WO2023207337A1 - 一种球轴承的故障确定方法、设备以及存储介质 - Google Patents

一种球轴承的故障确定方法、设备以及存储介质 Download PDF

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Publication number
WO2023207337A1
WO2023207337A1 PCT/CN2023/079698 CN2023079698W WO2023207337A1 WO 2023207337 A1 WO2023207337 A1 WO 2023207337A1 CN 2023079698 W CN2023079698 W CN 2023079698W WO 2023207337 A1 WO2023207337 A1 WO 2023207337A1
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Prior art keywords
fault
ball bearing
bearing
frequency
characteristic frequency
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PCT/CN2023/079698
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English (en)
French (fr)
Inventor
杜飞平
谭永华
王春民
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西安航天动力研究所
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Publication of WO2023207337A1 publication Critical patent/WO2023207337A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • G06F2218/06—Denoising by applying a scale-space analysis, e.g. using wavelet analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/04—Bearings
    • G01M13/045—Acoustic or vibration analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Definitions

  • the present application relates to the technical field of rolling bearings, and in particular to a ball bearing fault determination method, equipment and storage medium.
  • ball bearings are an important type of rolling bearings, it is particularly important to provide a method that can quickly and accurately determine bearing failure.
  • this application discloses a ball bearing fault determination method, equipment and storage medium, which are used to provide a technical solution that can quickly and accurately determine ball bearing faults.
  • the present invention provides a ball bearing fault determination method, which includes the following steps:
  • the vibration signal of the ball bearing is obtained, and the signals of each time period in the vibration signal are denoised based on the wavelet packet decomposition method to obtain the denoised signals of each time period.
  • the short-time Fourier transform is used to perform time-frequency analysis on the signals in each period to obtain the period in which the ball bearing failure occurs and the fault frequency corresponding to the period.
  • the signal in the period when the ball bearing failure occurs is analyzed in detail.
  • the fault frequency includes the fault characteristic frequency of the ball bearing
  • the duration of occurrence of the fault corresponding to the fault characteristic frequency is determined.
  • the fault type and fault severity of the ball bearing are determined.
  • the present invention performs denoising processing on the vibration signal of the ball bearing based on the wavelet packet decomposition method to eliminate the noise information in the vibration signal and improve the accuracy of the present invention in locating ball bearing faults. Moreover, vibration signal denoising based on the wavelet packet decomposition method does not damage the original characteristics of the vibration signal, retains the original characteristics of the vibration signal, and eliminates noise pollution, laying a foundation for subsequent parameter identification of vibration signals.
  • the present invention uses short-time Fourier transform to perform time-frequency analysis on the signals in each period, and can quickly locate the period of ball bearing fault wind noise and the frequency of fault occurrence, so as to improve the accuracy of ball bearing fault determination to a certain extent. efficiency.
  • the present invention uses the time-frequency analysis method of Choi-Williams distribution to conduct detailed analysis of the signal during the period when the ball bearing failure occurs, so as to obtain a clearer time-frequency diagram of the ball bearing failure. Then, combined with the ball bearing failure The fault characteristic frequency of the bearing determines the duration of the fault corresponding to the fault characteristic frequency, and then the fault type and fault severity of the ball bearing can be accurately determined, thereby improving the working reliability and mechanical environment adaptability of the ball bearing. sex.
  • embodiments of the present invention provide a ball bearing fault determination device, including a processor and a communication interface coupled with the processor; the processor is used to run a computer program or instructions to implement a ball bearing fault determination method. .
  • embodiments of the present invention provide a computer storage medium, wherein the computer stores Instructions are stored in the medium, and when the instructions are executed, the fault determination method of the ball bearing is implemented.
  • the second and third aspects of the present invention have the same beneficial effects as the above-mentioned technical solution assessment and evaluation method, and will not be described again here.
  • Figure 1 is a step flow chart of a bearing fault determination method provided by an embodiment of the present invention
  • Figure 2 is a schematic diagram of a three-layer wavelet packet decomposition tree provided by an embodiment of the present invention
  • Figure 3 is a schematic diagram of the change pattern of the rotation speed of the Data1 rotating shaft provided by the embodiment of the present invention.
  • Figure 4 is a schematic diagram of the time domain waveform of the radial vibration of the Data1 bearing provided by the embodiment of the present invention.
  • Figure 5 shows the signal denoising process based on wavelet packet decomposition of the signal in the 70s-110s period of Data1 provided by the embodiment of the present invention.
  • the specified orthogonal wavelet "Db4" with compact support is used, and the number of decomposition layers is 4. Then the schematic diagram of the decomposed wavelet packet coefficients of each layer;
  • Figure 6 is a schematic diagram comparing the original signal and the denoised signal in the Data1 (70s-110s) period provided by the embodiment of the present invention
  • Figure 7 shows the characteristics of the residual signal in the Data1 (70s-110s) period provided by the embodiment of the present invention.
  • Figure 8 is a short-time Fourier transform time-frequency diagram of Data1 (107s-110s) provided by the embodiment of the present invention.
  • Figure 9 is a short-time Fourier transform time-frequency diagram of Data1 (117s-120s) provided by the embodiment of the present invention.
  • Figure 10 is a CWD contour diagram of Data1 (109.92s-110s) provided by the embodiment of the present invention.
  • Figure 11 is the CWD waterfall chart of Data1 (109.92s-110s) provided by the embodiment of the present invention.
  • Figure 12 is a schematic diagram of the change pattern of the Data2 rotating shaft speed provided by the embodiment of the present invention.
  • Figure 13 is a schematic diagram of the time domain waveform of the radial vibration of the Data2 bearing provided by the embodiment of the present invention.
  • Figure 14 is a spectrum diagram from 70s to 110s of Data1 provided by the embodiment of the present invention.
  • Figure 15 is a spectrum diagram from 110s to 120s of Data1 provided by the embodiment of the present invention.
  • Figure 16 is a spectrum diagram from 120s to 160s of Data1 provided by the embodiment of the present invention.
  • Figure 17 is a spectrum diagram from 80s to 130s of Data2 provided by the embodiment of the present invention.
  • Figure 18 is a spectrum diagram from 130s to 145s of Data2 provided by the embodiment of the present invention.
  • Figure 19 is a spectrum diagram from 145s to 155s of Data2 provided by the embodiment of the present invention.
  • Figure 20 is a short-time Fourier transform time-frequency diagram of Data2 (138s-141s) provided by the embodiment of the present invention.
  • Figure 21 is a short-time Fourier transform time-frequency diagram of Data2 (149s-152s) provided by the embodiment of the present invention.
  • Figure 22 is a CWD contour diagram of Data2 (138.16s-138.23s) provided by the embodiment of the present invention.
  • Figure 23 is the CWD waterfall chart of Data2 (138.16s-138.23s) provided by the embodiment of the present invention.
