A method, apparatus, electronic device, and storage medium for diagnosing equipment operating status.

Abstract

This invention provides a method, apparatus, electronic device, and storage medium for diagnosing equipment operating status. The method includes: acquiring target stress wave spectrum data and target stress wave energy data of the equipment under test; determining the mean energy and standard deviation of energy based on the target stress wave energy data; determining a screening ratio based on the mean energy, standard deviation of energy, and a preset first sampling ratio; and screening the target stress wave energy data or target stress wave spectrum data based on the screening ratio to obtain screened data, which is then used to diagnose equipment operating status faults in the equipment under test. This invention can filter out target stress wave spectrum data that interferes with fault diagnosis, while the target stress wave spectrum data corresponding to the obtained screened data still carries characteristic information representing the equipment operating status, reducing the workload of diagnosis and increasing diagnostic efficiency.

Description

Technical Field

[0001] This application relates to the field of diagnostic technology, and in particular to a method, apparatus, electronic device, and storage medium for diagnosing the operating status of equipment. Background Technology

[0002] In equipment operation status monitoring, factories typically employ a periodic inspection system without conducting thorough checks and maintenance based on monitoring data, resulting in significant manpower and material costs. Stress wave sensors, however, are highly sensitive sensors specifically designed to detect ultrasonic stress waves generated by friction and impact during the movement of industrial machinery. While continuously acquiring equipment status signals, they generate a large amount of digital data, increasing the workload for both manual analysis and machine-based assessment of equipment operation status, thereby reducing diagnostic efficiency. Summary of the Invention

[0003] In view of the shortcomings of the prior art described above, this application provides a method, apparatus, electronic device and storage medium for diagnosing the operating status of equipment, so as to solve the technical problems of large workload and low efficiency in diagnosing the operating status of equipment.

[0004] This application provides a method for diagnosing the operating status of equipment, comprising: acquiring target stress wave spectrum data and target stress wave energy data of the equipment under test; determining the energy mean and energy standard deviation based on the target stress wave energy data; determining a screening ratio according to the energy mean, the energy standard deviation and a preset first sampling ratio; screening the target stress wave energy data or the target stress wave spectrum data based on the screening ratio to obtain screened data, so as to diagnose the equipment operating status fault of the equipment under test through the screened data.

[0005] In one embodiment of this application, the target stress wave energy data is classified by energy distance according to a clustering algorithm to obtain energy classification data; a first mean and a first standard deviation are obtained based on the target stress wave energy data; a second mean and a second standard deviation are obtained for each category based on the energy classification data; the first mean and each of the second means are used as the energy mean, and the first standard deviation and each of the second standard deviations are used as the energy standard deviation.

[0006] In one embodiment of this application, a second sampling ratio is determined based on the mean energy, the standard deviation of energy, and a preset adjustment parameter; if the second sampling ratio is greater than or equal to a preset first sampling ratio, the preset first sampling ratio is determined as the screening ratio; if the second sampling ratio is less than the preset first sampling ratio, the second sampling ratio is determined as the screening ratio.

[0007] In one embodiment of this application, the second means and the second standard deviations are sorted; the ratio of the difference between the largest and the smallest second means to the first mean is determined as a first parameter; the ratio of the difference between the largest and the smallest second standard deviations to the first standard deviation is determined as a second parameter; a third parameter is determined based on the sum of the first parameter and the second parameter; and the second sampling ratio is determined based on the third parameter and the preset adjustment parameter.

[0008] In one embodiment of this application, the number of samples to be screened is determined based on the number of data in each category of the energy classification data and the screening ratio; the sampling interval for systematic sampling is determined based on the number of samples to be screened; and the energy classification data or the target stress wave spectrum data corresponding to the energy classification data is systematically sampled based on the sampling interval to obtain the screened data.

[0009] In one embodiment of this application, initial stress wave spectrum data and initial stress wave energy data of the device under test are obtained; null values ​​in the initial stress wave energy data are filtered out; initial stress wave energy data that are less than a preset low quantile corresponding to the initial stress wave energy data are filtered out, the preset low quantile being used to characterize the energy fluctuation of the device in normal operation; the filtered initial stress wave energy data is used as target stress wave energy data, and the initial stress wave spectrum data corresponding to the target stress wave energy data is used as target stress wave spectrum data.

