A bearing gear fault automatic diagnosis method, system, device and storage medium

Abstract

The application provides a bearing gear fault automatic diagnosis method, system, equipment and storage medium, the method comprises the following steps: obtaining a sampling frequency through a spectrum sensing sampling frequency convex optimization algorithm; obtaining the installation number of a signal sensitive sensor through a multi-factor weighted constraint scaling algorithm; obtaining a signal to be processed according to the sampling frequency and the installation number of the signal sensitive sensor, processing the signal to be processed, and obtaining a fault diagnosis result; the bearing gear fault automatic diagnosis method solves the problem of monitoring the inventory equipment; when processing the signal, a hardware pulse is used as a sampling clock source, software interruption and the delay and jitter of calculation are avoided, high-precision synchronous sampling is realized, the non-stationary phenomenon of the signal caused by speed fluctuation is eliminated, and the signal analysis precision is improved; speed measurement and sampling frequency adjustment are completed within one pulse period, the response is fast, and high-speed and high-dynamic speed changes can be tracked; the bearing gear fault automatic diagnosis method has a wide application range and high practicability.

Description

Technical Field

[0001] This invention relates to the field of equipment condition monitoring and predictive maintenance technology, and in particular to an automatic diagnosis method, system, device and storage medium for bearing and gear faults. Background Technology

[0002] Bearings and gears are the most critical and vulnerable components in rotating machinery. Currently, there are two main methods for monitoring them:

[0003] Fixed online monitoring systems require pre-installation. For existing equipment already in operation, retrofitting or modifying them later is difficult and extremely costly, limiting their application scope.

[0004] Handheld portable vibration meters: Although flexible, they have limited functionality, the analysis process relies on the operator's experience, it is difficult to accurately capture early fault characteristics, and it cannot achieve automated and accurate diagnosis.

[0005] Therefore, how to perform rapid and non-destructive automatic diagnosis of bearings and gear components in operating equipment is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] To address the aforementioned technical problems, the present invention aims to provide an automatic diagnosis method, system, device, and storage medium for bearing and gear faults. This method requires no permanent modifications to existing equipment, can be quickly deployed, and automatically completes the entire process from sensor configuration and signal acquisition to fault diagnosis, significantly reducing the technical threshold and implementation costs, thereby enabling rapid and non-destructive automatic diagnosis of bearings and gear components in already operational equipment.

[0007] The first objective of this invention is to provide an automatic method for diagnosing bearing and gear faults;

[0008] The technical solution provided by this invention is as follows:

[0009] An automatic diagnosis method for bearing and gear faults includes the following steps:

[0010] The sampling frequency is obtained through a spectrum-aware sampling frequency convex optimization algorithm.

[0011] The number of signal-sensitive sensors installed is obtained through a multi-factor weighted constraint scaling algorithm;

[0012] The signal to be processed is obtained based on the sampling frequency and the number of signal-sensitive sensors installed, and the signal to be processed is processed to obtain the fault diagnosis result.

[0013] Preferably, obtaining the sampling frequency through the spectral sensing sampling frequency convex optimization algorithm specifically includes:

[0014] Input multimodal spectral sensing parameters;

[0015] Multiple constraints are constructed based on the multimodal spectrum sensing parameters;

[0016] The sampling frequency fs is obtained based on the multiple constraints.

[0017] Preferably, the multimodal spectrum sensing parameters include: rotating machinery dynamic parameters and hardware constraint parameters; wherein, the rotating machinery dynamic parameters include: shaft rotation frequency frot, gear meshing frequency fmesh, and bearing fault characteristic frequency set Fbearing; the hardware constraint parameters include: discrete sampling frequency set Fhw.

[0018] Preferably, the step of constructing multiple constraint conditions based on the multimodal spectral sensing parameters specifically includes:

[0019] The bearing fault analysis requirements are obtained based on the bearing fault characteristic frequency set Fbearing.

[0020] The gear fault analysis requirements are obtained based on the gear meshing frequency fmesh.

[0021] The target analysis frequency is determined based on the bearing failure analysis requirements and the gear failure analysis requirements to form physical mechanism constraints;

[0022] An aliasing constraint is formed based on the target analysis frequency and the sampling frequency fs;

[0023] Hardware discrete constraints are formed based on the discrete sampling frequency set Fhw and the sampling frequency fs.

[0024] Preferably, obtaining the sampling frequency fs based on the multiple constraint conditions specifically includes:

[0025] The globally optimal sampling frequency fs is obtained based on the physical mechanism constraints, the aliasing constraints, and the hardware discrete constraints, as follows:

[0026] Minimize fs

[0027] Subject to

[0028]

[0029] Fhw;

[0030] Where Minimize represents the minimum value that satisfies the constraints; Subject to represents the target requirement; denoted by , which represents the sampling coefficients that satisfy the sampling theorem to prevent signal aliasing; 'a' is the bearing harmonic safety factor; 'b' is the gear harmonic safety factor.

