Automobile vibration fault detection and analysis system and method

Abstract

The invention discloses an automobile vibration fault detection and analysis system and method, and belongs to the technical field of automobile vibration fault detection. The method comprises the steps that a multi-source data acquisition module carries out real-time dynamic data acquisition around a selected data acquisition range, and obtained internal operation core data and external operation core data are summarized and marked as a regional fault data set; the internal risk analysis module performs linkage analysis based on internal operation core data, historical operation data and standard working condition data; the multi-source data acquisition module focuses on core data to construct a data set, internal risk time sequence weighted analysis and external defect multi-stage linkage judgment are combined, the pertinence and accuracy of vibration fault detection are greatly improved, the fault development situation can be pre-judged by means of causal link analysis and abnormal trend curve fitting of the comprehensive feedback module, and the fault detection accuracy is improved. And in cooperation with a graded emergency plan, response is gradually upgraded from early warning prompt to emergency shutdown, and the fault deterioration probability is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of automotive vibration fault detection technology, specifically to an automotive vibration fault detection and analysis system and method. Background Technology

[0002] As an important means of assessing vehicle health, vehicle vibration fault detection is based on identifying potential faults by analyzing the vibration characteristics of the vehicle during operation. With the continuous development of the automotive industry, vibration problems have become a key indicator for measuring vehicle performance and safety. They not only directly affect driving comfort, but may also pose a significant threat to the lifespan of parts and the safety of the entire vehicle.

[0003] Based on the above, it should be noted that: existing automotive vibration fault detection technologies lack the ability to accurately locate non-compliant areas; the broad data collection range leads to excessive redundant and invalid data; internal and external fault analysis logics are homogenized, and a differentiated linkage judgment mechanism has not been formed; it is impossible to combine historical data and real-time status to predict the development trend of faults; and emergency plans are simplistic, making it difficult to respond flexibly according to the severity of faults, which easily leads to misjudgments or omissions, and cannot effectively ensure driving safety.

[0004] To address the aforementioned technical shortcomings, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide an automotive vibration fault detection and analysis system and method to solve the problems mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an automotive vibration fault detection and analysis system, comprising a fault detection center, wherein the fault detection center is communicatively connected to a multi-source data acquisition module, an internal risk analysis module, an external defect multi-level analysis module, and a comprehensive feedback module, the specific process of which is as follows;

[0007] The multi-source data acquisition module performs real-time dynamic data acquisition around the selected data acquisition range, and summarizes and marks the obtained internal and external core operating data into a regional fault dataset.

[0008] The internal risk analysis module performs a linkage analysis based on core internal operating data, historical operating data, and standard operating condition data to generate internal operating status judgment signals.

[0009] The external defect multi-level analysis module acquires core external operating data and performs hierarchical analysis and joint comparison based on the collection area of ​​the corresponding component to generate an external comprehensive status performance signal.

[0010] After acquiring internal operating status judgment signals and external comprehensive status performance signals, the integrated feedback module constructs an abnormal indicator trend curve to obtain the fault root cause feedback signal.

[0011] Furthermore, the multi-source data acquisition module processes the regional fault dataset as follows:

[0012] The vibration frequency and rotational speed fluctuation values, which reflect the core indicators of the internal mechanical operation status of the component, are recorded and marked as internal operation core data. The intuitive status of the corresponding component and mating parts, including the surface damage area and the actual clearance with the mating parts, are recorded and marked as external operation core data. Based on a large amount of historical measured data, data without analytical value are statistically filtered out, and the remaining valid data are summarized and marked as regional fault dataset.

[0013] Furthermore, the process by which the intrinsic risk analysis module judges and analyzes internal operating status signals is as follows:

[0014] The intrinsic risk analysis module extracts core internal operating data from the regional fault dataset, obtains historical values ​​of vibration frequency and speed fluctuation from the historical operating data of the corresponding component over the past three months, as well as the standard operating condition data of the corresponding component, sorts the historical operating data in chronological order, and obtains the time-weighted comprehensive value according to the formula.

[0015] Furthermore, the time-weighted composite value is compared with the standard value corresponding to the standard operating condition data, and the difference rate is obtained through a formula. The preset internal state qualification threshold is retrieved and the difference rate is analyzed together. When the difference rate exceeds the internal state qualification threshold by 5%-10%, it is judged as a slight anomaly; when the difference rate exceeds the internal state qualification threshold by 10%-20%, it is judged as a moderate anomaly; when the difference rate exceeds the internal state qualification threshold by 20% or more, it is judged as a severe anomaly; if the difference rate does not exceed the internal state qualification threshold, a normal signal is generated.

[0016] Furthermore, the analysis process of the external comprehensive state performance signal by the external defect multi-level analysis module is as follows:

[0017] The external defect multi-level analysis module extracts the core external operation data from the regional fault dataset, extracts the surface damage area value and the actual clearance value between the surface damage area and the mating parts, compares the surface damage area value with the standard allowable damage area to obtain the ratio, and marks it as the damage deviation; retrieves the standard clearance range for the part, takes the middle value of the standard clearance range as the reference value, subtracts the reference value from the actual clearance value, and divides it by the reference value, marking the resulting value as the clearance deviation.

