Variable frequency motor bearing state monitoring system and method

Abstract

The invention relates to a variable frequency motor bearing state monitoring system and method. The system comprises a high-frequency oscillation current sensor and a state monitoring circuit. Wherein the high-frequency oscillation current sensor is mounted on the side of a to-be-tested variable frequency motor in a non-contact manner and is used for extracting a high-frequency switch oscillation current signal of the to-be-tested variable frequency motor; the state monitoring circuit comprises a modal filtering module, a fault feature extraction module and a bearing state evaluation module which are connected in sequence, and the modal filtering module is used for extracting one or more dominant modal currents from the high-frequency switch oscillation current signals; the fault feature extraction module is used for extracting grease state features and mechanical fault state features from the dominant modal current, and the bearing state evaluation module is used for comprehensively evaluating the bearing state of the variable frequency motor to be detected according to the grease state features and the mechanical fault state features. Compared with the prior art, the weak change of the bearing state of the variable frequency motor can be accurately and safely detected.

Description

Technical Field

[0001] The invention belongs to the field of variable frequency motor state monitoring, and in particular relates to a variable frequency motor bearing state monitoring system and method. Background Art

[0002] Variable frequency motors are vital driving devices in modern industrial engineering and are widely used in important fields such as rail transportation, electric vehicles, ship propulsion, and wind power generation. Online monitoring of variable frequency motors is an important measure to improve system safety and reliability and reduce operation and maintenance costs. Bearings are an indispensable core component in variable frequency motor systems and are also one of the components with the highest failure rate. This is because the inner and outer raceways of the motor bearings maintain relative rotation and are continuously subjected to mechanical stress, wear, and other effects, which will shorten the life of the bearings over time. In actual systems, some bearings may also fail due to assembly, manufacturing defects, overload operation, or insufficient lubrication.

[0003] The operating status of the variable frequency motor bearing can be reflected by various signals of the system. The existing bearing status monitoring method usually realizes effective monitoring of the bearing status by comparing the above-mentioned operating information of the bearing with the information of the fault characteristics. According to the different state variable signals, the bearing status monitoring technology can be roughly divided into vibration method, current method and acoustic emission method. Among them, the vibration method monitors the operating status of the bearing by analyzing the vibration signal of the bearing. Generally, the vibration sensor is installed in the axial or radial position on the motor housing to collect the vibration signal. The vibration method can diagnose more serious bearing mechanical faults, but it is difficult to sensitively identify bearing grease deterioration or early mechanical faults. The current method monitors the operating status of the bearing by analyzing the motor current signal. When the health status of the bearing changes, the air gap of the motor also changes, and the corresponding harmonic current will be induced in the stator winding of the motor. By analyzing the stator current, the corresponding harmonic components under the unhealthy state can be extracted to realize non-invasive bearing status monitoring. However, in the actual system, the motor harmonic current is greatly affected by the fundamental wave and operating conditions, and it is easy to misdiagnose. The acoustic emission method reflects the health status of the bearing through sound signals. When the bearing material is subjected to deformation or abnormal external force, it will quickly release energy and produce a transient stress wave. However, in actual applications, the acoustic emission method is easily interfered by environmental noise and has high technical requirements.

[0004] In summary, it is necessary to design a variable frequency motor bearing condition monitoring solution that can sensitively and accurately identify slight changes in the variable frequency motor bearing condition. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a variable frequency motor bearing status monitoring system and method, so as to accurately and sensitively detect slight changes in the variable frequency motor bearing status, and realize early fault warning and status operation and maintenance.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] The present invention provides a variable frequency motor bearing state monitoring system, comprising a high frequency oscillation current sensor and a state monitoring circuit;

[0008] The high-frequency oscillation current sensor is installed contactlessly on the side of the variable frequency motor to be tested, and is used to extract the high-frequency switch oscillation current signal i of the variable frequency motor to be tested. sw ;

[0009] The state monitoring circuit comprises a modal filtering module, a fault feature extraction module and a bearing state evaluation module which are connected in sequence. The modal filtering module is used to extract the fault feature from the high-frequency switch oscillation current signal i sw Extract one or more dominant mode currents i mode (n); The fault feature extraction module is used to extract the dominant mode current i mode (n), the grease state feature K1(n) and the mechanical fault state feature K2(n) are extracted, and the grease state feature K1(n) is related to the dominant modal current i mode (n) is negatively correlated with the average amplitude per unit value, and the mechanical fault state characteristic K2(n) is positively correlated with the number of effective oscillations of the dominant mode of the variable-frequency motor to be tested; the bearing state evaluation module is used to comprehensively evaluate the bearing state of the variable-frequency motor to be tested based on the grease state characteristic K1(n) and the mechanical fault state characteristic K2(n).

