain the synchronous channel analysis results; The mixed channel analysis comprises: The reference axis analysis data is divided into different slice data, which are consistent with the reference axis slice data of the synchronous channel; Perform fast Fourier transform on all current slice data, calculate the self-spectrum linear spectrum, and take the maximum value of all self-spectra as the mixed channel analysis result.

7. A rotating machinery fault diagnosis method according to claim 6, characterized in that: The average value of multiple slice data of the rotating axis in the synchronous channel analysis is calculated based on the following formula : ； in, For the Signal segment individual data; Indicates the total number of signal segments; The averaged slice data is fast Fourier transformed based on the following formula : ； in, After Fourier transformation frequency components; In the time domain The signal mean of the sampling points; is the phase relationship of different frequency components; is the first frequency components, 0≤k≤N-1; is the total length of the signal; The synchronous channel analysis results are calculated based on the following formula: ； in, It is the result of synchronous channel analysis; The mixed channel analysis comprises: Based on the calculation of the autospectral linear spectrum of all current signal segment data: ； in, For the After Fourier transformation of the signal segment frequency components; For the Signal segment individual data; is the phase relationship of different frequency components; is the first frequency components, 0≤k≤ ; is the total length of the signal; Perform mixed channel calculation on the autospectral linear spectrum calculation result: ； in, is the analysis result of the mixed channel; is the total number of signal segments.

8. A rotating machinery fault diagnosis system, characterized in that: The system comprises: A data acquisition module is configured to acquire sampled data; the sampled data includes a speed pulse signal and a fault manifestation signal; A resampling module, configured to resample the sampled data into a rotation speed pulse signal and a fault manifestation signal at equal angle intervals; A fitting module is configured to perform speed fitting on the pulse signals with equal angle intervals to obtain a speed signal; an analysis data module configured to select fault manifestation signals with equal angle intervals as reference axis analysis data based on the rotation speed signal; and interpolate the reference axis analysis data to obtain non-reference axis analysis data based on the rotation speed signal and the rotation speed ratio of the reference axis to the non-reference axis; The analysis module is configured to obtain the gear position where the fault occurs by performing synchronous channel analysis, mixed channel analysis and synchronous channel analysis on the reference shaft analysis data and the non-reference shaft analysis data.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and when the instructions are executed by the at least one processor, the at least one processor executes a rotating machinery fault diagnosis method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes a rotating machinery fault diagnosis method as described in any one of claims 1 to 7.

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