  • Figure 24 is a CWD contour map of Data2 (150.60s-150.68s) provided by the embodiment of the present invention.
  • Figure 25 is the CWD waterfall chart of Data2 (150.60s-150.68s) provided by the embodiment of the present invention.
  • Figure 26 is a schematic diagram of the hardware structure of a ball bearing fault determination device provided by an embodiment of the present invention.
  • Figure 27 is a schematic structural diagram of a chip provided by an embodiment of the present invention.
  • the core of this application is to provide a ball bearing fault determination method.
  • the core of this application is to provide a ball bearing fault determination method.
  • In liquid rocket engines about 7% of failures in rotating machinery are caused by rolling bearing failures. Improper assembly, poor lubrication, foreign matter intrusion, corrosion, and overloading during the operation of the bearings may cause overload. Early damage, even if the installation, lubrication and use and maintenance are normal, after a period of operation, the rolling bearing will suffer from fatigue spalling and wear and cannot work normally.
  • ball bearings urgently need to provide a method that can quickly and accurately determine bearing failure, which is particularly important.
  • FIG. 1 shows a step flow chart of a bearing fault determination method provided by an embodiment of the present invention.
  • the above-mentioned bearing fault determination method includes the following steps:
  • the wavelet packet decomposition method applies a pair of associated low-pass filters and high-pass filters to decompose the signal sequence into low-frequency and high-frequency parts at a certain scale.
  • the decomposed low-frequency and high-frequency parts are The part is further decomposed again to obtain more detailed frequency components, and the frequency band can be adaptively selected according to the characteristics of the signal to match the signal spectrum, thereby improving the time-frequency resolution.
  • the embodiment of the present invention is based on the wavelet packet decomposition method to achieve denoising processing of the vibration signal of the ball bearing, which can improve the accuracy of ball bearing fault location according to the embodiment of the present invention.
  • vibration signal denoising based on the wavelet packet decomposition method does not damage the original characteristics of the vibration signal, retains the original characteristics of the vibration signal, and eliminates noise pollution, laying a foundation for subsequent parameter identification of vibration signals.
  • Wavelet packet transform not only decomposes the low frequency part of the signal, but also decomposes the high frequency part.
  • the three-layer wavelet packet decomposition tree is shown in Figure 2, where A represents low frequency, D represents high frequency, and the serial number at the end represents the number of layers of wavelet packet decomposition.
  • the k-layer wavelet packet can decompose the original frequency band into 2k sub-bands, thus improving the frequency resolution.
  • wavelet packet decomposition can represent
  • W j is the wavelet subspace, j, k ⁇ Z.
  • the denoised signals of each period include:
  • the adaptive threshold algorithm based on Stein's unbiased likelihood estimation principle uses the wavelet packet decomposition
  • the high-frequency coefficients of each layer corresponding to the decomposed signals in each period are subjected to threshold processing to retain the wavelet packet coefficients that conform to the signal rules;
  • the wavelet packets of the signals of each period are reconstructed to obtain the denoising Processed signals of each time period.
  • the first test data Data1 The bearing speed changes and radial vibration time domain waveforms are shown in Figure 3 and Figure 4 respectively.
  • the signal in the 70-110s period of Data1 is subjected to signal denoising processing based on wavelet packet decomposition, and the prescribed orthogonal method with compact support is used For wavelet "Db4", the number of decomposition layers is 4, and the decomposed wavelet packet coefficients of each layer are shown in Figure 5.
  • the adaptive threshold algorithm based on Stein's unbiased likelihood estimation principle is used to retain the wavelet packet coefficients that conform to the signal rules and eliminate the wavelet packet coefficients that do not conform to the signal rules, and then perform wavelet packet recovery and reconstruction on the signal.
  • the original signal and denoising The signal comparison and residual signal characteristics are shown in Figure 6 and Figure 7 respectively.
  • the vibration signal of the ball bearing is denoised without damaging the original characteristics of the vibration signal, retaining the original characteristics of the vibration signal, and eliminating noise pollution. This provides a foundation for subsequent parameter identification of vibration signals.
  • S200 Use short-time Fourier transform to perform time-frequency analysis on the signals in each period to obtain the period in which the ball bearing failure occurs and the fault frequency corresponding to the period.
  • the results of using short-time Fourier transform to process the Data1 signal at 107-110s and 117-120s are shown in Figure 8 and Figure 9 respectively.
  • the Data1 signal begins to have a frequency component of around 1700Hz near 109s, and then continues until the end of the experiment.
  • the fault frequency includes the fault characteristic frequency of the ball bearing, according to the ball bearing
  • the characteristic frequency of the fault determines the duration of occurrence of the fault corresponding to the characteristic frequency of the fault.
  • the time-frequency analysis method based on Choi-Williams distribution is used to perform detailed analysis on the signal during the period when the ball bearing failure occurs.
  • the 109.92-110s period of the Data1 signal is analyzed in detail, as shown in Figure 10 and Figure 11.
  • the time-frequency diagram of the signal obtained by the time-frequency analysis method based on the Choi-Williams distribution is very clear.
  • the ball bearing QJS224 has a frequency component of about 1700Hz, which continues until the end of the test.
  • the ball bearing failure determination method further includes the following steps:
  • the characteristic frequencies of different faults of the ball bearing are calculated.
  • the calculation of bearing fault characteristic frequencies is the core.
  • an angular contact ball bearing is taken as an example.
  • the calculation formula of the bearing fault characteristic frequency can be derived by analyzing the relative motion relationship between the various components of the bearing. Assume that the outer ring of the ball bearing is fixed, the rotation frequency of the inner ring (ie, the main shaft) is f r , the pitch circle diameter of the ball bearing is d m , the ball diameter is D w , the contact angle is ⁇ , and the number of balls is Z, and assume There is pure rolling contact between the balls and the inner and outer rings of the bearing.
  • the rotation frequency of the cage can be obtained as
  • f o is the fault characteristic frequency of the bearing outer ring
  • f i is the fault characteristic frequency of the bearing outer ring
  • f b is the fault characteristic frequency of the bearing outer ring
  • f r is the rotation frequency of the inner ring of the ball bearing
  • d m is the pitch circle diameter of the ball bearing
  • D w is the ball diameter
  • ⁇ is the contact angle
  • Z is the number of balls.
  • the double half inner ring four-point contact ball bearing QJS224 of the heavy-duty liquid oxygen kerosene engine turbine oxygen pump malfunctioned during a certain operation test, resulting in test failure.
  • the first test data Data1 The bearing speed changes and radial vibration time domain waveforms are shown in Figures 3 and 4 respectively.
  • the bearing speed changes and radial vibration time domain waveforms of the second test data Data2 are shown in Figures 12 and 13 respectively.
