Electric vehicle electric drive axle bearing low-speed abnormal sound test method based on vibration analysis
By using a vibration analysis-based method, vibration signals of the reducer housing of the electric drive axle were collected, and the characteristic period and order were calculated. This solved the problem of detecting low-speed abnormal noise in the bearings of electric drive axles in electric vehicles, and enabled precise positioning and efficient diagnosis.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGXI JIANGLING CHASSIS CO LTD
- Filing Date
- 2025-12-15
- Publication Date
- 2026-05-01
AI Technical Summary
Existing technologies cannot objectively, accurately, and efficiently detect and locate abnormal noise problems in electric drive axle bearings of electric vehicles at low speeds. They suffer from strong subjectivity, inaccurate positioning, dependence on operating conditions, and low efficiency.
By collecting vibration signals of the reducer housing during low-speed uniform operation of the electric drive axle, the peak value of periodic vibration is identified, the characteristic period and frequency are calculated, the characteristic order is calculated in combination with the drive motor speed, and compared with the theoretical fault characteristic order of the bearing to locate the source of abnormal noise.
It enables objective and quantifiable detection of low-speed abnormal noise in electric drive axle bearings, improves the accuracy and repeatability of test results, simplifies the configuration of the testing system, reduces costs and complexity, and improves fault diagnosis efficiency.
Smart Images
Figure CN121954483A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of electric vehicle drive system testing technology, specifically to a method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis. Background Technology
[0002] With the increasing popularity of electric vehicles, NVH (noise, vibration, and harshness) performance has become a key indicator for measuring their quality. As a core component of the powertrain, the electric drive axle is prone to abnormal noises, especially the intermittent "humming" and "clunking" noises produced when driving at low speeds (such as below 20 km / h), which seriously affect driving comfort.
[0003] Currently, the detection of abnormal noises in electric drive axles mainly relies on subjective evaluation by experienced testers in a semi-anechoic chamber or on actual road testing. This method has the following inherent drawbacks:
[0004] 1. High subjectivity: Different testers have different sensitivities and evaluation standards for abnormal noises, resulting in poor repeatability and comparability of results.
[0005] 2. Inability to pinpoint the exact source: The human ear cannot accurately determine which specific bearing in the reducer (such as the input shaft bearing, intermediate shaft bearing, or differential bearing) is causing the abnormal noise.
[0006] 3. Operating conditions depend on the environment: Low-speed abnormal noises are often masked by background noise and are difficult to identify effectively in noisy environments.
[0007] 4. Inefficient: It relies entirely on manual judgment, resulting in long testing cycles and high costs.
[0008] While traditional vibration order analysis can be used for fault diagnosis, it typically relies on speed sensors to accurately obtain the shaft's rotational frequency. At low speeds, the speed signal has a low signal-to-noise ratio, and installing additional speed sensors increases the complexity and cost of the test.
[0009] Therefore, there is an urgent need in this field for a test method that can objectively, accurately, and efficiently detect and locate low-speed abnormal noises in electric drive axle bearings. Summary of the Invention
[0010] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis.
[0011] To achieve the above objectives, the technical solution adopted by the present invention is as follows:
[0012] A method for testing low-speed abnormal noise in electric drive axle bearings of electric vehicles based on vibration analysis includes the following steps:
[0013] The vibration signal of the reducer housing was collected when the electric drive axle was running at a low constant speed.
[0014] Identify the periodic vibration peak corresponding to the abnormal noise from the time-domain waveform of the vibration signal;
[0015] The characteristic period and characteristic frequency of the abnormal noise are calculated based on the time interval between adjacent vibration peaks.
[0016] Obtain the speed of the drive motor and calculate its fundamental frequency. Based on the characteristic frequency and the fundamental frequency, calculate the characteristic order of the abnormal noise.
[0017] The abnormal noise source is located by comparing the characteristic order with the theoretical fault characteristic order of each bearing inside the electric drive axle.
[0018] Furthermore, the low-speed constant-speed operation refers to a constant-speed coasting or driving state with a vehicle speed below 20km / h.
