Multi-parameter monitoring bearing vibration fault diagnosis system

Abstract

The invention discloses a bearing vibration fault diagnosis system based on multi-parameter monitoring, and the system comprises a parameter setting module which is used for setting the sampling frequency and sampling length of signal collection, the parameters of a to-be-detected bearing, and the characteristic frequency of a defect, and the parameter setting module is in communication with an external sensor so as to receive bearing data collected by the external sensor; and the intelligent analysis module is internally provided with a vibration threshold value, a waviness abnormity reference parameter and a scratch reference parameter, and the intelligent analysis module communicates with the parameterization setting module, receives the collected bearing data, analyzes the bearing data, outputs an analysis result of the bearing and judges whether the bearing has a vibration fault or not. According to the multi-parameter monitoring bearing vibration fault diagnosis system, through arrangement of the parameter setting module and the intelligent analysis module, diagnosis of bearing vibration faults can be simply and effectively realized.

Description

Technical Field

[0001] The present invention relates to a fault diagnosis system, and more particularly to a bearing vibration fault diagnosis system with multi-parameter monitoring. Background Art

[0002] There are many types of rolling bearings, but rolling bearings are the most widely produced and widely used, accounting for over 70% of my country's total bearing production. However, if a bearing exhibits poor vibration performance during production, it is likely to continue to deteriorate during subsequent service, becoming a source of quality issues. Therefore, it is essential to explore technologies for identifying and diagnosing weak defect signals during the bearing production process. This research could overcome the influence of human factors on bearing vibration performance evaluation, enabling automated online vibration measurement and defect diagnosis, with significant theoretical and practical value. However, currently, no relevant systems exist in the existing technology for diagnosing bearing vibration faults. Summary of the Invention

[0003] In view of the shortcomings of the prior art, the object of the present invention is to provide a system that can effectively diagnose bearing vibration faults.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solution: a bearing vibration fault diagnosis system with multi-parameter monitoring, characterized in that it includes: A parameter setting module is used to set the sampling frequency and sampling length of signal acquisition, the parameters of the bearing to be detected, and the characteristic frequency of the defect. The parameter setting module communicates with the external sensor to receive the bearing data collected by the external sensor; An intelligent analysis module, which is equipped with vibration thresholds, waviness abnormality reference parameters, and scratch reference parameters. The intelligent analysis module communicates with the parameter setting module, receives and analyzes the collected bearing data, and then outputs the analysis results of the bearing to determine whether the bearing has a vibration fault; The intelligent analysis module analyzes bearing data in the following way: first, it analyzes the vibration data in the bearing data in the time domain to determine whether the vibration is generated inside the bearing. Then, it uses frequency domain analysis to determine whether the bearing waviness is abnormal. Then, it uses envelope spectrum analysis to determine whether the abnormality is caused by scratches. Finally, it combines the above judgment results to finally output whether the bearing has a vibration fault.

[0005] As a further improvement of the present invention, the parameter setting module includes: System parameter setting module, which is used to set the sampling frequency and sampling length of signal acquisition according to the observed frequency range; The bearing parameter setting module is used to input the pitch diameter, steel ball diameter, number of steel balls, contact angle and spindle rotation speed of the bearing to be diagnosed; The special frequency point setting module is used to input the characteristic frequency of the defect.

[0006] As a further improvement of the present invention, the intelligent analysis module includes: The time domain analysis judgment threshold setting module is used to input the vibration value, peak count one, peak count three, peak factor, and kurtosis threshold of the detected bearing based on experience or customer needs, and determine whether the vibration peak of the bearing to be diagnosed is within the threshold range through time domain analysis; The frequency domain analysis judgment threshold setting module is used to judge the abnormal factor parameters of the bearing according to experience or customer needs, including outer ring waviness, inner ring waviness, steel ball waviness, mixed balls / missing balls, and determine whether the frequency of the bearing to be tested matches the characteristic frequency of the defect through frequency domain analysis, and then determine whether the bearing to be tested has abnormal waviness. The judgment threshold setting module of envelope spectrum analysis is used to judge the abnormal factor parameters of the bearing based on experience or customer needs, including outer ring scratches, inner ring scratches, steel ball scratches and the degree of damage at a specific frequency. It determines whether the bearing abnormality is caused by scratches by judging whether the abnormal factor parameters are met; The bearing model parameter storage module is used to store input bearing models and parameters.