  • the sampling frequency of the test system is 25600Hz, and the data amounts of Data1 and Data2 are approximately 5.54 million and 8.12 million respectively.
  • the bearing rotation frequency of Data1 fluctuates between 203.833Hz and 278.000Hz.
  • the bearing rotation frequency of Data2 Fluctuates between 218.167Hz ⁇ 264.833Hz; it can be seen from the time domain waveform of the radial vibration signal that Data1 can be roughly divided into three periods: 70s-110s, 110s-120s and 120s-160s, and Data2 can be roughly divided into 80s- There are three periods: 130s, 130s-145s and 145s-155s.
  • the characteristic frequencies corresponding to different faults of ball bearings can be obtained, such as bearing cage, outer ring , the fault characteristic frequencies of the inner ring and the ball are about 140Hz, about 1700Hz, about 2000Hz and about 800Hz respectively. It can be seen from Figures 14 to 19 that obvious rotational frequency division components appear in the spectrum of each period. Within the range of rotational speed fluctuations, the characteristic frequencies corresponding to different faults can be observed.
  • ball bearing failures are divided into bearing cage failures, bearing outer ring failures, bearing inner ring failures, and ball bearing failures.
  • the characteristic frequencies of the ball bearings include the characteristic frequencies of the bearing cage failure, the characteristic frequencies of the bearing outer ring failures, and the bearing failure. Characteristic frequencies of inner race faults and characteristic frequencies of ball faults.
  • the characteristic frequency of bearing cage failure is about 140Hz
  • the characteristic frequency of bearing outer ring failure is about 1700Hz
  • the characteristic frequency of bearing inner ring failure is about 2000Hz
  • the characteristic frequency of ball failure is about 800Hz.
  • S400 Determine the fault type and fault severity of the ball bearing according to the fault characteristic frequency of the ball bearing and the duration of fault occurrence corresponding to the fault characteristic frequency.
  • the above step S400 may include: S401, determining that the fault of the ball bearing is one or more of a bearing outer ring fault, a bearing inner ring fault and a ball fault according to the fault characteristic frequency of the ball bearing.
  • the fault of the ball bearing when the fault characteristic frequency of the ball bearing satisfies the characteristic frequency of the bearing outer ring fault of the ball bearing, the fault of the ball bearing is determined to be a bearing outer ring fault.
  • the fault characteristic frequency of the ball bearing satisfies the
  • the characteristic frequency of the fault of the inner ring of the ball bearing is determined, the fault of the ball bearing is determined to be the fault of the inner ring of the bearing.
  • the characteristic frequency of the fault of the ball bearing meets the characteristic frequency of the fault of the ball of the ball bearing, the fault of the ball bearing is determined
  • the fault is ball failure.
  • the fault severity of the ball bearing is determined according to the duration of fault occurrence corresponding to the fault characteristic frequency.
  • the fault occurrence duration corresponding to the fault characteristic frequency is less than or equal to the first duration, it is determined that the fault of the ball bearing is a minor fault; when the fault occurrence duration corresponding to the fault characteristic frequency exceeds the second duration, it is determined The failure of the ball bearing is a serious failure.
  • the first duration ranges from 0.01S to 0.1S; the second duration is greater than 0.1S.
  • the ball bearing QJS224 has a frequency component of about 1700Hz. At this time, the outer ring of the bearing fails.
  • the signals from 138.16s to 138.23s and 150.60s to 150.68s of the Data2 signal are analyzed in detail, as shown in Figures 22 to 25.
  • the time-frequency diagram of the signal obtained by the time-frequency analysis method based on the Choi-Williams distribution is very clear. A frequency of about 1700Hz appears from 138.17s to 138.21s.
  • the time-frequency distribution is point-like.
  • embodiments of the present invention perform denoising processing on the vibration signal of the ball bearing based on the wavelet packet decomposition method to eliminate noise information in the vibration signal and improve the accuracy of ball bearing fault location according to the embodiment of the present invention.
  • vibration signal denoising based on the wavelet packet decomposition method does not damage the original characteristics of the vibration signal, retains the original characteristics of the vibration signal, and eliminates noise pollution, laying a foundation for subsequent parameter identification of vibration signals.
  • the embodiment of the present invention uses short-time Fourier transform to perform time-frequency analysis on the signals in each period, which can quickly locate the period of ball bearing failure wind noise and the frequency of fault occurrence, so as to improve the detection of ball bearing faults to a certain extent. Definite efficiency.
  • the embodiment of the present invention uses the time-frequency analysis method of Choi-Williams distribution to conduct detailed analysis of the signal during the period when the ball bearing failure occurs, so as to obtain a clearer time-frequency diagram of the ball bearing failure, and then, Combined with the fault characteristic frequency of the ball bearing, the duration of the fault occurrence corresponding to the fault characteristic frequency can be determined, and the fault type and fault severity of the ball bearing can be accurately determined, thereby improving the working reliability and mechanics of the ball bearing.
  • Environmental adaptability is the time-frequency analysis method of Choi-Williams distribution to conduct detailed analysis of the signal during the period when the ball bearing failure occurs, so as to obtain a clearer time-frequency diagram of the ball bearing failure.
  • Figure 26 shows a schematic diagram of the hardware structure of a ball bearing fault determination device provided by an embodiment of the present invention.
  • the ball bearing-based fault determination device 80 includes a processor 801 and a communication interface 802 .
  • the above-mentioned processor can be a general central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more processors used to control the computer.
  • the invention proposes an integrated circuit for program execution.
  • There may be one or more communication interfaces.
  • the communication interface may use any device such as a transceiver for communicating with other devices or communication networks.
  • the above ball bearing failure determination device may further include a communication line 803 .
  • the communication line may include a path to carry information between the above-mentioned components.
  • the ball bearing fault determination device may also include a memory 804 .
  • the memory is used to store computer execution instructions for executing the solutions of the present invention, and is controlled by the processor for execution.
  • the processor is used to execute computer execution instructions stored in the memory, thereby implementing the method provided by the embodiment of the present invention.
  • the above-mentioned memory can be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (random access memory, RAM) or a Other types of dynamic storage devices for information and instructions, which may also be electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or Other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or can be used to carry or store desired information in the form of instructions or data structures Program code and any other medium capable of being accessed by a computer, without limitation.
  • the memory can exist independently and be connected to the processor through communication lines. Memory can also be integrated with the processor.
  • the computer-executed instructions in the embodiment of the present invention may also be called application codes, which are not specifically limited in the embodiment of the present invention.
  • the processor 801 may include one or more CPUs, such as CPU0 and CPU1 in Figure 26.
  • the ball bearing fault determination device may include multiple processors, such as processor 801-1 and processor 801-2 in FIG. 26 . Each of these processors can be a single-core processor or a multi-core processor.