[0019] Furthermore, when collecting vibration signals, the vibration acceleration sensor is installed at a specific location near the bearing housing of the reducer.
[0020] Furthermore, the identification of periodic vibration peaks is achieved by observing the periodic transient impact peaks that appear in the time-domain waveform.
[0021] Furthermore, the time interval Δti between adjacent vibration peaks is calculated, Δti = ti+1 - ti (i = 1, 2, ..., n-1), where ti is the time corresponding to the i-th vibration peak. The average value of multiple consecutive time intervals Δti is then taken to obtain the characteristic period T of the abnormal noise.
[0022] Where n is the number of peaks.
[0023] Furthermore, based on the obtained characteristic period T, the characteristic frequency f of the abnormal noise is calculated, where,
[0024]
[0025] Furthermore, the drive motor speed N corresponding to the current test vehicle speed V is obtained through the vehicle bus, and the rotational fundamental frequency fshaft of the motor shaft is calculated.
[0026] Calculate the order of the abnormal noise characteristic frequency f relative to the motor shaft rotation fundamental frequency fshaft:
[0027] Wherein, the vehicle speed V is in km / h, the motor speed N is in rpm, and the rotational fundamental frequency fshaft is in Hz.
[0028] Furthermore, the theoretical fault characteristic order is calculated in advance based on the bearing's geometric parameters and the speed ratio relationship of the transmission system, including at least one of the outer ring fault characteristic order, inner ring fault characteristic order, rolling element fault characteristic order, and cage fault characteristic order.
[0029] Furthermore, when the error between the characteristic order and a certain theoretical fault characteristic order of a certain bearing is within a preset range, the bearing is determined to be the source of abnormal noise.
[0030] Furthermore, the vibration signal is acquired through an LMS data acquisition system with a sampling frequency of not less than 25.6kHz, and the speed of the drive motor is obtained through the vehicle's CAN bus.
[0031] The beneficial effects of this invention are as follows: 1. By extracting the characteristic time intervals corresponding to abnormal noise events in vibration signals and performing calculations, the traditional subjective auditory evaluation that relies on human ears is transformed into objective and quantifiable physical parameters, which significantly improves the accuracy, repeatability and traceability of the detection results and overcomes the problem of poor consistency in manual evaluation;
[0032] 2. By comparing the calculated characteristic order with the theoretical fault characteristic order of each bearing inside the electric drive axle (such as the input shaft, intermediate shaft, and differential bearing), the specific bearing causing the abnormal noise and even the fault type (such as outer ring, inner ring, rolling element, or cage fault) can be accurately identified, providing a direct and reliable technical basis for subsequent maintenance and quality improvement.
[0033] 3. The characteristic frequency and order can be directly deduced from the periodic vibration peaks generated by the abnormal noise itself, eliminating the need for an additional high-precision speed sensor to acquire low signal-to-noise ratio low-speed speed signals. This simplifies the test system configuration, reduces test costs and complexity, and is particularly suitable for low-speed, low-signal-to-noise ratio operating conditions;
[0034] 4. This method can be efficiently integrated with standard vibration testing equipment such as LMS and data analysis software to achieve rapid data acquisition, automatic analysis and intelligent diagnosis, which greatly shortens the testing cycle, reduces reliance on senior testing personnel, and improves the overall efficiency and reliability of fault diagnosis. Attached Figure Description
[0035] Figure 1 This is a time-domain diagram of the vibration signal collected in one embodiment of the present invention.
[0036] Figure 2 This is a schematic diagram of the structure of the electric drive axle reducer of the present invention. Detailed Implementation
[0037] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0038] A method for testing low-speed abnormal noise in electric drive axle bearings of electric vehicles based on vibration analysis includes the following steps:
[0039] The vibration signal of the reducer housing was collected when the electric drive axle was running at a low constant speed.
[0040] Identify the periodic vibration peak corresponding to the abnormal noise from the time-domain waveform of the vibration signal;
[0041] The characteristic period and characteristic frequency of the abnormal noise are calculated based on the time interval between adjacent vibration peaks.