[0007] As a further improvement of the present invention, the judgment threshold setting module of the time domain analysis will display the data collected and analyzed in the time domain in the form of a curve image during the analysis and detection process, and output the data in the form of Excel, and the time domain diagnosis results will be displayed according to the color of the light, green represents qualified and gray represents unqualified.

[0008] As a further improvement of the present invention, the judgment threshold setting module of the frequency domain analysis presents the frequency domain analysis data in the form of a curve image during the analysis and detection process, and outputs the data in the form of Excel, and the frequency domain diagnosis results are displayed according to the color of the light, green represents qualified and gray represents unqualified.

[0009] As a further improvement of the present invention, the judgment threshold setting module of the envelope spectrum analysis presents the data of the envelope spectrum analysis in the form of a curve image during the analysis and detection process, and outputs the data in the form of Excel, and the envelope spectrum analysis diagnosis results are displayed according to the color of the light, green represents qualified and gray represents unqualified.

[0010] The beneficial effect of the present invention is that, through the setting of the parameter setting module, the parameter input of the bearing to be tested and the collection of external sensor data can be effectively realized, and further through the setting of the intelligent analysis module, the parameters set by the parameter setting module and the collected external sensor data can be effectively analyzed and processed, thereby realizing the analysis and processing of time domain parameters, corrugation parameters in the frequency domain state and scratch degree parameters of envelope spectrum analysis through time domain, frequency domain and envelope spectrum analysis. The time domain parameters include vibration value, peak count one, peak count three, peak factor and kurtosis, which can preliminarily reflect the vibration state of the bearing and conditions such as excessive garbage or lubricant particles; the corrugation parameters include inner and outer ring corrugation and steel ball corrugation, which can reflect the inner and outer ring profiles of the bearing and abnormal steel ball profiles; the scratch degree parameters include inner and outer ring scratches, steel ball scratches and other specific frequencies, which can reflect the inner and outer ring damage of the bearing, steel ball damage and special fault conditions, and have the characteristics of convenience, speed, reliability and practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is the system block diagram; Figure 2 This is a schematic diagram of the system parameter setting interface; Figure 3 This is a schematic diagram of the bearing parameter setting interface; Figure 4 Schematic diagram of the interface for setting up special frequency points for observation; Figure 5 Schematic diagram of the interface for setting the judgment threshold for time domain analysis; Figure 6 Schematic diagram of the interface for setting the judgment threshold for frequency domain analysis; Figure 7 Schematic diagram of the interface for setting the judgment threshold of envelope spectrum analysis; Figure 8 Schematic diagram of the system workflow; Figure 9 This is a detailed diagram of the system detection steps; Figure 10 This is a schematic diagram of the time domain diagnosis results; Figure 11 This is a schematic diagram of the frequency domain diagnosis results; Figure 12 Schematic diagram of envelope spectrum diagnosis results. DETAILED DESCRIPTION

[0012] The present invention will be further described below with reference to the embodiments shown in the accompanying drawings.

[0013] Reference Figures 1 to 12 As shown, a bearing vibration fault diagnosis system with multi-parameter monitoring in this embodiment is characterized by comprising: A parameter setting module is used to set the sampling frequency and sampling length of signal acquisition, the parameters of the bearing to be detected, and the characteristic frequency of the defect. The parameter setting module communicates with the external sensor to receive the bearing data collected by the external sensor; An intelligent analysis module, which is equipped with vibration thresholds, waviness abnormality reference parameters, and scratch reference parameters. The intelligent analysis module communicates with the parameter setting module, receives and analyzes the collected bearing data, and then outputs the analysis results of the bearing to determine whether the bearing has a vibration fault; Among them, the specific way in which the intelligent analysis module analyzes the bearing data is as follows: first, through time domain analysis of the vibration data in the bearing data, it is determined whether the vibration is generated inside the bearing; then, through frequency domain analysis, it is determined whether the bearing waviness is abnormal; then, through envelope spectrum analysis, it is determined whether the abnormality is caused by scratches; finally, based on the above judgment results, it is finally output whether there is a vibration fault in the bearing. The combination of the parameter setting module and the intelligent analysis module can realize the step-by-step time domain analysis, frequency domain analysis and envelope spectrum analysis of the bearing parameter data, so as to realize the step-by-step determination of whether the vibration is generated inside the bearing; then, based on whether the vibration is caused by abnormal waviness, it is further determined whether the vibration is caused by the inner ring or scratches, thereby effectively realizing the detection of the bearing. In addition to having the above parameter setting module and intelligent analysis module, the system in this embodiment also includes an acceleration sensor, a charge amplifier, an A / D data acquisition card, an audio system and a host computer (wherein the parameter setting module and the intelligent analysis module are arranged in the host computer). The lower computer mainly applies axial force through the claw plate to fix the outer ring of the bearing. The core shaft is driven by the motor to drive the inner ring of the bearing to rotate. A sensor is installed on the outer ring of the bearing to collect the radial vibration signal of the bearing during operation. The vibration signal is further analyzed from the perspectives of time domain, frequency domain and envelope spectrum by the upper computer, and the fault status of the bearing is judged according to the monitored parameter indicators.