  • Figure 27 is a schematic structural diagram of a chip provided by an embodiment of the present invention. As shown in FIG. 27 , the chip 90 includes one or more (including two) processors 801 and a communication interface 802 .
  • the chip also includes a memory 804, which can include read-only memory and random access memory, and provides operating instructions and data to the processor.
  • a memory 804 can include read-only memory and random access memory, and provides operating instructions and data to the processor.
  • Part of the memory may also include non-volatile random access memory (NVRAM).
  • NVRAM non-volatile random access memory
  • the memory stores the following elements, execution modules or data structures, or their subsets, or their extended sets.
  • the processor controls the processing operations of any one of the ball bearing fault determination devices.
  • the processor may also be called a central processing unit (CPU).
  • memory may include read-only memory and random access memory and provide instructions and data to the processor.
  • Part of the memory may also include NVRAM.
  • the memory, communication interface and memory are coupled together through a bus system.
  • the bus system may also include a power bus, a control bus, a status signal bus, etc.
  • the various buses are labeled bus system 805 in FIG. 27 .
  • the method disclosed in the above embodiment of the present invention can be applied in a processor, or implemented by the processor.
  • the processor may be an integrated circuit chip that has signal processing capabilities.
  • each step of the above method can be completed by instructions in the form of hardware integrated logic circuits or software in the processor.
  • the above-mentioned processor can be a general-purpose processor, digital signal processing (DSP), ASIC, off-the-shelf programmable gate array (field-programmable gate array, FPGA) or other programmable logic devices, discrete gates or transistor logic. devices, discrete hardware components.
  • DSP digital signal processing
  • ASIC application-the-shelf programmable gate array
  • FPGA field-programmable gate array
  • Each method, step and logical block diagram disclosed in the embodiment of the present invention can be implemented or executed.
  • a general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.
  • the steps of the method disclosed in conjunction with the embodiments of the present invention can be directly implemented by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor.
  • the software module can be located in random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers and other mature storage media in this field.
  • the storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.
  • the communication interface is used to obtain images collected by the camera.
  • the processor is used to execute steps 101 to 103 of the assessment and evaluation method in the embodiment shown in FIG. 1 .
  • a computer-readable storage medium is provided. Instructions are stored in the computer-readable storage medium. When the instructions are executed, the functions performed by the ball bearing fault determination device in the above embodiments are realized.
  • a chip is provided.
  • the chip is used in a ball bearing fault determination device.
  • the chip includes at least one processor and a communication interface.
  • the communication interface is coupled to at least one processor.
  • the processor is used to run instructions to implement the above embodiments.