[0010] In one embodiment of this application, if the screening data is an energy value, the target stress wave spectrum data corresponding to the screening data is used as sample spectrum data; if the screening data is a spectrum, the screening data is used as sample spectrum data; the average amplitude, standard deviation of amplitude, and number of amplitudes are determined based on the sample spectrum data; the spectral skewness coefficient and spectral kurtosis coefficient are determined according to the average amplitude, standard deviation of amplitude, and number of amplitudes; the spectral signal complexity of the sample spectrum data is classified according to the spectral skewness coefficient and spectral kurtosis coefficient to obtain fault diagnosis data, so as to perform equipment operation status fault diagnosis on the device under test through the fault diagnosis data.

[0011] This application provides a device for diagnosing the operational status of an equipment, comprising: a data acquisition module for acquiring target stress wave spectrum data and target stress wave energy data of the equipment under test; a mathematical processing module for determining the energy mean and energy standard deviation based on the target stress wave energy data; a ratio determination module for determining a screening ratio based on the energy mean, the energy standard deviation, and a preset first sampling ratio; and a fault diagnosis module for screening the target stress wave energy data or target stress wave spectrum data based on the screening ratio to obtain screened data, thereby performing fault diagnosis of the operational status of the equipment under test using the screened data.

[0012] According to one aspect of the embodiments of this application, an electronic device is provided, the electronic device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the electronic device enables the device operation status diagnosis method as described in any of the above embodiments.

[0013] According to one aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a computer processor, causes the computer to perform the device operation status diagnosis method described in any of the above embodiments.

[0014] The beneficial effects of this invention are as follows: This invention provides a method, device, electronic device, and storage medium for diagnosing equipment operating status. This invention determines the screening ratio by using the mean energy and standard deviation of the energy obtained from the target stress wave energy data. The target stress wave energy data or target stress wave spectrum data is screened by the screening ratio to obtain screened data. The target stress wave spectrum data corresponding to the screened data still carries characteristic information representing the operating status of the equipment. By filtering out the target stress wave spectrum data that interferes with fault diagnosis through the screening method, the workload of diagnosis is reduced and the diagnosis efficiency is increased.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0017] Figure 1 A schematic diagram of an exemplary system architecture to which the technical solutions of the embodiments of this application can be applied is shown;

[0018] Figure 2 A schematic flowchart of a device operation status diagnosis method according to an embodiment of this application is shown;

[0019] Figure 3 A block diagram of a device operation status diagnostic apparatus according to an embodiment of this application is shown;

[0020] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0021] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0022] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0023] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the present application. However, it will be apparent to those skilled in the art that embodiments of the present application may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the present application.

[0024] Please see Figure 1 , Figure 1 A schematic diagram of an exemplary system architecture to which the technical solutions of the embodiments of this application can be applied is shown. For example... Figure 1 As shown, the system architecture may include a computer device 101 and a stress wave sensor 102. The computer device 101 may be at least one of a general-purpose computer, a neural network computer, etc. Technical personnel can use the computer device 101 to filter the acquired target stress wave spectrum data and target stress wave energy data, so as to perform equipment operation status fault diagnosis on the equipment under test based on the target stress wave spectrum data corresponding to the filtered data. The stress wave sensor 102 is used to collect the initial stress wave spectrum data and initial stress wave energy data of the equipment under test, and provides the target stress wave spectrum data and target stress wave energy data obtained based on the initial stress wave spectrum data and initial stress wave energy data to the computer device 101 for filtering.

[0025] In a schematic manner, after computer device 101 acquires the target stress wave spectrum data and target stress wave energy data of the equipment under test, it determines the mean energy and standard deviation of energy based on the target stress wave energy data; it then determines a screening ratio based on the mean energy, standard deviation of energy, and a preset first sampling ratio; and finally, it filters the target stress wave energy data or target stress wave spectrum data according to the screening ratio to obtain screened data, which is then used for fault diagnosis of the equipment's operating status. This method filters out target stress wave spectrum data that interferes with fault diagnosis, while the resulting screened data still carries characteristic information representing the equipment's operating status, reducing the workload of diagnosis and increasing diagnostic efficiency.