[0010] Furthermore, the installation position of the high-frequency oscillation current sensor includes three-phase insertion, phase line insertion or ground line insertion.

[0011] Furthermore, the lower limit bandwidth of the high-frequency oscillation current sensor is greater than the switching frequency of the variable frequency motor to be measured when it is running.

[0012] Furthermore, the modal filtering module obtains the high-frequency switch oscillating current signal i by using an analog bandpass filter, a digital bandpass filter, an FFT fast Fourier transform or a time-frequency analysis algorithm. sw Extract one or more dominant mode currents i mode (n).

[0013] Furthermore, the center frequency of the modal filter module is the high-frequency switch oscillation current signal i sw The frequency of the dominant mode in .

[0014] Furthermore, the calculation method of the grease state characteristic K1(n) is as follows:

[0015] K1(n)=I mode_ave0 / I mode_ave

[0016] Among them, I mode_ave0 is the average amplitude of the dominant modal current of a healthy motor, I mode_ave is the average amplitude of the dominant mode current of the variable frequency motor to be tested.

[0017] Furthermore, the number of dominant mode effective oscillations is the number of dominant mode currents whose amplitude exceeds a set threshold within a data segment.

[0018] Furthermore, the calculation method of the mechanical fault state characteristic K2(n) is as follows:

[0019] K2(n)=N imode / N imode0

[0020] Among them, N imode0 is the number of effective oscillations in the dominant mode of a healthy motor, N imode is the effective oscillation number of the dominant mode of the variable frequency motor to be tested.

[0021] Furthermore, the bearing state evaluation module compares the grease state characteristics K1(n) and mechanical fault state characteristics K2(n) of the variable frequency motor to be tested with the reference values ​​calculated based on the healthy motor, and comprehensively evaluates the bearing state of the variable frequency motor to be tested.

[0022] The present invention also provides a variable frequency motor bearing state monitoring method, comprising the following steps:

[0023] S1. Install the high-frequency oscillation current sensor contactlessly on the side of the variable frequency motor to be tested, and extract the high-frequency switch oscillation current signal i of the variable frequency motor to be tested. sw ;

[0024] S2, through the modal filtering module from the high-frequency switch oscillation current signal i sw Extract one or more dominant mode currents i mode (n);

[0025] S3, through the fault feature extraction module from the dominant mode current i mode Extract the grease state feature K1(n) and the mechanical fault state feature K2(n) from (n);

[0026] S4. The bearing state evaluation module comprehensively evaluates the bearing state of the variable frequency motor to be tested according to the grease state feature K1(n) and the mechanical fault state feature K2(n).

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] The present invention proposes a variable frequency motor bearing condition monitoring system, including a high-frequency oscillation current sensor and a condition monitoring circuit. The high-frequency oscillation current sensor is non-contactly installed on the side of the variable frequency motor to be tested, without changing the hardware structure of the main circuit of the variable frequency motor. The high-frequency switching oscillation current signal of the variable frequency motor to be tested can be extracted without direct electrical connection between the sensor and the main circuit of the variable frequency motor system, thereby improving safety. The condition monitoring circuit includes a modal filtering module, a fault feature extraction module and a bearing condition evaluation module connected in sequence, wherein the modal filtering module is used to extract one or more dominant modal currents from the high-frequency switching oscillation current signal; the fault feature extraction .... The extraction module is used to extract grease status characteristics and mechanical fault status characteristics from the dominant mode current. Specifically, the grease status characteristics are negatively correlated with the per-unit value of the average amplitude of the dominant mode current of the variable-frequency motor to be tested, and the mechanical fault status characteristics are positively correlated with the number of effective oscillations of the dominant mode of the variable-frequency motor to be tested; the bearing status evaluation module is used to comprehensively evaluate the bearing status of the variable-frequency motor to be tested based on the grease status characteristics and the mechanical fault status characteristics; the above-mentioned system can sensitively reflect the slight changes in the motor bearing grease deterioration and the early mechanical fault status by online monitoring and analyzing the status characteristics of the high-frequency switching oscillation current signal, thereby realizing early fault warning and status operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is the structural diagram of the variable frequency motor bearing condition monitoring system.

