Quaternion-based rolling bearing fault diagnosis method and device and storage medium

Abstract

The embodiment of the invention provides a quaternion-based rolling bearing fault diagnosis method, a quaternion-based rolling bearing fault diagnosis device and a storage medium. The quaternion-based rolling bearing fault diagnosis method comprises the steps of collecting a plurality of bearing motion state signals; based on a quaternion, fusing the plurality of bearing motion state signals to obtain a quaternion signal; based on quaternion Fourier transform, converting the quaternion signal to a frequency domain to obtain a frequency spectrum of the quaternion signal; and determining whether the bearing has a fault according to the frequency spectrum of the quaternion signal. The technical scheme provided by the invention is used for solving the problem that fault information is not easy to identify in the prior art, so that the accuracy of bearing fault diagnosis is improved.

Description

Technical Field

[0001] This document relates to the field of rotating machinery fault diagnosis, and in particular to a rolling bearing fault diagnosis method, device and storage medium based on quaternions. Background Art

[0002] Low-speed and heavy-load equipment plays a very important role in mechanical systems, and its parts experience more complex and difficult environments than ordinary equipment: higher pressure, greater load, more wear, higher temperature, etc. Therefore, it is very important to monitor and diagnose the operating status of the vulnerable rolling bearings.

[0003] A more common method is to use multiple sets of sensors to collect vibration acceleration signals in multiple directions and then fuse these signals.

[0004] However, during the fusion process, the various acoustic signals are usually intertwined, which makes the frequency band where the fault information exists contain a lot of noise, making it difficult to identify, thereby reducing the accuracy of bearing fault diagnosis. Summary of the invention

[0005] In order to solve at least one of the above technical problems, the present application aims to propose a rolling bearing fault diagnosis method, device and storage medium based on quaternion to improve the accuracy of bearing fault diagnosis.

[0006] In a first aspect, one or more embodiments of this specification provide a rolling bearing fault diagnosis method based on quaternions, comprising:

[0007] Collect multiple bearing motion status signals;

[0008] Based on quaternion, the plurality of bearing motion state signals are fused to obtain a quaternion signal;

[0009] Based on quaternion Fourier transform, convert the quaternion signal into frequency domain to obtain the frequency spectrum of the quaternion signal; and

[0010] Whether a bearing failure occurs is determined based on the frequency spectrum of the quaternion signal.

[0011] Further, collecting a plurality of bearing motion state signals comprises:

[0012] A plurality of bearing motion state signals are collected respectively by a plurality of sensors,

[0013] or

[0014] According to a preset collection time interval, a plurality of bearing motion state signals are continuously collected.

[0015] Furthermore, the data format of the quaternion is:

[0016] a+bi+cj+dk;

[0017] Where a, b, c, d are real numbers and i, j, k are imaginary units, and i 2 =j 2 =k 2 =ijk=-1, ij=-ji=k, jk=-kj=i, ki=-ik=j;

[0018] Furthermore, the plurality of bearing motion state signals are fused to obtain a quaternion signal including:

[0019] The multiple bearing motion state signals are preferentially substituted into the real number parameters of the imaginary part respectively, and the remaining real number parameters are set to 0.

[0020] Further, based on the quaternion Fourier transform, converting the quaternion signal into the frequency domain comprises:

[0021] Decomposing the quaternion signal into a first complex signal and a second complex signal;

[0022] Fourier transform is performed on the first complex signal and the second complex signal respectively, and the two transform results are summed to convert the quaternion signal into the frequency domain.

[0023] Further, determining whether a bearing failure occurs according to the frequency spectrum of the quaternion signal includes:

[0024] determining a trend component of the frequency spectrum of the quaternion signal;

[0025] According to the trend component, determining a minimum value of the trend component;

[0026] Taking the minimum value of the trend component as the binary frequency, filtering the frequency spectrum of the quaternion signal to obtain envelope diagrams of multiple frequency bands;

[0027] Whether a bearing failure occurs is determined based on the envelope diagrams of the multiple frequency bands.

[0028] Further, determining the trend component of the frequency spectrum of the quaternion signal includes:

[0029] Performing Fourier transform on the frequency spectrum of the quaternion signal to obtain a key function;

[0030] According to a preset interval, intercepting the target function from the key function; and

[0031] Perform inverse Fourier transform on the objective function to obtain the trend component.