[0014] The parameter setting module and intelligent analysis module are further described below; The parameter setting module includes: System parameter setting module, which is used to set the sampling frequency and sampling length of signal acquisition according to the observed frequency range, and to select the appropriate magnification for the convenience of intuitive numerical display; The bearing parameter setting module is used to input the pitch diameter, steel ball diameter, number of steel balls, contact angle and spindle rotation speed of the bearing to be diagnosed; The special frequency point setting module for observation is used to input the characteristic frequency of the defect. The characteristic frequency of the defect is calculated by the input bearing parameters and does not need to be entered. If the customer has a special characteristic frequency to observe, it can be entered directly, and the default state is 0.

[0015] The intelligent analysis module includes: The judgment threshold setting module of time domain analysis is used to input the vibration value, peak count one, peak count three of the detected bearing according to experience or customer needs ( Figure 5 Peak count 1 and peak count 3 (used to represent two peak counts), peak factor, kurtosis threshold, and time domain analysis to determine whether the vibration peak of the bearing to be diagnosed is within the threshold range. The default parameters are 35dB, 5, 200, 5, and 3 respectively; The frequency domain analysis judgment threshold setting module is used to input parameters for abnormal factors of bearings based on experience or customer needs, including outer ring waviness, inner ring waviness, steel ball waviness, and mixed / missing balls. The default parameters are 8, 15, 15, and 4, respectively. Frequency domain analysis is used to determine whether the frequency of the bearing to be tested matches the characteristic frequency of the defect, and then determine whether the bearing to be tested has abnormal waviness. The judgment threshold setting module of envelope spectrum analysis is used to input parameters for abnormal factors of bearing detection based on experience or customer needs, including outer ring scratches, inner ring scratches, steel ball scratches, and the degree of damage at a specific frequency. The default parameters are 7, 6, 6, 0, and 0, respectively. The module determines whether the bearing abnormality is caused by scratches by judging whether the abnormal factor parameters are met. The bearing model parameter storage module is used to store the input bearing model and parameters. Specifically, after entering the bearing model and parameters for the first time, click Save. The next time you detect the same model of bearing, you do not need to enter the bearing parameters and can directly select the bearing model.

[0016] In addition, this embodiment provides a human-computer interaction interface, such as Figure 10 As shown in the figure, during the detection process, you only need to follow the steps in the figure, enter the file name in sequence, and then click the corresponding button. In this way, you can simply and effectively complete the intelligent diagnosis of bearing vibration faults; The diagnostic results of this embodiment are mainly displayed in the following ways: View the time domain diagnostic results: During the detection process, the data collected and analyzed in the time domain will be presented in the form of a curve image and output in the form of Excel. The time domain diagnosis results are displayed according to the color of the light, green represents qualified and gray represents unqualified.

[0017] View the frequency domain diagnostic results: During the detection process, the frequency domain analysis data is presented in the form of a curve image and output in the form of Excel. The frequency domain diagnosis results are displayed according to the color of the light, green represents qualified and gray represents unqualified.

[0018] View the envelope spectrum diagnosis results: During the testing process, the envelope spectrum analysis data is presented in the form of a curve image and output in the form of Excel. The envelope spectrum analysis diagnosis results are displayed according to the color of the light, green represents qualified and gray represents unqualified.