[0030] Description of reference numerals: 1. high frequency oscillation current sensor; 2. state monitoring circuit; 201. modal filtering module; 202. fault feature extraction module; 203. bearing state evaluation module;

[0031] Figure 2 It is the schematic diagram of the test system;

[0032] Figure 3 This is a schematic diagram of the measured high-frequency current signal of the motor;

[0033] Figure 4 This is a schematic diagram of the extracted common mode current 14MHz modal signal;

[0034] Figure 5 This is a schematic diagram of the monitoring results of the bearing status characteristics;

[0035] Figure 6 The figure is a flow chart of the variable frequency motor bearing condition monitoring method. DETAILED DESCRIPTION

[0036] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0037] Example 1

[0038] This embodiment provides a variable frequency motor bearing status monitoring system. Figure 1 As shown, it includes a high-frequency oscillation current sensor 1 and a state monitoring circuit 2.

[0039] The high-frequency oscillating current sensor 1 is installed contactlessly on the side of the variable frequency motor to be tested. Its installation position can be installed in three ways: three-phase pull-through, phase line pull-through or ground line pull-through. The lower limit bandwidth of the high-frequency oscillating current sensor 1 is greater than the switching frequency of the variable frequency motor to be tested when it is running, so as to filter out the interference of the fundamental and harmonic currents of the variable frequency motor to be tested when it is running; the upper limit bandwidth of the high-frequency oscillating current sensor 1 should be greater than 1MHz, which is used to extract the high-frequency switching oscillating current signal i of the variable frequency motor to be tested. sw .

[0040] The state monitoring circuit 2 includes a modal filtering module 201, a fault feature extraction module 202 and a bearing state evaluation module 203 connected in sequence. The modal filtering module 201 extracts the fault feature from the high-frequency switch oscillation current signal i by using an analog bandpass filter, a digital bandpass filter, a FFT fast Fourier transform or a time-frequency analysis algorithm. sw Extract one or more dominant mode currents i mode (n), n = 1, ..., N. The center frequency of the modal filter module 201 is the high-frequency switching oscillation current signal i sw The frequency of the dominant mode in mode (n), n=1,…,N.

[0041] The fault feature extraction module 202 is used to extract the fault feature from the dominant mode current i mode (n) to extract the grease state feature K1(n) that can reflect the deterioration of the bearing grease and the mechanical failure state feature K2(n) that can reflect the mechanical failure of the bearing. Since the bearing oil film capacitance can directly reflect the bearing operation state, when the bearing grease deteriorates or mechanical failure occurs, the bearing oil film will undergo characteristic changes, resulting in corresponding changes in the amplitude of the dominant mode current or the number of effective oscillations. Therefore, the present invention extracts the dominant mode current i mode The two characteristic quantities K1(n) of grease status and K2(n) of mechanical fault status are extracted from the grease status feature K1(n) and the mechanical fault status feature K2(n), thereby realizing sensitive and accurate fault diagnosis.

[0042] Grease state characteristic K1(n) and the dominant modal current i of the variable frequency motor to be tested mode(n) The average amplitude and per unit value are negatively correlated. The specific calculation method is as follows:

[0043] K1(n)=I mode_ave0 / I mode_ave

[0044] Among them, I mode_ave0 is the average amplitude of the dominant modal current of a healthy motor, I mode_ave is the average amplitude of the dominant mode current of the variable frequency motor to be tested.