[0031] Preferably, the step of acquiring the signal to be processed through a signal-sensitive sensor according to the sampling frequency specifically includes:

[0032] The bearing gear to be monitored is subjected to signal sensitivity by mounting a signal-sensitive sensor on the stationary surface of the bearing gear.

[0033] Preferably, obtaining the number of signal-sensitive sensors installed using a multi-factor weighted constraint scaling algorithm specifically includes:

[0034] Input the number of bearings to be monitored, N_b, and the number of gears to be monitored, N_g;

[0035] Input scaling factors, wherein the scaling factors include: scaling factor C1 for the number of bearings of the same type.

[0036] Scaling factor C2 for the number of gears of the same type, scaling factor C3 for system complexity, scaling factor C4 for bearing signal transmission complexity, and scaling factor C5 for gear signal transmission complexity;

[0037] The number of signal-sensitive sensors Num to be installed is obtained based on the number of bearings N_b to be monitored, the number of gears N_g to be monitored, and the scaling factor. The specific calculation method for the number of installations Num is as follows:

[0038] Num=ceil((N_b×C1×C4+N_g×C2×C5)×C3).

[0039] Preferably, the processing of the signal to be processed to obtain fault diagnosis results specifically includes:

[0040] The signals to be processed include: rotation speed signals and vibration signals, wherein the rotation speed signals include rotation speed pulse signals, network rotation speed signals, and software input rotation speed signals;

[0041] The speed signal is processed as follows:

[0042] Prioritize detecting the hardware pulse channel and then make a judgment on the pulse channel:

[0043] If a stable and effective rotational speed pulse signal is detected within a preset time, the pulse tracking mode will be entered automatically.

[0044] If the pulse channel is invalid, then check if the network speed signal exists. If it exists, identify and parse the network speed value. If it does not exist, automatically switch to the software input speed signal.

[0045] Preferably, the pulse tracking mode specifically includes:

[0046] Multiple pulse signals are acquired using the signal-sensitive sensor;

[0047] The rising edge of each pulse is captured by a high-precision counter, and the time interval between two consecutive rising edges is accurately measured.

[0048] The instantaneous rotational speed is obtained based on the time interval;

[0049] The instantaneous rotational frequency is obtained based on the instantaneous rotational speed;

[0050] The resampling clock pulse sequence is obtained based on the instantaneous frequency conversion;

[0051] The resampling clock pulse sequence is used as an external sampling clock to trigger the analog-to-digital converter to synchronously acquire the analog signal from the vibration acceleration sensor in order to obtain a stationary signal in the angular domain.

[0052] Preferably, the step of processing the signal to be processed to obtain fault diagnosis results further includes:

[0053] The vibration signal is processed to obtain a vibration impact signal.

[0054] Preferably, the processing of the signal to be processed to obtain fault diagnosis results specifically includes:

[0055] The bearings and gears are diagnosed based on the angular domain stable signal and the vibration and shock signal to obtain the fault diagnosis results.

[0056] The second objective of this invention is to provide an automatic diagnostic system for bearing and gear faults;

[0057] The technical solution provided by this invention is as follows:

[0058] An automatic bearing and gear fault diagnosis system includes: a sampling frequency acquisition module, an installation quantity acquisition module, and a fault diagnosis module;

[0059] The sampling frequency acquisition module is used to acquire the sampling frequency through a spectrum-aware sampling frequency convex optimization algorithm;

[0060] The installation quantity acquisition module is used to obtain the installation quantity of signal sensitive sensors through a multi-factor weighted constraint scaling algorithm;

[0061] The fault diagnosis module is used to acquire the signal to be processed based on the sampling frequency and the number of installed signal sensitive sensors, and to process the signal to be processed in order to obtain the fault diagnosis result.

[0062] The third objective of this invention is to provide an electronic device;

[0063] The technical solution provided by this invention is as follows:

[0064] An electronic device, comprising:

[0065] At least one processor; and

[0066] A memory communicatively connected to the at least one processor, the memory storing a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method steps of any one of the automatic diagnosis methods for bearing gear faults.

[0067] A fourth objective of this invention is to provide a computer-readable storage medium;

[0068] The technical solution provided by this invention is as follows:

[0069] A computer-readable storage medium for storing a computer program for causing a computer to perform the steps of any one of the methods for automatic diagnosis of bearing gear faults.