[0032] In a second aspect, one or more embodiments of this specification provide a rolling bearing fault diagnosis device based on quaternions, comprising:

[0033] Acquisition module, fusion module, transformation module and detection module

[0034] The acquisition module is used to acquire a plurality of bearing motion state signals;

[0035] The fusion module is used to fuse the multiple bearing motion state signals based on quaternion to obtain a quaternion signal;

[0036] The transformation module is used to transform the quaternion signal into a frequency domain based on a quaternion Fourier transform to obtain a frequency spectrum of the quaternion signal; and

[0037] The detection module is used to determine whether a bearing failure occurs according to the frequency spectrum of the quaternion signal.

[0038] Furthermore, the acquisition module is used to respectively acquire the plurality of bearing motion state signals through a plurality of sensors, or to continuously acquire the plurality of bearing motion state signals according to a preset acquisition time interval.

[0039] Furthermore, the data format of the quaternion is:

[0040] a+bi+cj+dk;

[0041] Where a, b, c, d are real numbers and i, j, k are imaginary units, and,

[0042] i 2 =j 2 =k 2 =ijk=-1, ij=-ji=k, jk=-kj=i, ki=-ik=j;

[0043] The fusion module is used to preferentially substitute the multiple bearing motion state signals into the real number parameters of the imaginary part respectively, and set the remaining real number parameters to 0.

[0044] In a third aspect, one or more embodiments of this specification provide a storage medium, including:

[0045] Used to store computer executable instructions, which implement the method described in any one of the first aspects when executed.

[0046] Compared with the prior art, this application can at least achieve the following technical effects:

[0047] In the prior art, although multiple dimensional data are placed in an array, set, or matrix, when it comes to frequency domain conversion, it is still performed separately for each dimension, and then the time domains corresponding to each dimension are fused. However, this application is based on the form of quaternions, which fuses four signals into a quaternion signal, and then converts the quaternion signal to the frequency domain to achieve frequency domain conversion of multiple dimensional data as a whole. Since multiple dimensional data are converted into the frequency domain as a whole, the fault signal will be significantly enhanced, making it easier to diagnose whether the rolling shaft is faulty, thereby improving the accuracy of bearing fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1 A flowchart of a rolling bearing fault diagnosis method based on quaternions provided for one or more embodiments of this specification;

[0050] Figure 2 The waveform, Fourier spectrum and envelope spectrum of the three channel signals provided for the example;

[0051] Figure 3 Fast Kurtogram processing results of the three channels provided for the example;

[0052] Figure 4 It is the envelope spectrum of each component after AMD (Analytical Mode Decomposition) decomposition in the prior art;

[0053] Figure 5a Quaternion Fourier spectrogram provided for the example;

[0054] Figure 5b Based on Figure 5a Schematic diagram of the obtained trend components and boundaries (binary frequencies);

[0055] Figure 6a for Figure 5b The time domain waveforms corresponding to the 8 parts;

[0056] Figure 6b for Figure 5b The envelope spectra corresponding to the 8 parts;

[0057] Figure 7A schematic diagram of the structure of a rolling bearing fault diagnosis device based on quaternions provided in one or more embodiments of this specification. DETAILED DESCRIPTION

[0058] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the following will be combined with the drawings in one or more embodiments of this specification to clearly and completely describe the technical solutions in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0059] The present application embodiment provides a rolling bearing fault diagnosis method based on quaternion, such as Figure 1 As shown, the following steps are included:

[0060] Step 1: Collect multiple bearing motion state signals.

[0061] In the embodiment of the present application, three bearing motion state signals are usually collected. The bearing motion state signal can be a vibration acceleration and an audio signal. It should be noted that there are two situations for collecting multiple bearing motion state signals:

[0062] In the first case, multiple bearing motion state signals are collected separately by multiple sensors.

[0063] In the second case, multiple bearing motion state signals are continuously collected according to a preset collection time interval, for example, one bearing motion state signal is collected every second, and three bearing motion state signals are continuously collected.

[0064] Step 2: Based on quaternion, multiple bearing motion state signals are fused to obtain a quaternion signal.

[0065] In the embodiment of the present application, the data format of the quaternion is:

[0066] a+bi+cj+dk;

[0067] Where a, b, c, d are real numbers and i, j, k are imaginary units, and i 2 =j 2 =k 2 =ijk=-1, ij=-ji=k, jk=-kj=i, ki=-ik=j. Where a, b, c and d correspond to the collected signals.

[0068] Step 3: Based on the quaternion Fourier transform, the quaternion signal is converted into the frequency domain to obtain the spectrum of the quaternion signal.