  • the failure of a ball bearing determines the function performed by the equipment.
  • the computer program product includes one or more computer programs or instructions.
  • the computer may be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user equipment, or other programmable device.
  • the computer program or instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another.
  • the computer program or instructions may be transmitted from a website, computer, A server or data center transmits via wired or wireless means to another website site, computer, server, or data center.
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media.
  • the available media may be magnetic media, such as floppy disks, hard disks, and magnetic tapes; they may also be optical media, such as digital video discs (DVDs); they may also be semiconductor media, such as solid state drives (solid state drives). ,SSD).

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Abstract

一种球轴承的故障确定方法、设备以及存储介质，涉及滚动轴承技术领域。所述球轴承的故障确定方法包括：获取球轴承的振动信号，基于小波包分解方法对振动信号中的各时段信号进行消噪处理，得到消噪处理后的各时段信号(S100)；利用短时傅里叶变换对各时段的信号进行时频分析，得到球轴承故障发生的时段和时段对应的故障频率(S200)；基于Choi-Williams分布的时频分析法，对球轴承故障发生的时段的信号进行细化分析，当故障频率包括球轴承的故障特征频率时，根据球轴承的故障特征频率，确定故障特征频率对应的故障发生的时长(S300)；根据球轴承的故障特征频率，以及故障特征频率对应的故障发生的时长，确定球轴承的故障类型和故障严重程度(S400)。

Description

一种球轴承的故障确定方法、设备以及存储介质
本申请要求于2022年04月28日提交中国专利局、申请号为202210459251.0、申请名称为“一种球轴承的故障确定方法、设备以及存储介质”的中国专利优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及滚动轴承技术领域，尤其涉及一种球轴承的故障确定方法、设备以及存储介质。
背景技术
目前，在液体火箭发动机中，旋转机械中发生的故障有约7％是因滚动轴承故障而引发的，轴承在运转过程中由于装配不当、润滑不良、异物侵入、腐蚀和过载等都可能导致过早损伤，即使在安装、润滑和使用维护都正常的情况下，经过一段时间的运转，滚动轴承也会出现疲劳剥落和磨损而不能正常工作。
而球轴承作为滚动轴承中的重要类型，提供一种能够快速准确判断轴承发生故障的方法显得尤为重要。
发明内容
有鉴于此，本申请公开了一种球轴承的故障确定方法、设备以及存储介质，用于提供一种能够快速准确确定球轴承故障的技术方案。
第一方面，本发明提供了一种球轴承的故障确定方法，包括以下步骤：
获取所述球轴承的振动信号，基于小波包分解方法对所述振动信号中的各时段信号进行消噪处理，得到消噪处理后的各时段信号。
利用短时傅里叶变换对各时段的信号进行时频分析，得到所述球轴承故障发生的时段和所述时段对应的故障频率。
基于Choi-Williams分布的时频分析法，对所述球轴承故障发生的时段的信号进行细化分析，当所述故障频率包括所述球轴承的故障特征频率时，根 据所述球轴承的故障特征频率，确定所述故障特征频率对应的故障发生的时长。
根据所述球轴承的故障特征频率，以及所述故障特征频率对应的故障发生的时长，确定所述球轴承的故障类型和故障严重程度。
在采用上述技术方案的情况下，本发明基于小波包分解方法对所述球轴承的振动信号进行消噪处理，以消除振动信号中的噪声信息，提高本发明对球轴承故障定位的准确性。且基于小波包分解方法的振动信号消噪对振动信号的原始特征没有造成破坏，保留了振动信号的原始特性，而且消除了噪声污染，为后续振动信号的参数辨识提供了奠定了基础。
再者，本发明利用短时傅里叶变换对对各时段的信号进行时频分析，能够快速定位球轴承故障风声的时段，以及故障发生的频率，以在一定程度上提高球轴承故障确定的效率。
最后，由于短时傅里叶变换的窗函数大小和形状时固定的，不随时间和频率的变化而变化，因此，利用短时傅里叶变换获得的球轴承故障的时频图清晰度欠佳，基于此，本发明利用Choi-Williams分布的时频分析法对所述球轴承故障发生的时段的信号进行细化分析，以获取更为清晰的球轴承故障的时频图，然后，结合球轴承的故障特征频率，确定所述故障特征频率对应的故障发生的时长，进而可以准确的确定出所述球轴承的故障类型和故障严重程度，从而提高了球轴承的工作可靠性和力学环境适应性。
第二方面，本发明实施例提供了一种球轴承的故障确定设备，包括处理器以及与处理器耦合的通信接口；所述处理器用于运行计算机程序或指令，以实现球轴承的故障确定方法。
第三方面，本发明实施例提供了一种计算机存储介质，所述计算机存储 介质中存储有指令，当所述指令被运行时，实现球轴承的故障确定方法。
与现有技术相比，本发明第二方面以及第三方面与上述技术方案考核评价方法的有益效果相同，此处不做赘述。
附图说明
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图进行说明：
图1为本发明实施例提供的一种轴承的故障确定方法的步骤流程图；
图2为本发明实施例提供的一种三层小波包分解树示意图；
图3为本发明实施例提供的Data1转轴转速变化规律的示意图；
图4为本发明实施例提供的Data1轴承径向振动时域波形示意图；
图5为本发明实施例提供的将Data1的70s-110s时段的信号进行基于小波包分解的信号消噪处理，采用具有紧支集的规定正交小波“Db4”，分解层数为4层，则分解后的各层小波包系数示意图；
图6为本发明实施例提供的Data1(70s-110s)时段的原始信号与消噪信号的对比示意图；
图7为本发明实施例提供的Data1(70s-110s)时段的残余信号的特性；
图8为本发明实施例提供的Data1(107s-110s)的短时傅里叶变换时频图；
图9为本发明实施例提供的Data1(117s-120s)的短时傅里叶变换时频图；
图10为本发明实施例提供的Data1(109.92s-110s)的CWD等高线图；
图11为本发明实施例提供的Data1(109.92s-110s)的CWD瀑布图；
图12为本发明实施例提供的Data2转轴转速变化规律的示意图；
图13为本发明实施例提供的Data2轴承径向振动时域波形示意图；
图14为本发明实施例提供的Data1的70s-110s的频谱图；
图15为本发明实施例提供的Data1的110s-120s的频谱图；
图16为本发明实施例提供的Data1的120s-160s的频谱图；
图17为本发明实施例提供的Data2的80s-130s的频谱图；
图18为本发明实施例提供的Data2的130s-145s的频谱图；
图19为本发明实施例提供的Data2的145s-155s的频谱图；
图20为本发明实施例提供的Data2(138s-141s)的短时傅里叶变换时频图；