[0019] In summary, the multi-parameter monitoring bearing vibration fault diagnosis system of this embodiment includes an acceleration sensor, a charge amplifier, an A / D data acquisition card, an audio system, and a host computer. The host computer primarily applies axial force to secure the outer ring of the bearing by means of a claw plate. A motor drives the core shaft, which rotates the inner ring of the bearing. A sensor is installed on the outer ring of the bearing to collect radial vibration signals from the bearing during operation. The host computer further analyzes the vibration signals from the perspectives of time domain, frequency domain, and envelope spectrum, and determines the bearing fault status based on the monitored parameter indicators. These parameters include time domain parameters, waviness parameters in the frequency domain, and scratch degree parameters from envelope spectrum analysis. The time domain parameters include vibration value, peak count one, peak count three, peak factor and kurtosis, which can preliminarily reflect the vibration status of the bearing and conditions such as excessive garbage or lubricant particles; the waviness parameters include inner and outer ring waviness and steel ball waviness, which can reflect problems such as the inner and outer ring profiles of the bearing and abnormal steel ball profiles; the scratch degree parameters include inner and outer ring scratches, steel ball scratches and other specific frequencies, which can reflect the inner and outer ring damage, steel ball damage and special fault conditions of the bearing.

[0020] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A bearing vibration fault diagnosis system with multi-parameter monitoring, characterized in that: include: A parameter setting module, used to set the sampling frequency and sampling length of signal acquisition, the parameters of the bearing to be detected and the characteristic frequency of the defect, and the parameter setting module communicates with the external sensor to receive the bearing data collected by the external sensor; An intelligent analysis module, which is provided with a vibration threshold, a waviness abnormality reference parameter, and a scratch reference parameter. The intelligent analysis module communicates with the parameter setting module, receives the collected bearing data, analyzes the bearing data, and then outputs the analysis result of the bearing to determine whether the bearing has a vibration fault; Among them, the specific way in which the intelligent analysis module analyzes the bearing data is as follows: first, the vibration data in the bearing data is analyzed in the time domain to determine whether the vibration is generated inside the bearing, and then the bearing waviness is determined by frequency domain analysis. After that, the envelope spectrum analysis is used to determine whether the abnormality is caused by scratches. Finally, the above judgment results are combined to finally output whether the bearing has a vibration fault.

2. The bearing vibration fault diagnosis system with multi-parameter monitoring according to claim 1 is characterized in that: The parameter setting module includes: A system parameter setting module, which is used to set the sampling frequency and sampling length of signal acquisition according to the observed frequency range; The bearing parameter setting module is used to input the pitch diameter, steel ball diameter, number of steel balls, contact angle and spindle rotation speed of the bearing to be diagnosed; The special frequency point setting module is used to input the characteristic frequency of defects.

3. The bearing vibration fault diagnosis system with multi-parameter monitoring according to claim 2 is characterized in that: The intelligent analysis module includes: The judgment threshold setting module of time domain analysis is used to input the vibration value, peak count one, peak count three, peak factor, and kurtosis threshold of the detected bearing according to experience or customer needs, and judge whether the vibration peak value of the bearing to be diagnosed is within the threshold range through time domain analysis; The judgment threshold setting module of frequency domain analysis is used to judge the abnormal factor parameters of the bearing according to experience or customer needs, including outer ring waviness, inner ring waviness, steel ball waviness, mixed balls / missing balls, and judge whether the frequency of the bearing to be tested matches the characteristic frequency of the defect through frequency domain analysis, and then judge whether the bearing to be tested has abnormal waviness; The judgment threshold setting module of envelope spectrum analysis is used to input the abnormal factor parameters of the bearing according to experience or customer needs, including outer ring scratches, inner ring scratches, steel ball scratches and the degree of damage at a specific frequency observed, so as to determine whether the bearing is abnormal due to scratches by judging whether it meets the abnormal factor parameters; The bearing model parameter storage module is used to store input bearing models and parameters.

4. The bearing vibration fault diagnosis system with multi-parameter monitoring according to claim 3 is characterized in that: The judgment threshold setting module of the time domain analysis will display the data collected and analyzed in the time domain in the form of a curve image during the analysis and detection process, and output the data in the form of Excel, and the time domain diagnosis result will be displayed according to the color of the light, green represents qualified and gray represents unqualified.

5. The bearing vibration fault diagnosis system with multi-parameter monitoring according to claim 4 is characterized in that: The judgment threshold setting module of the frequency domain analysis presents the frequency domain analysis data in the form of a curve image during the analysis and detection process, and outputs the data in the form of Excel, and the frequency domain diagnosis results are displayed according to the color of the light, green represents qualified and gray represents unqualified.

6. The bearing vibration fault diagnosis system with multi-parameter monitoring according to claim 5 is characterized in that: The judgment threshold setting module of the envelope spectrum analysis presents the data of the envelope spectrum analysis in the form of a curve image during the analysis and detection process, and outputs the data in the form of Excel, and the envelope spectrum analysis diagnosis result is displayed according to the color of the light, green represents qualified and gray represents unqualified.

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