[0070] Compared with existing technologies, the present invention provides an automatic bearing and gear fault diagnosis method, comprising the following steps: obtaining a sampling frequency through a spectral sensing sampling frequency convex optimization algorithm; obtaining the number of signal-sensitive sensors installed through a multi-factor weighted constraint scaling algorithm; obtaining a signal to be processed based on the sampling frequency and the number of signal-sensitive sensors installed; processing the signal to be processed to obtain a fault diagnosis result; this automatic bearing and gear fault diagnosis method solves the problem of monitoring existing equipment, thereby achieving non-destructive, rapid retrofitting and no need for wiring modifications; and the cost of a single diagnosis is extremely low, avoiding huge fixed investments; at the same time, it directly utilizes the signal processing... Using a hardware pulse as the sampling clock source avoids delays and jitter caused by software interrupts and calculations, achieving high-precision synchronous sampling, eliminating signal non-stationarity caused by speed fluctuations, and improving signal analysis accuracy. Speed ​​measurement and sampling frequency adjustment are completed within one pulse cycle, with extremely fast response speed, perfectly tracking high-speed, high-dynamic speed changes. Moreover, this method is adaptable to three speed scenarios: speed pulse, network speed, and software input, with a wide range of applications and strong engineering practicality. In addition, this method provides redundant backup for speed signal acquisition; if one signal source fails, the system can still continue to work through another method, ensuring the smooth completion of diagnostic tasks.

[0071] The present invention also provides an automatic diagnosis system for bearing and gear faults. Since this system and the automatic diagnosis method for bearing and gear faults solve the same technical problems and belong to the same technical concept, they should have the same beneficial effects, and will not be described in detail here. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 A flowchart of an automatic diagnosis method for bearing gear faults is provided as one embodiment;

[0074] Figure 2 A schematic diagram of the structure of an automatic bearing and gear fault diagnosis method system provided in one embodiment;

[0075] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0076] To enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0077] like Figure 1 As shown in the figure, an embodiment of the present invention provides an automatic diagnosis method for bearing gear faults, including the following steps:

[0078] S1. Obtain the sampling frequency using a spectrum-aware sampling frequency convex optimization algorithm;

[0079] S2. Obtain the number of signal-sensitive sensors to be installed using a multi-factor weighted constraint scaling algorithm;

[0080] S3. Obtain the signal to be processed according to the sampling frequency and the number of signal sensitive sensors installed, process the signal to be processed, and obtain the fault diagnosis result.

[0081] In steps S1 to S3, the sampling frequency is obtained through a spectral sensing sampling frequency convex optimization algorithm; then, the number of signal-sensitive sensors installed is obtained through a multi-factor weighted constraint scaling algorithm; finally, the signal to be processed is obtained based on the sampling frequency and the number of signal-sensitive sensors installed, and the signal to be processed is processed to obtain the fault diagnosis result. This method solves the problem of monitoring existing equipment, thereby achieving non-destructive, rapid post-installation and no wiring modification required; and the cost of a single diagnosis is extremely low, avoiding huge fixed investments; at the same time, hardware pulses are directly used as the sampling clock source during signal processing, avoiding delays and jitter caused by software interruptions and calculations, achieving high-precision synchronous sampling, eliminating the non-stationary phenomenon of signals caused by speed fluctuations, and improving the accuracy of signal analysis; speed measurement and sampling frequency adjustment are completed within one pulse cycle, with extremely fast response speed, and can perfectly track high-speed, high-dynamic speed changes; and this method can adapt to three speed scenarios: speed pulse, network speed, and software input, with a wide range of applications and strong engineering practicality. In addition, this method provides redundant backup for speed signal acquisition, so that when one signal source fails, the system can still continue to work in another way, ensuring the smooth completion of the diagnostic task.

[0082] Preferably, obtaining the sampling frequency through the spectral sensing sampling frequency convex optimization algorithm specifically includes:

[0083] Input multimodal spectral sensing parameters;

[0084] Multiple constraints are constructed based on the multimodal spectrum sensing parameters;

[0085] The sampling frequency fs is obtained based on the multiple constraints.

[0086] In practical applications, obtaining the sampling frequency through the convex optimization algorithm of spectrum sensing sampling frequency involves the following steps: Inputting multimodal spectrum sensing parameters, which include rotating machinery dynamics parameters and hardware constraint parameters; the rotating machinery dynamics parameters specifically include: shaft rotation frequency frot, gear meshing frequency fmesh, and bearing fault characteristic frequency set Fbearing = {fb, fw, fnei, fg}; the hardware constraint parameters include: the discrete sampling frequency set supported by the data acquisition system, discrete sampling frequency set Fhw = {fhw1, fhw2, ..., fhwn}; then defining the optimization problem, in this embodiment, with the decision variable: sampling frequency fs; and the objective function: minimize fs; then constructing multiple constraints based on the above multimodal spectrum sensing parameters; and then integrating the above steps into a constraint optimization problem based on these multiple constraints, solving it, and finally outputting the globally optimal sampling frequency fs.

[0087] Specifically, consider a device comprising three shafts: one gear on each of the input and output shafts, and two gears on the intermediate shaft, for a total of four gears, all of which are of different models. Each shaft is supported by two bearings, for a total of six bearings, all of which are of different models.