[0069] In the embodiment of the present application, the specific process of converting the quaternion signal to the frequency domain is:

[0070] Assume that the quaternion represented in the standard basis {1,i,j,k} is:

[0071] q=a+bi+cj+dk;

[0072] Then a new basis is given, which can be expressed as:

[0073]

[0074] The four real values ​​of the quaternion expressed in the new basis are:

[0075]

[0076] Among them, μ1μ2μ3 are imaginary units.

[0077] After basis transformation, the quaternion signal can be used as two complex signals:

[0078]

[0079] Where I represents the complex root of -1.

[0080] By obtaining the Fourier transform results of the two complex roots respectively and summing the two results, the Fourier transform of the quaternion signal can be obtained, thereby converting the quaternion signal to the frequency domain and obtaining the spectrum of the quaternion signal.

[0081] Step 4: Determine whether the bearing is faulty based on the frequency spectrum of the quaternion signal.

[0082] In the embodiment of the present application, the spectrum obtained based on the quaternion signal can significantly enhance the fault signal, and the enhanced fault signal can weaken the influence of noise on fault diagnosis, thereby improving the accuracy of fault diagnosis.

[0083] In the embodiment of the present application, the fusion process is: substitute multiple bearing motion state signals into real number parameters respectively. Preferably, the multiple bearing motion state signals are preferentially substituted into the real number parameters of the imaginary part respectively, and the remaining real number parameters are set to 0. Since quaternions have the following association relationship:

[0084] i 2 =j 2 =k 2 =ijk=-1

[0085] ij=-ji=k, jk=-kj=i, ki=-ik=j

[0086] Therefore, substituting the bearing motion state signal into the imaginary part first can improve the signal fusion effect.

[0087] It should be noted that the bearing motion state signals are usually no more than 4. When the bearing motion state signals exceed 4, they can be grouped into 4 and signal fusion can be performed separately.

[0088] In the embodiment of the present application, since there is no commutative law for quaternions, it is very difficult to merge the imaginary parts. Therefore, it is proposed to first decompose the quaternion signal into two complex signals, use the existing algorithm for each complex signal to realize Fourier transform, and finally add the Fourier transform results of the two complex signals to obtain the Fourier transform of the quaternion signal, so as to convert the quaternion signal to the frequency domain.

[0089] Specifically, the quaternion signal is decomposed into a first complex signal and a second complex signal; Fourier transform is performed on the first complex signal and the second complex signal respectively, and the two transform results are summed to convert the quaternion signal into the frequency domain.

[0090] In an embodiment of the present application, when determining whether a fault has occurred, the trend component of the frequency spectrum of the quaternion signal is first obtained, and then the frequency bands of the spectrum are divided based on the trend component. Then, the envelope diagram of each frequency band is obtained based on filtering, and finally, by identifying whether there is a frequency band or waveform corresponding to the fault in the envelope diagram, it is determined whether a fault has occurred.

[0091] Specifically, determine the trend component of the spectrum of the quaternion signal. According to the trend component, determine the minimum value of the trend component. Use the minimum value of the trend component as the binary frequency to filter the spectrum of the quaternion signal to obtain envelope diagrams of multiple frequency bands. According to the envelope diagrams of the multiple frequency bands, determine whether the bearing is faulty. The spectrum component refers to the amplitude of the frequency in the frequency domain after transformation.

[0092] The process of determining the spectral components is as follows:

[0093] Performing Fourier transformation on the spectrum of the quaternion signal to obtain a key function; intercepting a target function from the key function according to a preset interval; and performing inverse Fourier transformation on the target function to obtain the trend component. The preset interval is usually an area of ​​the key function close to the origin.

[0094] In order to illustrate the feasibility of the above solution, this application gives the following examples: Figure 2 -6:

[0095] The acoustic signal experimental data used in this example comes from Mie University. In the bearing fault simulator, the drive end is connected to the bearing part with a pulley. The part where the bearing is installed bears a load of about 500kg. The rotation speed of the shaft is 70rpm. At the same time, this example uses a rusty chain to add noise to the system. During the experiment, the vibration acceleration sensor, acoustic emission sensor, and microphone synchronously collect signals. The present invention uses the experimental site signal collected by the microphone. The sampling frequency of the microphone is 96kHz, the collection time is about 20s, and the number of collections is 3 times. The interval time for each collection is 1 minute to ensure that the environmental noise is different from the previous time.