[0026] To address the aforementioned technical problems, this application provides a method, apparatus, electronic device, and storage medium for diagnosing device operating status. The implementation details of the technical solutions in the embodiments of this application are described in detail below.

[0027] Please see Figure 2 , Figure 2 A schematic flowchart of a device operation status diagnosis method according to an embodiment of this application is shown. Figure 2 As shown, in an exemplary embodiment, the device operating status diagnosis method includes at least steps S210 to S240, which are described in detail below:

[0028] Step S210: Obtain the target stress wave spectrum data and target stress wave energy data of the device under inspection.

[0029] In one embodiment of this application, a stress wave sensor acquisition device monitors digital record (DR) data within a preset acquisition time. DR data is a rapid record of the input signal acquired by the stress wave sensor, representing stress wave monitoring characteristic data of the measurement point of the device under test. The preset acquisition time includes, but is not limited to, 2 seconds and 5 seconds. The DR data can be represented as time-domain data, from which spectral data and energy data can be obtained. The energy data is in the integral form of the time-domain data.

[0030] In one embodiment of this application, after acquiring the initial stress wave spectrum data and initial stress wave energy data of the device under test, the initial stress wave spectrum data is inspected. First, null values ​​(NA) in the initial stress wave energy data are filtered out. The generation of null values ​​includes, but is not limited to, device disconnection during the preset acquisition time of the DR data. Initial stress wave energy data below a preset low quantile is filtered out. The preset low quantile is used to characterize the energy fluctuations during normal device operation. The preset low quantile can be set to the 10th quantile, meaning that initial stress wave energy data below the 10th quantile is filtered out. Initial stress wave energy data below the 10th quantile represents the energy fluctuation range during normal device operation; this is merely an example, and this application does not impose any limitations. The filtered initial stress wave energy data is used as the target stress wave energy data, and the initial stress wave spectrum data corresponding to the target stress wave energy data is also used as the target stress wave spectrum data.

[0031] Step S220: Determine the mean energy and standard deviation of energy based on the target stress wave energy data.

[0032] In one embodiment of this application, energy distance classification is performed on the target stress wave energy data using a clustering algorithm to obtain energy classification data. The clustering algorithm includes, but is not limited to, the K-means clustering algorithm. The energy classification data includes, but is not limited to, three categories; this is merely an example. A first mean and a first standard deviation are obtained based on the target stress wave energy data; the first mean and each second mean are used as the energy mean, and the first standard deviation and each second standard deviation are used as the energy standard deviation. A second mean and a second standard deviation are obtained for each category based on the energy classification data.

[0033] In one embodiment of this application, the energy classification data of each category is at least greater than or equal to a preset classification threshold. The classification category can be limited based on the preset classification threshold and the amount of target stress wave energy data. The preset classification threshold can be 100. This is just an example and this application does not impose any restrictions.

[0034] Step S230: Determine the screening ratio based on the mean energy, standard deviation of energy, and preset first sampling ratio.

[0035] In one embodiment of this application, a second sampling ratio is determined based on the energy mean, energy standard deviation, and preset adjustment parameters; if the second sampling ratio is greater than or equal to a preset first sampling ratio, the preset first sampling ratio is determined as the screening ratio; if the second sampling ratio is less than the preset first sampling ratio, the second sampling ratio is determined as the screening ratio.

[0036] In one embodiment of this application, the target stress wave energy data is denoted as X, and the first mean value corresponding to the target stress wave energy data is denoted as... The first standard deviation of the target stress wave energy data is denoted as S. The category label of the energy classification data is denoted as i, and the second mean is denoted as... Let each second standard deviation be denoted as S. i ,

[0037] In one embodiment of this application, the second means and second standard deviations are sorted. For example, if there are 3 categories of energy classification data, the energy classification data with the smallest second mean is denoted as X1, and the corresponding second mean of this energy classification data is denoted as X2. The second standard deviation corresponding to this energy classification data is denoted as S1; the energy classification data with the median second mean is denoted as X2, and the second mean corresponding to this energy classification data is denoted as... The second standard deviation corresponding to this energy classification data is denoted as S2; the energy classification data with the smallest second mean is denoted as X3, and the second mean corresponding to this energy classification data is denoted as... The second standard deviation corresponding to this energy classification data is denoted as S3; the category labels of the energy classification data are i = 1, 2, 3.