[0088] 1. Input multimodal spectrum sensing

[0089] ① Dynamic parameters of rotating machinery:

[0090] Shaft rotation frequency frot = 30000 / 60 = 500Hz;

[0091] The characteristic frequency set of bearing failure is Fbearing={110,1500,1800,1300};

[0092] Gear meshing frequency fmesh = frot × 20 = 10000 Hz;

[0093] ② Hardware constraint parameters: The set of discrete sampling frequency ranges supported by the data acquisition system is Fhw={10240,25600,51200,102400};

[0094] 2. Define the optimization problem

[0095] Decision variable: sampling frequency fs; Objective function: minimize fs;

[0096] 3. Construct multiple constraints;

[0097] 4. Solve the optimization problem by integrating the above steps into a constrained optimization problem:

[0098] Minimize fs;

[0099] subject to ;

[0100] ;

[0101] Find the global optimal solution:

[0102] 10240 Hz < 90000 Hz → obsolete;

[0103] 25600 Hz < 90000 Hz → obsolete;

[0104] 51200 Hz < 90000 Hz → obsolete;

[0105] 102400 Hz > 90000 Hz → All constraints are satisfied;

[0106] 5. Algorithm output: Global optimal sampling frequency fs = 102400Hz.

[0107] Preferably, the step of constructing multiple constraint conditions based on the multimodal spectral sensing parameters specifically includes:

[0108] The bearing fault analysis requirements are obtained based on the bearing fault characteristic frequency set Fbearing.

[0109] The gear fault analysis requirements are obtained based on the gear meshing frequency fmesh.

[0110] The target analysis frequency is determined based on the bearing failure analysis requirements and the gear failure analysis requirements to form physical mechanism constraints;

[0111] An aliasing constraint is formed based on the target analysis frequency and the sampling frequency fs;

[0112] Hardware discrete constraints are formed based on the discrete sampling frequency set Fhw and the sampling frequency fs.

[0113] In practical applications, multiple constraints are constructed based on multimodal spectral sensing parameters:

[0114] (1) Physical mechanism constraints

[0115] Based on the analysis objectives, the candidate highest analysis frequencies required for different failure modes are calculated in parallel:

[0116] Based on the bearing failure characteristic frequency set Fbearing, the bearing failure analysis requirements are calculated. The calculation formula is:

[0117] ;

[0118] Where 'a' is the bearing harmonic safety factor;

[0119] Gear fault analysis requirements are calculated based on the gear meshing frequency fmesh. The calculation formula is:

[0120] ;

[0121] Where b is the gear harmonic safety factor;

[0122] The final maximum system analysis frequency is calculated based on the requirements for bearing fault analysis and gear fault analysis. The calculation formula is:

[0123] ;

[0124] This step ensures that the algorithm can automatically adapt to "multiple vibration source competition" and select the most demanding analysis requirements as the design benchmark.

[0125] (2) Aliasing constraint:

[0126] in This represents the sampling coefficients that satisfy the sampling theorem, preventing signal aliasing.

[0127] (3) Hardware discrete constraints: Fhw.

[0128] Specifically, consider a device comprising three shafts: one gear on each of the input and output shafts, and two gears on the intermediate shaft, for a total of four gears, all of which are of different models. Each shaft is supported by two bearings, for a total of six bearings, all of which are of different models.

[0129] Constructing multiple constraints:

[0130] Bearing fault analysis requirements: ;

[0131] Gear fault analysis requirements: ;

[0132] Determine the final system's highest analysis frequency:

[0133] ;

[0134] aliasing constraints: ;

[0135] Hardware discrete constraints: .

[0136] Preferably, obtaining the sampling frequency fs based on the multiple constraints specifically includes: obtaining the globally optimal sampling frequency fs based on the physical mechanism constraints, the aliasing constraints, and the hardware discrete constraints, as follows:

[0137] Minimize fs

[0138] Subject to

[0139]

[0140] Fhw;

[0141] Here, Minimize represents the minimum value that satisfies the constraints; Subject to represents the target requirement.

[0142] In practical applications, the above steps are integrated into a constrained optimization problem, and the globally optimal sampling frequency fs is solved through the above calculation process.

[0143] Preferably, the step of acquiring the signal to be processed through a signal-sensitive sensor according to the sampling frequency specifically includes:

[0144] The bearing gear to be monitored is subjected to signal sensitivity by mounting a signal-sensitive sensor on the stationary surface of the bearing gear.

[0145] In practical applications, to make the bearing gear to be monitored sensitive to signals, the number of signal-sensitive sensors to be installed is first calculated by using a multi-factor weighted constraint scaling algorithm. Then, the signal-sensitive sensors, typically vibration acceleration sensors and speed sensors, are installed on its stationary surface.

[0146] Preferably, obtaining the number of signal-sensitive sensors installed using a multi-factor weighted constraint scaling algorithm specifically includes:

[0147] Input the number of bearings to be monitored, N_b, and the number of gears to be monitored, N_g;

[0148] Input scaling factors, wherein the scaling factors include: scaling factor C1 for the number of bearings of the same type.