[0045] The mechanical fault state feature K2(n) is positively correlated with the number of effective oscillations of the dominant mode of the variable frequency motor to be tested. The number of effective oscillations of the dominant mode is the number of dominant mode currents whose amplitude exceeds a set threshold value (in this embodiment, the set threshold value is 30% of the maximum amplitude of the dominant mode current) within a data segment. The calculation method of the mechanical fault state feature K2(n) is as follows:

[0046] K2(n)=N imode / N imode0

[0047] Among them, N imode0 is the number of effective oscillations in the dominant mode of a healthy motor, N imode is the effective oscillation number of the dominant mode of the variable frequency motor to be tested.

[0048] The bearing state evaluation module 203 is used to compare the grease state characteristics K1(n) and mechanical fault state characteristics K2(n) of the variable frequency motor to be tested with the reference values ​​calculated based on the healthy motor, and comprehensively evaluate the bearing state of the variable frequency motor to be tested.

[0049] The above system uses a non-contact current sensor to extract and analyze the PWM switch high-frequency oscillation current signal from the current of the variable frequency motor system to be tested, so as to realize online monitoring of the variable frequency motor bearing status. On the one hand, the current sensor dedicated to status monitoring has no direct electrical connection with the main circuit of the variable frequency motor system, which will not affect the normal operation of the system and avoid potential safety hazards; on the other hand, by online monitoring and analyzing the characteristics of the high-frequency switch oscillation status, it can sensitively reflect the slight changes in the deterioration of the motor bearing grease and the early mechanical failure status, and realize early fault warning and status operation and maintenance.

[0050] In order to verify the effectiveness of the above system, this embodiment combines the field test results of the high-power variable frequency motor system to verify the effectiveness of the present invention. Figure 3 As shown, in the test system, one inverter drives four variable frequency motors simultaneously, where the variable frequency motors are three-phase induction motors (rated voltage 1140V, rated current 190A, rated power 200kW), and the inverter capacity is 1MW.

[0051] The three-phase cables of the variable frequency motors No. 1 to No. 4 are respectively threaded with non-contact common mode current sensors and ground current sensors. The output signal of each sensor is connected to the oscilloscope. The eight sensors are divided into two groups and connected to two digital oscilloscopes. The common mode current and ground current of the four motors are measured online. The data is continuously collected using a digital oscilloscope, recording 6 data segments per minute, and the time window length of each data segment is 100ms. The test process takes a total of 40 minutes. The measured motor high-frequency common mode current signal is as follows: Figure 3 shown.

[0052] The 14MHz mode of the common mode current is selected as the characteristic mode to quantitatively evaluate the bearing health status of the four motors. A second-order digital bandpass filter is used in Matlab / Simulink to extract the 14MHz mode component from the common mode current. The results are shown in Figure 2. Figure 4 As shown. Taking the healthy motor No. 1 as the benchmark, the grease state characteristics K1 and mechanical fault state characteristics K2 of each motor bearing are calculated respectively. The test results are shown in Figure 5 As shown, it can be seen that the grease of motors 2 to 4 has degraded to a certain extent (the grease of motor 4 is the worst), and motors 3 and 4 have mechanical failures to a certain extent. The online monitoring results are consistent with the actual situation, verifying the effectiveness of the present invention.

[0053] Example 2

[0054] This embodiment provides a variable frequency motor bearing state monitoring method, which is applicable to the system described in Example 1. Figure 6 As shown, the following steps are included:

[0055] S1, non-contact installation of the high-frequency oscillation current sensor 1 on the side of the variable frequency motor to be tested, extracting the high-frequency switch oscillation current signal i of the variable frequency motor to be tested sw .

[0056] S2, through the modal filter module 201 from the high-frequency switch oscillation current signal i sw Extract one or more dominant mode currents i mode (n).

[0057] S3, extracting the dominant mode current i from the fault feature extraction module 202 mode The grease state feature K1(n) and the mechanical failure state feature K2(n) are extracted from (n).

[0058] S4. The bearing state evaluation module 203 comprehensively evaluates the bearing state of the variable frequency motor to be tested according to the grease state feature K1(n) and the mechanical fault state feature K2(n).

[0059] If the above method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0060] The above description of the embodiments is to facilitate the understanding and use of the invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the present invention should be within the scope of protection of the present invention.