[0096] The three sets of sound signals collected by the microphone are treated as three channels and processed. The waveforms, Fourier spectra and envelope spectra of the three channel signals are shown in Figure 1. Figure 2 As shown, due to the characteristics of acoustic signals, such as low sensitivity and strong noise, it is difficult to find periodic pulses in the waveform.

[0097] First, Fast Kurtogram is used to process the data of the three channels respectively to obtain the reconstructed components and envelope spectra of the frequency bands of the three channels. The processing results are as follows: Figure 3 As shown. Figure 3 It can be seen that there is an obvious single pulse in the waveform, but no periodic information. In the envelope spectrum, only the Z-Channel shows the characteristic frequency of the outer ring fault, but there is no harmonic.

[0098] The traditional AMD method requires manually setting the important frequencies that represent useful information and distinguishing different components by calculating the binary frequencies. Specifically, after extracting each component of the three channels, the envelope spectrum of each component is calculated. The results are as follows: Figure 4 As shown in the figure, four components are obtained for each channel, and there is no obvious fault characteristic frequency or harmonic in the envelope spectrum. It can be seen that the traditional method of fault diagnosis has the problems of low efficiency and low success rate.

[0099] This example uses the rolling bearing fault diagnosis method based on quaternion analytical modal decomposition to diagnose the vibration signal again.

[0100] First, the three-channel signals are integrated into quaternions. The signal fusion method based on quaternion Fourier transform can not only bring together useful information, but also provide more consistent objects for spectrum segmentation. Figure 5a Due to the high sampling frequency, there is a large gap between the amplitude of the high-frequency part and the low-frequency part of the quaternion Fourier spectrum. The high-amplitude frequency components are concentrated within 10000Hz. The spectrum segmentation method is used to process the signal. The part with a frequency lower than 10000Hz is divided into 8 parts such as Figure 5bBoundaries represents the boundaries of frequency bands, Trend spectrum represents the trend component, and Spectrum represents the quaternion Fourier spectrum.

[0101] Extract the information in each frequency band, and each frequency band is reconstructed into a time domain waveform such as Figure 6a There is still a lot of noise in the components, and the periodic pulses are difficult to identify from the waveform. The envelope spectrum of each component is as follows Figure 6b , the characteristic frequency of the bearing outer race fault and its harmonics appear. The amplitude of the harmonics shows a decreasing trend. These results indicate that there is a bearing outer race fault in the collected signal.

[0102] By comparing the prior art with the technical solution of the present application, it can be seen that the present application can improve the accuracy of fault diagnosis.

[0103] The present application embodiment provides a rolling bearing fault diagnosis device based on quaternion, such as Figure 7 As shown, it includes: a collection module 201, a fusion module 202, a transformation module 203 and a detection module 204;

[0104] The acquisition module 201 is used to acquire multiple bearing motion state signals;

[0105] The fusion module 202 is used to fuse multiple bearing motion state signals based on quaternions to obtain a quaternion signal;

[0106] The transformation module 203 is used to transform the quaternion signal into the frequency domain based on the quaternion Fourier transform to obtain the frequency spectrum of the quaternion signal; and

[0107] The detection module 204 is used to determine whether a bearing failure occurs according to the frequency spectrum of the quaternion signal.

[0108] In an embodiment of the present application, the acquisition module is used to respectively acquire multiple bearing motion state signals through multiple sensors, or to continuously acquire multiple bearing motion state signals according to a preset acquisition time interval.

[0109] In the embodiment of the present application, the data format of the quaternion is:

[0110] a+bi+cj+dk;

[0111] Where a, b, c, d are real numbers and i, j, k are imaginary units, and,

[0112] i 2 =j 2 =k 2 =ijk=-1, ij=-ji=k, jk=-kj=i, ki=-ik=j;

[0113] The fusion module is used to preferentially substitute multiple bearing motion state signals into the real number parameters of the imaginary part, and set the remaining real number parameters to 0.

[0114] The present application provides a storage medium, including:

[0115] Used to store computer executable instructions, which implement the method described in any of the above embodiments when executed.

[0116] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0117] In the 1930s, improvements to a technology could be clearly distinguished as hardware improvements (for example, improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the method flow). However, with the development of technology, many improvements to the method flow today can be regarded as direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital method on a PLD by programming it themselves, without having to ask a chip manufacturer to design and produce a dedicated integrated circuit chip. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0118] The controller can be implemented in any appropriate manner, for example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a purely computer-readable program code manner, the controller can be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included therein for implementing various functions can also be regarded as structures within the hardware component. Or even, the devices for implementing various functions can be regarded as both software modules for implementing the method and structures within the hardware component.