[0149] Scaling factor C2 for the number of gears of the same type, scaling factor C3 for system complexity, scaling factor C4 for bearing signal transmission complexity, and scaling factor C5 for gear signal transmission complexity;

[0150] The number of signal-sensitive sensors Num to be installed is obtained based on the number of bearings N_b to be monitored, the number of gears N_g to be monitored, and the scaling factor. The specific calculation method for the number of installations Num is as follows:

[0151] Num=ceil((N_b×C1×C4+N_g×C2×C5)×C3).

[0152] In practical applications, a multi-factor weighted constraint scaling algorithm is used to determine the number of vibration acceleration sensors Num to be installed. The specific process is as follows:

[0153] Take a device containing 3 shafts: one gear on each of the input and output shafts, and two gears on the intermediate shaft, for a total of 4 gears, all of which are different models. Each shaft is supported by 2 bearings, for a total of 6 bearings, all of which are different models.

[0154] Input parameters: N_b = 6, N_g = 4;

[0155] The scaling factor can generally be determined empirically, and the value of each scaling factor ranges from 0 to 1, including 0 and 1.

[0156] The scaling factor C1 for the number of bearings of the same type is 1. If there are bearings of the same type, it is recommended that the value be less than 1.

[0157] The scaling factor C2 for the number of gears of the same type is 1. If there are gears of the same type, it is recommended that the value be less than 1.

[0158] The system complexity scaling factor C3 = 0.6; the more complex the system, the larger the value.

[0159] The bearing signal transmission complexity scaling factor C4=1, and the larger the value is as the signal transmission becomes more difficult.

[0160] The gear signal transmission complexity scaling factor C5=1, and the larger the value is as the signal transmission becomes more difficult.

[0161] Calculate the number of vibration acceleration sensors, Num:

[0162] Num=ceil((N_b×C1×C4+N_g×C2×C5) ×C3)= ceil((6×1×1+4×1×1)×0.6)=6;

[0163] Specifically, rotational speed is one of the key parameters for the operation of bearings and gears, and it is also an important physical quantity for fault diagnosis. Since it is also necessary to determine the sensitivity mode of the rotational speed signal, in this method, the rotational speed pulse signal is preferentially connected, that is, the rotational speed signal of several (which can be 1 or a decimal) pulse waveforms generated per revolution. When there is no rotational speed pulse signal, the network speed can be connected, or it can be not connected.

[0164] Preferably, the processing of the signal to be processed to obtain fault diagnosis results specifically includes:

[0165] The signals to be processed include: rotation speed signals and vibration signals, wherein the rotation speed signals include rotation speed pulse signals, network rotation speed signals, and software input rotation speed signals;

[0166] The speed signal is processed as follows:

[0167] Prioritize detecting the hardware pulse channel and then make a judgment on the pulse channel:

[0168] If a stable and effective rotational speed pulse signal is detected within a preset time, the pulse tracking mode will be entered automatically.

[0169] If the pulse channel is invalid, then check if the network speed signal exists. If it exists, identify and parse the network speed value. If it does not exist, automatically switch to the software input speed signal.

[0170] In practical applications, the signals are processed. The signals include at least rotation speed signals and vibration signals. The rotation speed signals can be rotation speed pulse signals, network rotation speed signals, or software input rotation speed signals. The rotation speed pulse signals are generally pulse sequences from photoelectric / magnetoelectric rotation speed sensors. The network rotation speed signals are rotation speed values ​​transmitted over the network at regular communication cycles. The software input rotation speed signals refer to the rated rotation speed values ​​manually input by the user.

[0171] After the device under inspection is powered on, the hardware pulse channel is detected first by default; if a stable and effective pulse signal is detected within a preset time, it will automatically enter the "pulse tracking mode".

[0172] If the pulse channel is invalid (no signal, signal amplitude too low, frequency out of range), then check if the network speed exists. If it exists, identify and parse the network speed value. If it does not exist, the system automatically switches to the software input speed signal and prompts the user to input the known rated speed value through the software interface.

[0173] Preferably, the pulse tracking mode specifically includes:

[0174] Multiple pulse signals are acquired using the signal-sensitive sensor;

[0175] The rising edge of each pulse is captured by a high-precision counter, and the time interval between two consecutive rising edges is accurately measured.

[0176] The instantaneous rotational speed is obtained based on the time interval;

[0177] The instantaneous rotational frequency is obtained based on the instantaneous rotational speed;

[0178] The resampling clock pulse sequence is obtained based on the instantaneous frequency conversion;

[0179] The resampling clock pulse sequence is used as an external sampling clock to trigger the analog-to-digital converter to synchronously acquire the analog signal from the vibration acceleration sensor in order to obtain a stationary signal in the angular domain.

[0180] In practical applications, the pulse tracking mode employs a hardware-in-the-loop real-time time-based synchronous tracking algorithm. The purpose of this method is to convert the non-stationary time-domain signal a(t) (the non-stationarity is due to changes in rotational speed) into a stationary angular-domain signal a(θ), laying the foundation for accurate analysis. Specifically:

[0181] 1. By using a signal-sensitive sensor, specifically a speed sensor such as a photoelectric, magnetoelectric, or laser sensor, the P pulse signals (usually square waves or sine waves) generated per revolution are collected by aligning the key phase mark on the rotating shaft or the gear tooth groove. This pulse sequence is the time base source for synchronizing the entire system; alternatively, it can be a signal output from a certain channel after paralleling multiple channels of an existing speed sensor.