Claims

1. A variable frequency motor bearing condition monitoring system, characterized in that: It comprises a high-frequency oscillating current sensor (1) and a state monitoring circuit (2); The high-frequency oscillating current sensor (1) is non-contactly mounted on the side of the variable frequency motor to be tested, and is used to extract the high-frequency switching oscillating current signal i of the variable frequency motor to be tested. sw ; The state monitoring circuit (2) comprises a modal filtering module (201), a fault feature extraction module (202) and a bearing state evaluation module (203) connected in sequence, wherein the modal filtering module (201) is used to extract the fault feature from the high-frequency switch oscillation current signal i sw Extract one or more dominant mode currents i mode (n); The fault feature extraction module (202) is used to extract the fault feature from the dominant mode current i mode (n), the grease state feature K1(n) and the mechanical fault state feature K2(n) are extracted, and the grease state feature K1(n) is related to the dominant modal current i mode (n) the average amplitude per unit value is negatively correlated, and the mechanical fault state feature K2(n) is positively correlated with the number of effective oscillations of the dominant mode of the variable frequency motor to be tested; the bearing state evaluation module (203) is used to comprehensively evaluate the bearing state of the variable frequency motor to be tested based on the grease state feature K1(n) and the mechanical fault state feature K2(n).

2. A variable frequency motor bearing condition monitoring system according to claim 1, characterized in that: The installation position of the high-frequency oscillation current sensor (1) includes three-phase pull-through, phase line pull-through or ground line pull-through.

3. A variable frequency motor bearing condition monitoring system according to claim 1, characterized in that: The lower limit bandwidth of the high-frequency oscillating current sensor (1) is greater than the switching frequency of the variable frequency motor to be measured when it is running.

4. A variable frequency motor bearing condition monitoring system according to claim 1, characterized in that: The modal filtering module (201) uses an analog bandpass filter, a digital bandpass filter, an FFT fast Fourier transform or a time-frequency analysis algorithm to filter the high-frequency switch oscillation current signal i sw Extract one or more dominant mode currents i mode (n).

5. The variable frequency motor bearing condition monitoring system according to claim 1, characterized in that: The center frequency of the modal filtering module (201) is the high-frequency switch oscillation current signal i sw The frequency of the dominant mode in .

6. A variable frequency motor bearing condition monitoring system according to claim 1, characterized in that: The calculation method of the grease state characteristic K1(n) is as follows: K1(n)=I mode_ave0 / AND mode_ave Among them, I mode_ave0 is the average amplitude of the dominant modal current of a healthy motor, I mode_ave is the average amplitude of the dominant mode current of the variable frequency motor to be tested.

7. A variable frequency motor bearing condition monitoring system according to claim 1, characterized in that: The number of effective oscillations of the dominant mode is the number of dominant mode currents whose amplitude exceeds a set threshold within a data segment.

8. The variable frequency motor bearing condition monitoring system according to claim 1, characterized in that: The calculation method of the mechanical fault state characteristic K2(n) is as follows: K2(n)=N imode / N imode0 Among them, N imode0 is the number of effective oscillations in the dominant mode of a healthy motor, N imode is the effective oscillation number of the dominant mode of the variable frequency motor to be tested.

9. A variable frequency motor bearing condition monitoring system according to claim 1, characterized in that: The bearing state evaluation module (203) compares the grease state characteristics K1(n) and the mechanical fault state characteristics K2(n) of the variable frequency motor to be tested with the reference values ​​calculated based on the healthy motor, and comprehensively evaluates the bearing state of the variable frequency motor to be tested.

10. A method for monitoring the bearing status of a variable frequency motor, characterized in that: The following steps are involved: S1. Install the high-frequency oscillation current sensor (1) contactlessly on the side of the variable frequency motor to be tested, and extract the high-frequency switching oscillation current signal i of the variable frequency motor to be tested. sw ; S2, filtering the high-frequency switch oscillation current signal i from the modal filter module (201) sw Extract one or more dominant mode currents i mode (n); S3, extracting the dominant mode current i from the fault feature extraction module (202) mode Extract the grease state feature K1(n) and the mechanical fault state feature K2(n) from (n); S4. The bearing state evaluation module (203) comprehensively evaluates the bearing state of the variable frequency motor to be tested according to the grease state feature K1(n) and the mechanical fault state feature K2(n).

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