[0038] In one embodiment of this application, the ratio of the difference between the largest and smallest second mean to the first mean is determined as a first parameter, and the formula for determining the first parameter is as follows:

[0039]

[0040] in, As the first parameter, For each of the energy classification data, the second mean is... Let i be the first mean value corresponding to the target stress wave energy data, and let i be the category label of the energy classification data.

[0041] In one embodiment of this application, the ratio of the difference between the largest and smallest second standard deviations to the first standard deviation is determined as a second parameter, and the formula for determining the second parameter is as follows:

[0042]

[0043] Among them, P S S is the second parameter. i Let S be the second standard deviation corresponding to the energy classification data, S be the first standard deviation corresponding to the target stress wave energy data, and i be the category label of the energy classification data.

[0044] In one embodiment of this application, a third parameter is determined based on the sum of the first parameter and the second parameter, i.e., the third parameter is represented as follows: The second sampling ratio is determined based on the third parameter and the preset adjustment parameter, where β is the preset adjustment parameter. The second sampling ratio is expressed as follows:

[0045] In one embodiment of this application, the screening ratio is determined based on a preset first sampling ratio and a second sampling ratio, and the formula for determining the screening ratio is as follows:

[0046]

[0047] Where P is the screening ratio. P is the first parameter. S For the second parameter, The third parameter is α, where α is the preset first sampling ratio and β is the preset adjustment parameter. The second sampling ratio is used. If the second sampling ratio is greater than or equal to the preset first sampling ratio, the preset first sampling ratio is determined as the screening ratio; if the second sampling ratio is less than the preset first sampling ratio, the second sampling ratio is determined as the screening ratio. α can be 0.2, and β can be 0.1. This is merely an example, and this application does not impose any restrictions on the values ​​of α and β.

[0048] Step S240: Filter the target stress wave energy data or target stress wave spectrum data based on the screening ratio to obtain screened data, so as to perform equipment operation status fault diagnosis on the equipment under test through the screened data.

[0049] In one embodiment of this application, the number of samples to be selected is determined based on the number of data points in each category of the energy classification data and the selection ratio. The number of data points can be represented by L, and the formula for the number of samples to be selected for each category is as follows:

[0050] N i =L i P-form (4);

[0051] Where, N i The number of filters for each category, L i P represents the number of data points in each category of the energy classification data, and P is the selection ratio. i represents the category number of the energy classification data.

[0052] In one embodiment of this application, if the energy classification data is of 3 types, the number of data in X1 is recorded as L1, and the target stress wave energy data corresponding to X1 is recorded as D1; ​​the number of data in X2 is recorded as L2, and the target stress wave energy data corresponding to X2 is recorded as D2; the number of data in X3 is recorded as L3, and the target stress wave energy data corresponding to X3 is recorded as D3.

[0053] In one embodiment of this application, the sampling interval for systematic sampling is determined based on the number of screenings, with the sampling interval for each class being L.i / N i The data selected through systematic sampling is relatively evenly distributed.

[0054] In one embodiment of this application, fault diagnosis of the equipment operating status of the device under test is typically obtained through spectrum analysis. This can be achieved by systematically sampling energy classification data or the target stress wave spectrum data corresponding to the energy classification data according to the sampling interval, thus obtaining filtered data. In other words, fault diagnosis of the equipment operating status can be performed based on the target stress wave spectrum data corresponding to the filtered energy classification data, or the target stress wave spectrum data can be directly filtered.

[0055] In one embodiment of this application, the amount of data to be screened is determined by the frequency and amplitude of the stress wave energy value fluctuation. That is, when the frequency and amplitude of the stress wave energy value fluctuation are large, more data is extracted, and vice versa. By reducing the amount of data through numerical range filtering, data classification and sampling, the workload of diagnosis is reduced, thereby improving the efficiency of diagnosis.