[0182] 2. The rising edge of each pulse is captured by a high-precision counter on the data acquisition card, and the time interval ΔT (unit: seconds) between two consecutive rising edges is accurately measured.

[0183] 3. Calculate the instantaneous rotational speed R_inst based on the time interval ΔT. The calculation formula is as follows:

[0184] R_inst=60 / (P*ΔT) (unit: RPM, revolutions per minute);

[0185] 4. Calculate the instantaneous rotational frequency f_inst based on the instantaneous rotational speed R_inst. The calculation formula is as follows:

[0186] f_inst = R_inst / 60 (unit: Hz, revolutions per second);

[0187] 5. Resampling Pulse Generation: Based on the user-defined number of sampling points N per revolution, dynamically calculate and generate a resampling clock pulse sequence f_s_angular with equal angular intervals. The calculation formula is as follows:

[0188] f_s_angular = P * f_inst * N (unit: Hz);

[0189] It is important to note that N data points should be collected evenly within each rotation cycle, which means that the 360-degree mechanical angle is divided into N equal parts.

[0190] 6. The acquisition card uses the resampling pulse sequence f_s_angular as the external sampling clock to trigger the ADC (analog-to-digital converter) to synchronously acquire the analog signal a(t) from the vibration acceleration sensor. Since the sampling interval is equal in angle, the stationary signal a(θ) in the angular domain is directly obtained. At this time, any periodic component in the signal is strictly locked with the shaft rotation angle, thus realizing speed tracking sampling, which facilitates subsequent bearing and gear fault diagnosis. In this embodiment, hardware pulses are directly used as the sampling clock source, avoiding the delay and jitter caused by software interrupts and calculations, achieving high-precision synchronous sampling, eliminating the signal non-stationarity caused by speed fluctuations, and improving the accuracy of signal analysis. Speed ​​measurement and sampling frequency adjustment are completed within one pulse cycle, with extremely fast response speed, which can perfectly track high-speed and high-dynamic speed changes.

[0191] Preferably, the step of processing the signal to be processed to obtain fault diagnosis results further includes:

[0192] The vibration signal is processed to obtain a vibration impact signal.

[0193] In practical applications, vibration processing includes: preprocessing the vibration signal to identify its validity; and when rotational speed pulses are present, using the aforementioned hardware-in-the-loop real-time time-base synchronization tracking algorithm to track and sample the vibration signal.

[0194] Perform time-domain analysis on the vibration signal and extract the corresponding time-domain feature values;

[0195] Frequency domain analysis is performed on the vibration signal to extract the corresponding frequency domain feature values;

[0196] The vibration signal is subjected to generalized resonance demodulation processing to extract the vibration and impact signal.

[0197] Preferably, the processing of the signal to be processed to obtain fault diagnosis results specifically includes:

[0198] The bearings and gears are diagnosed based on the angular domain stable signal and the vibration and shock signal to obtain the fault diagnosis results.

[0199] In practical applications, fault diagnosis of bearings and gears is performed based on angular domain steady-state signals and vibration and shock signals to obtain fault diagnosis results. The diagnosis results are presented in a combination of text and graphics, directly giving a clear conclusion of "bearing / gear model - fault type - severity".

[0200] like Figure 2 As shown, this embodiment of the invention provides an automatic bearing and gear fault diagnosis system, including: a sampling frequency acquisition module, an installation quantity acquisition module, and a fault diagnosis module;

[0201] The sampling frequency acquisition module is used to acquire the sampling frequency through a spectrum-aware sampling frequency convex optimization algorithm;

[0202] The installation quantity acquisition module is used to obtain the installation quantity of signal sensitive sensors through a multi-factor weighted constraint scaling algorithm;

[0203] The fault diagnosis module is used to acquire the signal to be processed based on the sampling frequency and the number of installed signal sensitive sensors, and to process the signal to be processed in order to obtain the fault diagnosis result.