[0042] Obtain the speed of the drive motor and calculate its fundamental frequency. Based on the characteristic frequency and the fundamental frequency, calculate the characteristic order of the abnormal noise.
[0043] The abnormal noise source is located by comparing the characteristic order with the theoretical fault characteristic order of each bearing inside the electric drive axle.
[0044] Furthermore, the low-speed constant-speed operation refers to a constant-speed coasting or driving state with a vehicle speed below 20km / h.
[0045] Furthermore, when collecting vibration signals, the vibration acceleration sensor is installed at a specific location near the bearing housing of the reducer.
[0046] Furthermore, the identification of periodic vibration peaks is achieved by observing the periodic transient impact peaks that appear in the time-domain waveform.
[0047] Furthermore, the time interval Δti between adjacent vibration peaks is calculated, Δti = ti+1 - ti (i = 1, 2, ..., n-1), where ti is the time corresponding to the i-th vibration peak. The average value of multiple consecutive time intervals Δti is then taken to obtain the characteristic period T of the abnormal noise.
[0048] Where n is the number of peaks.
[0049] Furthermore, based on the obtained characteristic period T, the characteristic frequency f of the abnormal noise is calculated, where,
[0050]
[0051] Furthermore, the drive motor speed N corresponding to the current test vehicle speed V is obtained through the vehicle bus, and the rotational fundamental frequency fshaft of the motor shaft is calculated.
[0052] Calculate the order of the abnormal noise characteristic frequency f relative to the motor shaft rotation fundamental frequency fshaft:
[0053] Wherein, the vehicle speed V is in km / h, the motor speed N is in rpm, and the rotational fundamental frequency fshaft is in Hz.
[0054] Furthermore, the theoretical fault characteristic order is calculated in advance based on the bearing's geometric parameters and the speed ratio relationship of the transmission system, including at least one of the outer ring fault characteristic order, inner ring fault characteristic order, rolling element fault characteristic order, and cage fault characteristic order.
[0055] Furthermore, when the error between the characteristic order and a certain theoretical fault characteristic order of a certain bearing is within a preset range, the bearing is determined to be the source of abnormal noise.
[0056] Furthermore, the vibration signal is acquired through an LMS data acquisition system with a sampling frequency of not less than 25.6kHz, and the speed of the drive motor is obtained through the vehicle's CAN bus.
[0057] like Figure 2 As shown, the "ticking" noise that occurs when the electric drive axle of a certain model of electric vehicle is driven at a constant speed of 20km / h is taken as an example.
[0058] S1: On the vehicle, drive at a constant speed of 20 km / h on a chassis dynamometer. Install an ICP vibration acceleration sensor near the bearing housing of the reducer and connect it to the LMS data acquisition front end. Set the sampling frequency to 25.6 kHz and collect vibration data for a period of time.
[0059] S2: Import the collected data into the LMS Test.Lab software and observe the time-domain waveform. For example... Figure 1 As shown, a series of periodically occurring impact peaks can be clearly seen, which are synchronized in time with the "ticking" sound heard by the driver. The time points t1 to t6 of the six consecutive peaks were recorded.
[0060] S3: Calculate the time intervals Δt1 to Δt6 between adjacent peaks, and obtain the average characteristic period T = 0.016s.
[0061] S4: Calculate the characteristic frequency f = 1 / T = 6.25Hz. Read the current drive motor speed N = 2730rpm from the CAN bus. The fundamental frequency of the motor shaft rotation fshaft = 2730 / 60 ≈ 45.5Hz. Finally, calculate the characteristic frequency Order = 6.25 / 45.5 ≈ 0.137.
[0062] S5: According to the design drawings of the electric drive axle, the theoretical characteristic order of the cage failure of the tapered roller bearing in the intermediate shaft of the reducer is approximately 0.135, calculated using the formula. The measured order of 0.137 closely matches the theoretical value of 0.135. Therefore, the "ticking" noise can be diagnosed as originating from a cage failure in the intermediate shaft bearing.