图21为本发明实施例提供的Data2(149s-152s)的短时傅里叶变换时频图；
图22为本发明实施例提供的Data2(138.16s-138.23s)的CWD等高线图；
图23为本发明实施例提供的Data2(138.16s-138.23s)的CWD瀑布图；
图24为本发明实施例提供的Data2(150.60s-150.68s)的CWD等高线图；
图25为本发明实施例提供的Data2(150.60s-150.68s)的CWD瀑布图
图26为本发明实施例提供的一种球轴承的故障确定设备的硬件结构示意图；
图27为本发明实施例提供的一种芯片的结构示意图。
附图标记：801-1-处理器，801-2-处理器，801-处理器，802-通信接口，803-通信线路，804-存储器，805-总线系统，90-芯片。
具体实施方式
本申请的核心是提供了一种球轴承的故障确定方法。据统计，在液体火箭发动机中，旋转机械中发生的故障有约7％是因滚动轴承故障而引发的，轴承在运转过程中由于装配不当、润滑不良、异物侵入、腐蚀和过载等都可能导致过早损伤，即使在安装、润滑和使用维护都正常的情况下，经过一段时间的运转，滚动轴承也会出现疲劳剥落和磨损而不能正常工作。
而球轴承作为滚动轴承中的重要类型，急需提供一种能够快速准确判断轴承发生故障的方法显得尤为重要。
基于此，图1示出了本发明实施例提供的一种轴承的故障确定方法的步骤流程图。上述轴承的故障确定方法包括以下步骤：
S100，获取所述球轴承的振动信号，基于小波包分解方法对所述振动信号中的各时段信号进行消噪处理，得到消噪处理后的各时段信号。
应理解，在实际对球轴承的振动信号的测量中，由于受设备工作环境、测试仪器以及旋转机械的强转速波动等干扰因素，现场采集的振动信号往往被各种噪声污染。在球轴承的故障定位和诊断中，消噪效果直接影响后续的故障分析。为了消除振动信号中的噪声，传统的方法有最优滤波、最优估计方法以及自适应滤波方法等。但是在运用这些方法时，需要一定的先验知识和特定假设，如噪声类型和信号的结构等。近年来，由于小波变换具有低熵 性、多分辨率特性、去相关性以及选基灵活性等特点，因此，基于小波分解的信号消噪方法获得了国内外众多研究者的关注，并取得了一定的研究成果。
由于有用信号与噪声信号具有不同的Lipschitz指数，具体表现为有用信号的小波分解系数随分解层数的增大而变化缓慢，噪声的小波分解系数随分解层数的增大而迅速减小。小波包分解方法应用一对相关联的低通滤波器和高通滤波器，将信号序列分解为某一尺度下的低频和高频两部分，在改变尺度的情况下对已分解的低频和高频部分再次进一步分解，获取更为细化的频率成分，并能够根据信号的特征，自适应地选择频段，与信号频谱相互匹配，从而提高时频分辨率。因此本发明实施例基于小波包分解方法，实现对球轴承的振动信号的消噪处理，能够提高本发明实施例对球轴承故障定位的准确性。且基于小波包分解方法的振动信号消噪对振动信号的原始特征没有造成破坏，保留了振动信号的原始特性，而且消除了噪声污染，为后续振动信号的参数辨识提供了奠定了基础。
小波包变换不仅对信号低频进行分解，同时也对高频部分也进行分解。三层小波包分解树如图2所示，其中，A表示低频，D表示高频，末尾的序号数表示小波包分解的层数。
小波包分解具有如下关系式：
S＝AAA3+DAA3+ADA3+DDA3+AAD3+DAD3+ADD3+DDD3    (1)
因此，k层小波包可将原频带分解成2k个子频带，从而提高了频率分辨率。
根据多分辨率分析的基本理论，小波包分解可以表示
式中，Wj为小波子空间，j，k∈Z。
若采用频率为fs，则对应的频率范围为
令函数在子空间的小波包变换系数为因此函数f(x)可展开为
由式(4)可知，函数f(x)在子空间投影等价于f(x)在子空间和的投影之和，分别记作f1(x)和f2(x)，可推导出如下表达式

推导出和的小波包分解方法为
推导出和的小波包重构算法为
获取所述球轴承的振动信号，基于小波包分解方法对所述振动信中的各时段信号进行消噪处理，得到消噪处理后的各时段信号具体包括：
获取所述球轴承的振动信号，利用小波包分解方法对所述振动信中的各时段信号进行分解；
基于Stein无偏似然估计原理的自适应阈值算法，对利用所述小波包分解 方法分解后的各时段信号对应的每一层高频系数进行阈值处理，以保留符合信号规律的小波包系数；
根据对利用所述小波包分解方法分解后的各时段信号对应的最低层的低频系数和经过阈值处理后的每一层高频系数，对所述各时段信号进行小波包重构，得到消噪处理后的各时段信号。
在本发明实施例中，一种重型液氧煤油发动机涡轮氧泵的双半内圈四点接触球轴承QJS224在某次运转试验中出现了故障，导致了试验失败，其中第一次测试数据Data1的轴承转速变化和径向振动时域波形分别如图3和图4所示，将Data1的70-110s时段的信号进行基于小波包分解的信号消噪处理，采用具有紧支集的规定正交小波“Db4”，分解层数为4层，则分解后的各层小波包系数如图5所示。采用基于Stein无偏似然估计原理的自适应阈值算法，保留符合信号规律的小波包系数和剔除不符合信号规律的小波包系数，然后对信号进行小波包恢复重构，则原始信号和消噪信号的对比以及残余信号特性分别如图6、图7所示。
从图6和图7中可以看出，基于小波包分解方法，对球轴承的振动信号消噪，对振动信号的原始特征没有造成破坏，保留了振动信号的原始特征，而且消除了噪声污染，为后续振动信号的参数辨识提供了奠定了基础。
S200，利用短时傅里叶变换对各时段的信号进行时频分析，得到所述球轴承故障发生的时段和所述时段对应的故障频率。
具体的，利用短时傅里叶变换对Data1信号在107-110s和117-120s的信号进行处理的结果分别如图8、图9所示。从图8和图9中可以看出，Data1信号在109s附近开始出现1700Hz左右的频率成分，然后一直延续到试验结束。
S300，基于Choi-Williams分布的时频分析法，对所述球轴承故障发生的时段的信号进行细化分析，当所述故障频率包括所述球轴承的故障特征频率时，根据所述球轴承的故障特征频率，确定所述故障特征频率对应的故障发生的时长。
由于短时傅里叶变化的窗函数大小和形状固定，不随时间和频率的变化而变化，因此图8、图9所示的短时傅里叶变换时频图清晰度较为欠缺，因此， 本发明实施例在基于Choi-Williams分布的时频分析法，对所述球轴承故障发生的时段的信号进行细化分析。
根据Choi-Williams分布的时频分析法，对Data1信号的109.92～110s时段进行细化分析，如图10、图11所示。
从图10和图11可以看出，基于Choi-Williams分布的时频分析法获得的信号时频图十分清晰，此时球轴承QJS224出现1700Hz左右的频率成分，一直延续到试验结束。
在基于Choi-Williams分布的时频分析法，对所述球轴承故障发生的时段的信号进行细化分析之前，所述球轴承的故障确定方法还包括以下步骤：
基于所述球轴承的尺寸参数和转速频率，计算所述球轴承不同故障的特征频率。
在实际中，在滚动轴承的动特性参数辨识中，轴承故障特征频率的计算是核心。此处以角接触球轴承为例，以轴承的力学特性模型为基础，通过分析轴承各元件之间的相对运动关系可推导轴承故障特征频率的计算公式。设球轴承的外圈固定，内圈(即主轴)的旋转频率为fr，球轴承的节圆直径为dm，滚珠直径为Dw，接触角为α，滚珠个数为Z，并假设滚珠与轴承内、外圈之间为纯滚动接触。
通过分析球轴承各元件之间的相对运动关系，可得保持架转动频率为
Z个滚珠与外圈上某一固定点接触的频率为
Z个滚珠与内圈上某一固定点接触的频率为
滚珠上某一固定点与轴承外圈或内圈接触的频率为
式中，fo为所述轴承外圈的故障特征频率，fi为所述轴承外圈的故障特征频率，fb为所述轴承外圈的故障特征频率，当所述球轴承的外圈固定，fr为所述球轴承内圈的旋转频率，dm为球轴承的节圆直径为，Dw为滚珠直径，α为接触角，Z为滚珠个数。
在本发明实施例中，提供了重型液氧煤油发动机涡轮氧泵的双半内圈四点接触球轴承QJS224在某次运转试验中出现了故障，导致了试验失败，其中第一次测试数据Data1的轴承转速变化和径向振动时域波形分别如图3和图4所示，第二次测试数据Data2的轴承转速变化和径向振动时域波形分别如图12和图13所示。试验系统的采样频率为25600Hz，Data1和Data2的数据量分别约为554万和812万。