[0204] In practical applications, the automatic bearing and gear fault diagnosis system includes a sampling frequency acquisition module, an installation quantity acquisition module, and a fault diagnosis module. The fault diagnosis module is connected to both the sampling frequency acquisition module and the installation quantity acquisition module. The sampling frequency acquisition module obtains the sampling frequency using a spectral sensing sampling frequency convex optimization algorithm and then transmits it to the fault diagnosis module. The installation quantity acquisition module obtains the installation quantity of signal-sensitive sensors using a multi-factor weighted constraint scaling algorithm and then transmits it to the fault diagnosis module. The fault diagnosis module then obtains the signal to be processed based on the sampling frequency and the installation quantity of the signal-sensitive sensors, processes the signal, and obtains the fault diagnosis result. This system, through the coordinated operation of the sampling frequency acquisition module, the installation quantity acquisition module, and the fault diagnosis module, solves... This system addresses the challenges of monitoring existing equipment, enabling non-destructive and rapid retrofitting without the need for wiring modifications. Furthermore, it boasts extremely low single-diagnosis costs, avoiding substantial fixed investments. During signal processing, it directly utilizes hardware pulses as the sampling clock source, eliminating delays and jitter caused by software interruptions and calculations. This achieves high-precision synchronous sampling, eliminating signal non-stationarity caused by speed fluctuations and improving signal analysis accuracy. Speed ​​measurement and sampling frequency adjustment are completed within one pulse cycle, resulting in extremely fast response and perfect tracking of high-speed, highly dynamic speed changes. The system is adaptable to three speed scenarios: speed pulse, network speed, and software input, offering a wide range of applications and strong engineering practicality. Moreover, the system provides redundant backup for speed signal acquisition; if one signal source fails, the system can continue operating through another method, ensuring the successful completion of diagnostic tasks.

[0205] Specifically, in this embodiment, the automatic bearing and gear fault diagnosis system can be physically composed of three main parts: a signal sensing end, a signal acquisition and processing end, and a diagnosis and display end. Alternatively, the signal acquisition and processing end and the diagnosis and display end can be combined into one entity, such as a portable computer, integrating both functions. The workflow of this automatic bearing and gear fault diagnosis system is as follows:

[0206] Deployment: Install the sensor. The sensor can be connected by adhesive or bolts. When bolting, an additional adapter is usually required, with one end connected to the equipment surface and the other end connected to the sensor. Connection: Connect the sensor and other components such as signal acquisition, diagnostic display, etc.

[0207] Startup: Click "Start Sampling" on the software terminal, and the system will start working and automatically output diagnostic conclusions.

[0208] Termination and Disassembly: Click "End" on the software terminal to terminate the system and output the current diagnostic conclusion. Remove the sensor, collect the relevant cables and instruments, and move to the next device.

[0209] This system is adaptable to three speed scenarios: speed pulse, network speed, and software input. It has a wide range of applications, strong engineering practicality, and is fast and efficient throughout the entire process. Furthermore, the aforementioned automatic bearing and gear fault diagnosis method provides redundant backup for speed signal acquisition. Even if one signal source fails, the system can continue operating through another method, ensuring the successful completion of the diagnostic task.

[0210] Furthermore, embodiments of this application also disclose an electronic device, Figure 3 This is a structural diagram of an electronic device according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0211] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the automatic bearing gear fault diagnosis method disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0212] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create an automatic diagnosis channel for bearing and gear faults between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0213] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222 and data 223, etc., and the storage method can be temporary storage or permanent storage.

[0214] The operating system 221 manages and controls the various hardware devices on the electronic device 20 and the computer program 222 to enable the processor 21 to perform calculations and processing on the data 223 in the memory 22. It can be Windows Server, Netware, Unix, Linux, etc. The computer program 222, in addition to including a computer program capable of performing the automatic bearing and gear fault diagnosis method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, may further include computer programs capable of performing other specific tasks. The data 223 may include data received by the automatic bearing and gear fault diagnosis device from external devices, as well as data collected by its own input / output interface 25.

[0215] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0216] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned automatic bearing and gear fault diagnosis method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0217] It should be understood that the use of terms such as "method," "apparatus," "unit," and / or "module" in this application is merely to distinguish one method of different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0218] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "a," and / or "the" are not specifically singular and may include the plural. Generally, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements. An element defined by the phrase "comprising an..." does not exclude the presence of other identical elements in the process, method, product, or apparatus that includes the element.

[0219] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0220] If a flowchart is used in this application, it is used to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0221] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An automatic diagnosis method for bearing and gear faults, characterized in that, Includes the following steps: The sampling frequency is obtained through a spectrum-aware sampling frequency convex optimization algorithm. The number of signal-sensitive sensors installed is obtained through a multi-factor weighted constraint scaling algorithm; The signal to be processed is obtained based on the sampling frequency and the number of signal-sensitive sensors installed, and the signal to be processed is processed to obtain the fault diagnosis result; The process of obtaining the sampling frequency through a spectrum-aware sampling frequency convex optimization algorithm specifically includes: Input multimodal spectrum sensing parameters, wherein the multimodal spectrum sensing parameters include: rotating machinery dynamic parameters and hardware constraint parameters; Multiple constraints are constructed based on the multimodal spectrum sensing parameters; The sampling frequency fs is obtained based on the multiple constraints. The method of obtaining the number of signal-sensitive sensors installed using a multi-factor weighted constraint scaling algorithm specifically includes: Input the number of bearings to be monitored, N_b, and the number of gears to be monitored, N_g; Input scaling factors, wherein the scaling factors include: scaling factor C1 for the number of bearings of the same type. Scaling factor C2 for the number of gears of the same type, scaling factor C3 for system complexity, scaling factor C4 for bearing signal transmission complexity, and scaling factor C5 for gear signal transmission complexity; The number of signal-sensitive sensors Num to be installed is obtained based on the number of bearings N_b to be monitored, the number of gears N_g to be monitored, and the scaling factor. The specific calculation method for the number of installations Num is as follows: Num=ceil((N_b×C1×C4+N_g×C2×C5)×C3); The step of acquiring the signal to be processed through a signal-sensitive sensor according to the sampling frequency specifically includes: The bearing gear to be monitored is subjected to signal sensitivity by installing a signal-sensitive sensor on the stationary surface of the bearing gear to obtain the signal to be processed.