[0063] This invention extracts and calculates the characteristic time intervals corresponding to abnormal noise events in vibration signals, transforming the traditional subjective auditory evaluation relying on human hearing into objective and quantifiable physical parameters. This significantly improves the accuracy, repeatability, and traceability of the detection results, overcoming the problem of poor consistency in manual evaluation. By comparing the calculated characteristic order with the theoretical fault characteristic order of each bearing inside the electric drive axle (such as the input shaft, intermediate shaft, and differential bearing), the specific bearing and even the fault type (such as outer ring, inner ring, rolling element, or cage fault) that generates abnormal noise can be accurately identified, providing a direct and reliable technical basis for subsequent maintenance and quality improvement. It directly uses the periodic vibration peaks generated by the abnormal noise itself to infer the characteristic frequency and order, eliminating the need for additional high-precision speed sensors to acquire low signal-to-noise ratio low-speed speed signals. This simplifies the test system configuration, reduces test costs and complexity, and is particularly suitable for low-speed, low-signal-to-noise ratio operating conditions.
[0064] This method can be efficiently integrated with standard vibration testing equipment such as LMS and data analysis software to achieve rapid data acquisition, automatic analysis and intelligent diagnosis, which greatly shortens the testing cycle, reduces reliance on experienced testing personnel, and improves the overall efficiency and reliability of fault diagnosis.
[0065] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis, characterized in that, Includes the following steps: The vibration signal of the reducer housing was collected when the electric drive axle was running at a low constant speed. Identify the periodic vibration peak corresponding to the abnormal noise from the time-domain waveform of the vibration signal; The characteristic period and characteristic frequency of the abnormal noise are calculated based on the time interval between adjacent vibration peaks. Obtain the speed of the drive motor and calculate its fundamental frequency. Based on the characteristic frequency and the fundamental frequency, calculate the characteristic order of the abnormal noise. The abnormal noise source is located by comparing the characteristic order with the theoretical fault characteristic order of each bearing inside the electric drive axle.
2. The method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis according to claim 1, characterized in that, The low-speed constant speed operation refers to a constant speed coasting or driving state with a vehicle speed below 20km / h.
3. The method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis according to claim 1, characterized in that, When collecting vibration signals, the vibration acceleration sensor is installed at a specific location near the bearing housing of the reducer.
4. The method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis according to claim 1, characterized in that, Identifying periodic vibration peaks is achieved by observing transient impact peaks that occur periodically in the time-domain waveform.
5. The method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis according to claim 1, characterized in that, Calculate the time interval Δti and Δt between adjacent vibration peaks. i =t i+1 -t i (i = 1, 2, ..., n-1), where ti is the time corresponding to the i-th vibration peak; the characteristic period T of the abnormal noise is obtained by averaging multiple consecutive time intervals Δti: Where n is the number of peaks.
6. The method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis according to claim 5, characterized in that, Based on the obtained characteristic period T, calculate the characteristic frequency f of the abnormal noise, where...
7. The method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis according to claim 6, characterized in that, The drive motor speed N corresponding to the current test vehicle speed V is obtained through the vehicle bus, and the rotational fundamental frequency fshaft of the motor shaft is calculated. Calculate the order of the abnormal noise characteristic frequency f relative to the motor shaft rotation fundamental frequency fshaft: Wherein, the vehicle speed V is in km / h, the motor speed N is in rpm, and the rotational fundamental frequency fshaft is in Hz.
8. The method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis according to claim 1, characterized in that, The theoretical fault characteristic order is calculated in advance based on the bearing's geometric parameters and the speed ratio relationship of the transmission system, and includes at least one of the following: outer ring fault characteristic order, inner ring fault characteristic order, rolling element fault characteristic order, and cage fault characteristic order.
9. The method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis according to claim 1, characterized in that, When the error between the characteristic order and a certain theoretical fault characteristic order of a bearing is within a preset range, the bearing is determined to be a source of abnormal noise.
10. The method for testing low-speed abnormal noise of electric drive axle bearings in electric vehicles based on vibration analysis according to claim 1, characterized in that, The vibration signal is acquired through an LMS data acquisition system with a sampling frequency of not less than 25.6kHz, and the speed of the drive motor is obtained through the vehicle CAN bus.