[0056] In one embodiment of this application, if the screening data is an energy value, the target stress wave spectrum data corresponding to the screening data is used as sample spectrum data; if the screening data is a spectrum, the screening data is used as sample spectrum data; the average amplitude, standard deviation of amplitude, and number of amplitudes are determined based on the sample spectrum data; the spectral skewness coefficient and spectral kurtosis coefficient are determined based on the average amplitude, standard deviation of amplitude, and number of amplitudes; the sum of the spectral skewness coefficient and the spectral kurtosis coefficient is used, and the sample spectrum data is classified according to the complexity of the spectral signal using the K-means algorithm to obtain fault diagnosis data, so as to perform equipment operation status fault diagnosis on the equipment under test through the fault diagnosis data.

[0057] Please see Figure 3 , Figure 3 A block diagram of a device operation status diagnostic apparatus according to an embodiment of this application is shown. This apparatus can be applied to... Figure 1 The implementation environment shown is specifically configured in computer device 101. This device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applicable.

[0058] like Figure 3 As shown, a device operation status diagnosis device 300 according to an embodiment of this application includes: a data acquisition module 301, a mathematical processing module 302, a ratio determination module 303, and a fault diagnosis module 304.

[0059] Among them, the data acquisition module 301 is used to acquire the target stress wave spectrum data and target stress wave energy data of the equipment under inspection;

[0060] Mathematical processing module 302 is used to determine the mean energy and standard deviation of energy based on the target stress wave energy data;

[0061] The proportion determination module 303 is used to determine the screening proportion based on the mean energy, the standard deviation of energy, and the preset first sampling proportion.

[0062] The fault diagnosis module 304 is used to filter the target stress wave energy data or target stress wave spectrum data based on the filtering ratio to obtain filtered data, so as to perform equipment operation status fault diagnosis on the equipment under inspection through the filtered data.

[0063] It should be noted that the equipment operation status diagnosis device and the equipment operation status diagnosis method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the equipment operation status diagnosis device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0064] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the device operation status diagnosis method provided in the above embodiments.

[0065] Please see Figure 4 , Figure 4 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 4 The computer system 400 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0066] like Figure 4As shown, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from Storage Unit 408 into Random Access Memory (RAM) 403, such as performing the methods described in the above embodiments. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0067] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0068] According to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs various functions defined in the system of this application.

[0069] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0070] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0071] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0072] Another aspect of this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a computer's processor, causes the computer to perform the device operation status diagnosis method provided in the above embodiments. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not assembled into the electronic device.

[0073] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for diagnosing the operating status of equipment, characterized in that, include: Acquire the target stress wave spectrum data and target stress wave energy data of the equipment under inspection; The mean energy and standard deviation of energy are determined based on the target stress wave energy data. Specifically, this includes: classifying the target stress wave energy data by energy distance according to a clustering algorithm to obtain energy classification data; obtaining a first mean and a first standard deviation based on the target stress wave energy data; obtaining a second mean and a second standard deviation for each category based on the energy classification data; and using the first mean and each of the second means as the energy mean, and the first standard deviation and each of the second standard deviations as the energy standard deviation. The screening ratio is determined based on the energy mean, the energy standard deviation, and a preset first sampling ratio. Specifically, this includes: determining a second sampling ratio based on the energy mean, the energy standard deviation, and a preset adjustment parameter; if the second sampling ratio is greater than or equal to the preset first sampling ratio, then the preset first sampling ratio is determined as the screening ratio; if the second sampling ratio is less than the preset first sampling ratio, then the second sampling ratio is determined as the screening ratio. The determination of the second sampling ratio based on the energy mean, the energy standard deviation, and the preset adjustment parameter includes: sorting each of the second means and each of the second standard deviations; determining a first parameter as the ratio of the difference between the largest and smallest second means to the first mean; determining a second parameter as the ratio of the difference between the largest and smallest second standard deviations to the first standard deviation; determining a third parameter based on the sum of the first parameter and the second parameter; and determining the second sampling ratio based on the third parameter and the preset adjustment parameter. Based on the screening ratio, the target stress wave energy data or target stress wave spectrum data is screened to obtain screened data, which is then used to diagnose equipment operating status faults in the device under inspection.