2. The automatic bearing and gear fault diagnosis method according to claim 1, characterized in that, The rotating machinery dynamics parameters include: shaft rotation frequency frot, gear meshing frequency fmesh, and bearing fault characteristic frequency set Fbearing; the hardware constraint parameters include: discrete sampling frequency set Fhw.

3. The automatic bearing and gear fault diagnosis method according to claim 2, characterized in that, The construction of multiple constraint conditions based on the multimodal spectral sensing parameters specifically includes: The bearing fault analysis requirements are obtained based on the bearing fault characteristic frequency set Fbearing. The gear fault analysis requirements are obtained based on the gear meshing frequency fmesh. The target analysis frequency is determined based on the bearing failure analysis requirements and the gear failure analysis requirements to form physical mechanism constraints; An aliasing constraint is formed based on the target analysis frequency and the sampling frequency fs; Hardware discrete constraints are formed based on the discrete sampling frequency set Fhw and the sampling frequency fs.

4. The automatic bearing and gear fault diagnosis method according to claim 3, characterized in that, The step of obtaining the sampling frequency fs based on the multiple constraints specifically includes: The globally optimal sampling frequency fs is obtained based on the physical mechanism constraints, the aliasing constraints, and the hardware discrete constraints, as follows: Minimize fs; Subject to ; ； Phew; Where Minimize represents the minimum value that satisfies the constraints; Subject to represents the target requirement; denoted by , which represents the sampling coefficients that satisfy the sampling theorem to prevent signal aliasing; 'a' is the bearing harmonic safety factor; 'b' is the gear harmonic safety factor.

5. The automatic bearing and gear fault diagnosis method according to claim 1, characterized in that, The process of processing the signal to be processed to obtain fault diagnosis results specifically includes: The signals to be processed include: rotation speed signals and vibration signals, wherein the rotation speed signals include rotation speed pulse signals, network rotation speed signals, and software input rotation speed signals; The speed signal is processed as follows: Prioritize detecting the hardware pulse channel and then make a judgment on the pulse channel: If a stable and effective rotational speed pulse signal is detected within a preset time, the pulse tracking mode will be automatically entered. If the pulse channel is invalid, then check if the network speed signal exists. If it exists, identify and parse the network speed value. If it does not exist, automatically switch to the software input speed signal.

6. The automatic bearing and gear fault diagnosis method according to claim 5, characterized in that, The pulse tracking mode specifically includes: Multiple pulse signals are acquired using the signal-sensitive sensor; The rising edge of each pulse is captured by a high-precision counter, and the time interval between two consecutive rising edges is accurately measured. The instantaneous rotational speed is obtained based on the time interval; The instantaneous rotational frequency is obtained based on the instantaneous rotational speed; The resampling clock pulse sequence is obtained based on the instantaneous frequency conversion; The resampling clock pulse sequence is used as an external sampling clock to trigger the analog-to-digital converter to synchronously acquire the analog signal from the vibration acceleration sensor in order to obtain a stationary signal in the angular domain.

7. The automatic bearing and gear fault diagnosis method according to claim 6, characterized in that, The process of processing the signal to be processed to obtain fault diagnosis results specifically includes: The vibration signal is processed to obtain a vibration impact signal.

8. The automatic diagnosis method for bearing and gear faults according to claim 7, characterized in that, The process of processing the signal to be processed to obtain fault diagnosis results specifically includes: The bearings and gears are diagnosed based on the angular domain stable signal and the vibration and shock signal to obtain the fault diagnosis results.

9. An automatic diagnostic system for bearing and gear faults, characterized in that, The method for automatic diagnosis of bearing and gear faults as described in claim 1 includes: a sampling frequency acquisition module, an installation quantity acquisition module, and a fault diagnosis module; The sampling frequency acquisition module is used to acquire the sampling frequency through a spectrum-aware sampling frequency convex optimization algorithm; The installation quantity acquisition module is used to obtain the installation quantity of signal sensitive sensors through a multi-factor weighted constraint scaling algorithm; The fault diagnosis module is used to acquire the signal to be processed based on the sampling frequency and the number of installed signal sensitive sensors, and to process the signal to be processed in order to obtain the fault diagnosis result.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor, the memory storing a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The storage medium is used to store a computer program that causes a computer to perform the method described in any one of claims 1-8.

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