[0119] The methods, devices, modules or units described in the above embodiments may be implemented by a computer chip or entity, or by a product having a certain function. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0120] For the convenience of description, the above devices are described in terms of functions and are divided into various units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0121] It will be appreciated by those skilled in the art that one or more embodiments of this specification may be provided as methods, methods or computer program products. Therefore, one or more embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0122] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (methods), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0125] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0126] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0127] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0128] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0129] One or more embodiments of the present specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. One or more embodiments of the present specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0130] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the method embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0131] The above description is only an embodiment of this document and is not intended to limit this document. For those skilled in the art, this document may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this document should be included in the scope of the claims of this document.

Claims

1. A rolling bearing fault diagnosis method based on quaternion, characterized in that: include: Collect multiple bearing motion status signals; Based on quaternion, the plurality of bearing motion state signals are fused to obtain a quaternion signal; Based on quaternion Fourier transform, convert the quaternion signal into frequency domain to obtain the frequency spectrum of the quaternion signal; and Whether a bearing failure occurs is determined based on the frequency spectrum of the quaternion signal.

2. The method according to claim 1, characterized in that The collecting of multiple bearing motion state signals comprises: A plurality of bearing motion state signals are collected respectively by a plurality of sensors, or According to a preset collection time interval, a plurality of bearing motion state signals are continuously collected.

3. The method according to claim 1, characterized in that: The data format of the quaternion is: a+bi+cj+dk; Where a, b, c, d are real numbers and i, j, k are imaginary units, and i 2 =j 2 =k 2 =ijk=-1, ij=-ji=k, jk=-kj=i, ki=-ik=j; Furthermore, the plurality of bearing motion state signals are fused to obtain a quaternion signal including: The multiple bearing motion state signals are preferentially substituted into the real number parameters of the imaginary part respectively, and the remaining real number parameters are set to 0.

4. The method according to claim 1, characterized in that: Based on the quaternion Fourier transform, converting the quaternion signal into the frequency domain comprises: Decomposing the quaternion signal into a first complex signal and a second complex signal; Fourier transform is performed on the first complex signal and the second complex signal respectively, and the two transform results are summed to convert the quaternion signal into the frequency domain.

5. The method according to claim 1, characterized in that Determining whether a bearing failure occurs according to the frequency spectrum of the quaternion signal includes: determining a trend component of the frequency spectrum of the quaternion signal; According to the trend component, determining a minimum value of the trend component; Taking the minimum value of the trend component as the binary frequency, filtering the frequency spectrum of the quaternion signal to obtain envelope diagrams of multiple frequency bands; Whether a bearing failure occurs is determined based on the envelope diagrams of the multiple frequency bands.

6. The method according to claim 5, characterized in that Determining a trend component of the frequency spectrum of the quaternion signal includes: Performing Fourier transform on the frequency spectrum of the quaternion signal to obtain a key function; According to a preset interval, intercepting the target function from the key function; and Perform inverse Fourier transform on the objective function to obtain the trend component.

7. A rolling bearing fault diagnosis device based on quaternion, characterized in that: include: Acquisition module, fusion module, transformation module and detection module The acquisition module is used to acquire a plurality of bearing motion state signals; The fusion module is used to fuse the multiple bearing motion state signals based on quaternion to obtain a quaternion signal; The transformation module is used to transform the quaternion signal into a frequency domain based on a quaternion Fourier transform to obtain a frequency spectrum of the quaternion signal; and The detection module is used to determine whether a bearing failure occurs according to the frequency spectrum of the quaternion signal.

8. The device according to claim 7, characterized in that The acquisition module is used to respectively acquire the plurality of bearing motion state signals through a plurality of sensors, or to continuously acquire the plurality of bearing motion state signals according to a preset acquisition time interval.

9. The device according to claim 7, characterized in that The data format of the quaternion is: a+bi+cj+dk; where a, b, c, d are real numbers and i, j, k are imaginary units, and, i 2 =j 2 =k 2 =ijk=-1,ij=-ji=k,jk=-kj=i,ki=-ik=j; The fusion module is used to preferentially substitute the multiple bearing motion state signals into the real number parameters of the imaginary part respectively, and set the remaining real number parameters to 0.

10. A storage medium, characterized in that: include: Used to store computer executable instructions, which implement the method according to any one of claims 1 to 6 when executed.

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