从图3、图4、图12和图13可以看出，球轴承QJS224振动信号的成分随着时间在不断变化，Data1的轴承转频在203.833Hz～278.000Hz之间波动，Data2的轴承转频在218.167Hz～264.833Hz之间波动；从径向振动信号的时域波形可以看出，Data1大致可以分为70s-110s、110s-120s和120s-160s三个时段，Data2大致可以分为80s-130s、130s-145s和145s-155s三个时段。
将Data1和Data2不同时段的信号进行傅里叶变换，可得不同时段的频谱，其中Data1的三个不同时段的频谱如图14～图16所示，Data2的三个不同时段的频谱如图17～图19所示。
根据球轴承的完全动力学特性研究，结合球轴承QJS224的尺寸参数和转速频率，根据式(9)～式(12)，可得到球轴承不同故障对应的特征频率，如轴承保持架、外圈、内圈和滚珠的故障特征频率分别为140Hz左右、1700Hz左右、2000Hz左右和800Hz左右。从图14～图19中可以看出，在各时段频谱中出现了明显的转频分频成分，在转速波动范围内，可以观察不同故障对应的特征频率。
因此，球轴承的故障分为轴承保持架、轴承外圈故障、轴承内圈故障以及滚珠故障，所述球轴承的特征频率包括轴承保持架故障的特征频率，轴承外圈故障的特征频率，轴承内圈故障的特征频率以及滚珠故障的特征频率。 轴承保持架故障的特征频率为140Hz左右，轴承外圈故障的特征频率为1700Hz左右，轴承内圈故障的特征频率为2000Hz左右以及滚珠故障的特征频率为800Hz左右。
S400，根据所述球轴承的故障特征频率，以及所述故障特征频率对应的故障发生的时长，确定所述球轴承的故障类型和故障严重程度。
上述步骤S400可以包括：S401，根据所述所述球轴承的故障特征频率确定所述球轴承的故障为轴承外圈故障、轴承内圈故障以及滚珠故障中的一种或多种。
具体的，当所述球轴承的故障特征频率满足所述球轴承的轴承外圈故障的特征频率时，确定所述球轴承的故障为轴承外圈故障，当球轴承的故障特征频率满足所述球轴承的轴承内圈故障的特征频率时，确定所述球轴承的故障为轴承内圈故障，当球轴承的故障特征频率满足所述球轴承的滚珠故障的特征频率时，确定所述球轴承的故障为滚珠故障。
根据所述故障特征频率对应的故障发生的时长确定所述球轴承的故障严重程度。
当所述故障特征频率对应的故障发生的时长小于或等于第一时长时，确定所述球轴承的故障为轻微故障；当所述故障特征频率对应的故障发生的时长超过第二时长时，确定所述球轴承的故障为严重故障。其中，第一时长范围为0.01S-0.1S；所述第二时长大于0.1S。
示例性的，从图10和图11可以看出，球轴承QJS224出现1700Hz左右的频率成分，此时，轴承外圈发生了故障。
Data2信号在138s-141s和149s-152s的短时傅里叶变换的结果分别如图20、图21所示。由于短时傅里叶变换算法的局限性，分析结果显示频率在1600～1900Hz之间，难以区分。
根据Choi-Williams分布的时频分析法，对Data2信号的138.16s～138.23s和150.60s～150.68s的信号进行细化分析，如图22～图25所示。基于Choi-Williams分布的时频分析法获得的信号时频图十分清晰，在138.17s～138.21s中出现了1700Hz左右的频率，时频分布是点状的，此时球轴承QJS224的外圈还只是轻微故障，但从150.60s往后到试验结束，1700Hz左 右的频率连续出现，说明从150.60s开始，球轴承QJS224的外圈发生了严重的破坏。
基于以上描述，本发明实施例基于小波包分解方法对所述球轴承的振动信号进行消噪处理，以消除振动信号中的噪声信息，提高本发明实施例对球轴承故障定位的准确性。且基于小波包分解方法的振动信号消噪对振动信号的原始特征没有造成破坏，保留了振动信号的原始特性，而且消除了噪声污染，为后续振动信号的参数辨识提供了奠定了基础。
再者，本发明实施例利用短时傅里叶变换对对各时段的信号进行时频分析，能够快速定位球轴承故障风声的时段，以及故障发生的频率，以在一定程度上提高球轴承故障确定的效率。
最后，由于短时傅里叶变换的窗函数大小和形状时固定的，不随时间和频率的变化而变化，因此，利用短时傅里叶变换获得的球轴承故障的时频图清晰度欠佳，基于此，本发明实施例利用Choi-Williams分布的时频分析法对所述球轴承故障发生的时段的信号进行细化分析，以获取更为清晰的球轴承故障的时频图，然后，结合球轴承的故障特征频率，确定所述故障特征频率对应的故障发生的时长，进而可以准确的确定出所述球轴承的故障类型和故障严重程度，从而提高了球轴承的工作可靠性和力学环境适应性。
图26示出了本发明实施例提供的一种球轴承的故障确定设备的硬件结构示意图。如图26所示，该基于球轴承的故障确定设备80包括处理器801和通信接口802。
如图26所示，上述处理器可以是一个通用中央处理器(central processing unit，CPU)，微处理器，专用集成电路(application-specific integrated circuit，ASIC)，或一个或多个用于控制本发明方案程序执行的集成电路。上述通信接口可以为一个或多个。通信接口可使用任何收发器一类的装置，用于与其他设备或通信网络通信。
如图26所示，上述球轴承的故障确定设备还可以包括通信线路803。通信线路可包括一通路，在上述组件之间传送信息。
可选的，如图26所示，该球轴承的故障确定设备还可以包括存储器804。存储器用于存储执行本发明方案的计算机执行指令，并由处理器来控制执行。 处理器用于执行存储器中存储的计算机执行指令，从而实现本发明实施例提供的方法。
如图26所示，上述存储器可以是只读存储器(read-only memory，ROM)或可存储静态信息和指令的其他类型的静态存储设备，随机存取存储器(random access memory，RAM)或者可存储信息和指令的其他类型的动态存储设备，也可以是电可擦可编程只读存储器(electrically erasable programmable read-only memory，EEPROM)、只读光盘(compact disc read-only memory，CD-ROM)或其他光盘存储、光碟存储(包括压缩光碟、激光碟、光碟、数字通用光碟、蓝光光碟等)、磁盘存储介质或者其他磁存储设备、或者能够用于携带或存储具有指令或数据结构形式的期望的程序代码并能够由计算机存取的任何其他介质，但不限于此。存储器可以是独立存在，通过通信线路与处理器相连接。存储器也可以和处理器集成在一起。
可选的，本发明实施例中的计算机执行指令也可以称之为应用程序代码，本发明实施例对此不作具体限定。
在具体实现中，作为一种实施例，如图26所示，处理器801可以包括一个或多个CPU，如图26中的CPU0和CPU1。
在具体实现中，作为一种实施例，如图26所示，球轴承的故障确定设备可以包括多个处理器，如图26中的处理器801-1和处理器801-2。这些处理器中的每一个可以是一个单核处理器，也可以是一个多核处理器。
图27是本发明实施例提供的芯片的结构示意图。如图27所示，该芯片90包括一个或两个以上(包括两个)处理器801和通信接口802。
可选的，如图27所示，该芯片还包括存储器804，存储器可以包括只读存储器和随机存取存储器，并向处理器提供操作指令和数据。存储器的一部分还可以包括非易失性随机存取存储器(non-volatile random access memory，NVRAM)。
在一些实施方式中，如图27所示，存储器存储了如下的元素，执行模块或者数据结构，或者他们的子集，或者他们的扩展集。
在本发明实施例中，如图27所示，通过调用存储器存储的操作指令(该操作指令可存储在操作系统中)，执行相应的操作。
如图27所示，处理器控制球轴承的故障确定设备中任一个的处理操作，处理器还可以称为中央处理单元(central processing unit，CPU)。
如图27所示，存储器可以包括只读存储器和随机存取存储器，并向处理器提供指令和数据。存储器的一部分还可以包括NVRAM。例如应用中存储器、通信接口以及存储器通过总线系统耦合在一起，其中总线系统除包括数据总线之外，还可以包括电源总线、控制总线和状态信号总线等。但是为了清楚说明起见，在图27中将各种总线都标为总线系统805。