2. The equipment operation status diagnosis method according to claim 1, characterized in that, Based on the stated screening ratio, the target stress wave energy data or target stress wave spectrum data is screened to obtain screened data, including: The number of samples to be filtered is determined based on the number of data points in each category of the energy classification data and the filtering ratio. The sampling interval for system sampling is determined based on the number of samples screened. The energy classification data or the target stress wave spectrum data corresponding to the energy classification data are systematically sampled according to the sampling interval to obtain the screened data.

3. The equipment operation status diagnosis method according to claim 2, characterized in that, Before acquiring the target stress wave spectrum data and target stress wave energy data of the equipment under inspection, the equipment operation status diagnosis method further includes: Acquire the initial stress wave spectrum data and initial stress wave energy data of the equipment under test; Filter out null values ​​from the initial stress wave energy data; The initial stress wave energy data that is less than the initial stress wave energy data corresponding to a preset low quantile is filtered out. The preset low quantile is used to characterize the energy fluctuation of the equipment during normal operation. The filtered initial stress wave energy data is used as the target stress wave energy data, and the initial stress wave spectrum data corresponding to the target stress wave energy data is used as the target stress wave spectrum data.

4. The equipment operation status diagnosis method according to claim 2, characterized in that, Based on the stated screening ratio, the target stress wave energy data or target stress wave spectrum data is screened to obtain screened data. After using the screened data to perform equipment operation status fault diagnosis on the equipment under inspection, the equipment operation status diagnosis method further includes: If the screening data is an energy value, then the target stress wave spectrum data corresponding to the screening data is used as the sample spectrum data; If the filtered data is a spectrum, then the filtered data will be used as sample spectrum data; The amplitude mean, amplitude standard deviation, and amplitude count are determined based on the sample spectrum data. The spectral skewness coefficient and spectral kurtosis coefficient are determined based on the average amplitude, the standard deviation of the amplitude, and the number of amplitudes. The sample spectrum data is classified according to the spectrum skewness coefficient and spectrum kurtosis coefficient to obtain fault diagnosis data, which is then used to diagnose the equipment operating status faults of the device under test.

5. A device for diagnosing equipment operating status, characterized in that, include: The data acquisition module is used to acquire the target stress wave spectrum data and target stress wave energy data of the equipment under inspection; The mathematical processing module is used to determine the energy mean and energy standard deviation based on the target stress wave energy data; specifically, it includes: classifying the target stress wave energy data by energy distance according to a clustering algorithm to obtain energy classification data; obtaining a first mean and a first standard deviation based on the target stress wave energy data; obtaining a second mean and a second standard deviation for each category based on the energy classification data; and using the first mean and each of the second means as the energy mean, and the first standard deviation and each of the second standard deviations as the energy standard deviation. A proportion determination module is used to determine a screening proportion based on the energy mean, the energy standard deviation, and a preset first sampling proportion. Specifically, it includes: determining a second sampling proportion based on the energy mean, the energy standard deviation, and a preset adjustment parameter; if the second sampling proportion is greater than or equal to the preset first sampling proportion, then the preset first sampling proportion is determined as the screening proportion; if the second sampling proportion is less than the preset first sampling proportion, then the second sampling proportion is determined as the screening proportion. The determination of the second sampling proportion based on the energy mean, the energy standard deviation, and the preset adjustment parameter includes: sorting each of the second means and each of the second standard deviations; determining a first parameter as the ratio of the difference between the largest and smallest second means to the first mean; determining a second parameter as the ratio of the difference between the largest and smallest second standard deviations to the first standard deviation; determining a third parameter based on the sum of the first parameter and the second parameter; and determining the second sampling proportion based on the third parameter and the preset adjustment parameter. The fault diagnosis module is used to filter the target stress wave energy data or target stress wave spectrum data based on the filtering ratio to obtain filtered data, so as to perform equipment operation status fault diagnosis on the equipment under inspection through the filtered data.

6. An electronic device, characterized in that, The electronic device includes: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the device operation status diagnosis method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, It stores a computer program, which, when executed by the computer's processor, causes the computer to perform the device operation status diagnosis method according to any one of claims 1 to 4.

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