如图27所示，上述本发明实施例揭示的方法可以应用于处理器中，或者由处理器实现。处理器可能是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法的各步骤可以通过处理器中的硬件的集成逻辑电路或者软件形式的指令完成。上述的处理器可以是通用处理器、数字信号处理器(digital signal processing，DSP)、ASIC、现成可编程门阵列(field-programmable gate array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本发明实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。结合本发明实施例所公开的方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器，处理器读取存储器中的信息，结合其硬件完成上述方法的步骤。
一种可能的实现方式中，如图27所示，通信接口用于获得相机采集的图像。处理器用于执行图1所示的实施例中的考核评价方法的步骤101至步骤103。
一方面，提供一种计算机可读存储介质，计算机可读存储介质中存储有指令，当指令被运行时，实现上述实施例中由球轴承的故障确定设备执行的功能。
一方面，提供一种芯片，该芯片应用于球轴承的故障确定设备中，芯片包括至少一个处理器和通信接口，通信接口和至少一个处理器耦合，处理器用于运行指令，以实现上述实施例中由球轴承的故障确定设备执行的功能。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机程序或指令。在计算机上加载和执行所述计算机程序或指令时，全部或部分地执行本发明实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、终端、用户设备或者其它可编程装置。所述计算机程序或指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机程序或指令可以从一个网站站点、计算机、服务器或数据中心通过有线或无线方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是集成一个或多个可用介质的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质，例如，软盘、硬盘、磁带；也可以是光介质，例如，数字视频光盘(digital video disc，DVD)；还可以是半导体介质，例如，固态硬盘(solid state drive，SSD)。
尽管在此结合各实施例对本发明进行了描述，然而，在实施所要求保护的本发明过程中，本领域技术人员通过查看附图、公开内容、以及所附权利要求书，可理解并实现公开实施例的其他变化。在权利要求中，“包括”(comprising)一词不排除其他组成部分或步骤，“一”或“一个”不排除多个的情况。单个处理器或其他单元可以实现权利要求中列举的若干项功能。相互不同的从属权利要求中记载了某些措施，但这并不表示这些措施不能组合起来产生良好的效果。
尽管结合具体特征及其实施例对本发明进行了描述，显而易见的，在不脱离本发明的精神和范围的情况下，可对其进行各种修改和组合。相应地，本说明书和附图仅仅是所附权利要求所界定的本发明的示例性说明，且视为已覆盖本发明范围内的任意和所有修改、变化、组合或等同物。显然，本领域的技术人员可以对本发明进行各种改动和变型而不脱离本发明的精神和范围。这样，倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内，则本发明也意图包括这些改动和变型在内。

Claims (8)

  1. 一种球轴承的故障确定方法，其特征在于，所述球轴承的故障确定方法包括以下步骤：
    获取所述球轴承的振动信号，基于小波包分解方法对所述振动信号中的各时段信号进行消噪处理，得到消噪处理后的各时段信号；
    利用短时傅里叶变换对各时段的信号进行时频分析，得到所述球轴承故障发生的时段和所述时段对应的故障频率；
    基于Choi-Williams分布的时频分析法，对所述球轴承故障发生的时段的信号进行细化分析，当所述时段对应的故障频率包括所述球轴承的故障特征频率时，根据所述球轴承的故障特征频率，确定所述故障特征频率对应的故障发生的时长；
  2. 根据所述球轴承的故障特征频率，以及所述故障特征频率对应的故障发生的时长，确定所述球轴承的故障类型和故障严重程度。
    根据权利要求1所述的球轴承的故障确定方法，其特征在于：获取所述球轴承的振动信号，基于小波包分解方法对所述振动信中的各时段信号进行消噪处理，得到消噪处理后的各时段信号包括：
    获取所述球轴承的振动信号，利用小波包分解方法对所述振动信中的各时段信号进行分解；
    基于Stein无偏似然估计原理的自适应阈值算法，对利用所述小波包分解方法分解后的各时段信号对应的每一层高频系数进行阈值处理，以保留符合信号规律的小波包系数；
    根据对利用所述小波包分解方法分解后的各时段信号对应的最低层的低频系数和经过阈值处理后的每一层高频系数，对所述各时段信号进行小波包重构，得到消噪处理后的各时段信号。
  3. 根据权利要求1所述的球轴承的故障确定方法，其特征在于，在基于Choi-Williams分布的时频分析法，对所述球轴承故障发生的时段的信号进行细化分析之前，所述球轴承的故障确定方法还包括以下步骤：
    基于所述球轴承的尺寸参数和转速频率，计算所述球轴承不同故障的特征频率。
  4. 根据权利要求3所述的球轴承的故障确定方法，其特征在于，所述球轴承的故障包括轴承保持架、轴承外圈故障、轴承内圈故障以及滚珠故障，所述球轴承的特征频率包括轴承保持架故障的特征频率、轴承外圈故障的特征频率、轴承内圈故障的特征频率以及滚珠故障的特征频率。
  5. 根据权利要求4所述的球轴承的故障确定方法，其特征在于，基于所述球轴承的尺寸参数和转速频率，计算所述球轴承的不同故障的特征频率的表达式为：


    其中，fo为所述轴承外圈的故障特征频率，fi为所述轴承外圈的故障特征频率，fb为所述轴承外圈的故障特征频率，当所述球轴承的外圈固定，fr为所述球轴承内圈的旋转频率，dm为球轴承的节圆直径为，Dw为滚珠直径，α为接触角，Z为滚珠个数。
  6. 根据权利要求4所述的球轴承的故障确定方法，其特征在于，根据所述球轴承的故障特征频率，以及所述故障特征频率对应的故障发生的时长，确定所述球轴承的故障类型和故障严重程度包括：
    根据所述所述球轴承的故障特征频率确定所述球轴承的故障为轴承外圈故障、轴承内圈故障以及滚珠故障中的一种或多种；
    根据所述故障特征频率对应的故障发生的时长确定所述球轴承的故障严重程度。
  7. 根据权利要求6所述的球轴承的故障确定方法，其特征在于，根据所 述所述球轴承的故障特征频率确定所述球轴承的故障为轴承外圈故障、轴承内圈故障以及滚珠故障中的一种或多种包括：
    当所述球轴承的故障特征频率满足所述球轴承的轴承外圈故障的特征频率时，确定所述球轴承的故障为轴承外圈故障，当球轴承的故障特征频率满足所述球轴承的轴承内圈故障的特征频率时，确定所述球轴承的故障为轴承内圈故障，当球轴承的故障特征频率满足所述球轴承的滚珠故障的特征频率时，确定所述球轴承的故障为滚珠故障；
    根据所述故障特征频率对应的故障发生的时长确定所述球轴承的故障严重程度包括：
    当所述故障特征频率对应的故障发生的时长小于或等于第一时长时，确定所述球轴承的故障为轻微故障；当所述故障特征频率对应的故障发生的时长超过第二时长时，确定所述球轴承的故障为严重故障。
  8. 根据权利要求7所述的球轴承的故障确定方法，其特征在于，所述第一时长范围为0.01S-0.1S；所述第二时长大于0.1S。
    一种球轴承的故障确定设备，其特征在于，包括处理器以及与处理器耦合的通信接口；所述处理器用于运行计算机程序或指令，以实现权利要求1-8任一项所述的球轴承的故障确定方法。
    一种计算机存储介质，其特征在于，所述计算机存储介质中存储有指令，当所述指令被运行时，实现权利要求1至8任一项所述的球轴承的故障确定方法。
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