[0018] Furthermore, the preset deviation levels are retrieved, including deviation levels categorized by magnitude as follows: deviation ≤ 20% is marked as mild, deviation 20% < deviation ≤ 50% is marked as moderate, and deviation > 50% is marked as severe. The corresponding levels of damage deviation and clearance deviation are obtained. The higher of the two levels is taken as the result of the component's external condition analysis. For example, if the damage deviation is mild and the clearance deviation is moderate, the result of the component's external condition analysis is moderate deviation. If both are mild or below, the highest actual level is used.

[0019] Furthermore, two sets of matching data are retrieved separately: the corresponding surface damage area value and the actual clearance value with the mating parts. These include the design parameters of the mating parts and the historical wear data of the mating parts. Based on the difference between the actual clearance and the standard clearance in the external operating core data, the actual wear amount of the mating parts is obtained. The actual wear amount of the mating parts is compared with the wear resistance limit in the design parameters. Combined with the changing trend of the historical wear data, the wear rate is obtained according to the formula.

[0020] Furthermore, the preset wear rate threshold, the design wear resistance limit threshold, the actual wear amount, and the wear rate are retrieved for joint analysis: if the actual wear amount exceeds the design wear resistance limit threshold, it is directly determined that there is abnormal wear on the mating parts; if the actual wear amount does not exceed the wear resistance limit threshold, but the wear rate is greater than the wear rate threshold, it is determined that there is abnormal wear; if the actual wear amount does not exceed the wear resistance limit threshold, and the wear rate is less than the wear rate threshold, it is determined that there is no abnormal wear on the mating parts.

[0021] The specific process for determining the overall external condition and generating the overall external condition performance signal of automotive components is as follows: If the external condition of a component is at a moderate or higher level of deviation, and there is abnormal wear on the external parts, and the deviation levels of the two are consistent, then the overall external condition is determined to be abnormal. If there is only a single data deviation, or the deviation levels are inconsistent, further verification is required. Finally, combined with the external condition qualification threshold, the overall external condition performance signal of the automotive component is generated, including normal / slightly abnormal / moderately abnormal / severely abnormal.

[0022] Furthermore, the analysis process of the integrated feedback module in acquiring internal operating status judgment signals and external comprehensive status performance signals is as follows: an abnormal indicator trend curve is constructed with time as the horizontal axis and the difference rate and deviation value of internal and external abnormal indicators as the vertical axis. The curve is fitted and the slope is calculated. A slope > 0 indicates that the abnormality is aggravated, and a slope ≤ 0 indicates that the abnormality is stable. Combined with the initial vibration abnormality degree of the non-conforming item, a fault root cause feedback signal is generated.

[0023] A method for detecting and analyzing automotive vibration faults includes the following steps:

[0024] S1: The fault detection center generates a detection command, retrieves the overall vibration qualification threshold of the vehicle, completes the macroscopic evaluation by comparing real-time vibration data with the threshold, locates the component area corresponding to the non-conforming item, and selects the data collection range.

[0025] S2: The multi-source data acquisition module collects internal and external core operating data around the selected area, and after filtering out invalid data, it summarizes and marks it as a regional fault dataset.

[0026] S3: The internal risk analysis module generates internal status judgment signals by linking historical and standard data, and the external defect multi-level analysis module generates external comprehensive status performance signals by determining the deviation level and wear.

[0027] S4: The integrated feedback module combines two types of signals through causal link and trend curve analysis to generate a fault root cause feedback signal. The fault detection center triggers the corresponding emergency plan according to the abnormality level.

[0028] The beneficial effects of this invention are:

[0029] 1. This invention uses a fault detection center to accurately select areas of non-conforming components, a multi-source data acquisition module to focus on core data to build a dataset, and combines internal risk time-series weighted analysis with external defect multi-level linkage judgment to avoid data redundancy and logical repetition, thereby significantly improving the pertinence and accuracy of vibration fault detection.

[0030] 2. This invention uses causal link analysis and abnormal trend curve fitting of the integrated feedback module to predict the development trend of faults. With the help of a graded emergency plan, the response is upgraded step by step from early warning to emergency shutdown, which effectively reduces the probability of fault deterioration and comprehensively protects vehicle driving safety and component life. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention.

[0032] Figure 1 This is a system flowchart of the present invention;

[0033] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0035] Example 1: Please refer to Figure 1 - Figure 2 As shown, this embodiment is an automotive vibration fault detection and analysis system and method, including a fault detection center. The fault detection center is communication-linked to a multi-source data acquisition module, an internal risk analysis module, an external defect multi-level analysis module, and a comprehensive feedback module. The specific process is as follows:

[0036] When the fault detection center coordinates with the automotive vibration fault detection equipment for joint use, if any non-compliance is found during the vibration detection of the target vehicle, the fault detection center generates a detection instruction based on the non-compliance, retrieves the overall vibration compliance threshold for the corresponding vehicle model, and compares the overall vibration data collected during vehicle testing with the vibration compliance threshold in real time to complete a macroscopic evaluation.

[0037] If the overall vibration data exceeds the vibration qualification threshold, it is determined that there is a non-compliance item. Based on the frequency and propagation path of the vibration anomaly, the corresponding automotive component is located, and the area where the component is located is selected. This selected area is used as the data collection range for subsequent data acquisition to ensure the targeting of data collection. Targeted detection instructions are generated and sent to the multi-source data acquisition module.

[0038] After receiving the detection command, the multi-source data acquisition module performs real-time dynamic data acquisition around the selected data acquisition range. It records the vibration frequency value and speed fluctuation value, which reflect the core indicators of the internal mechanical operation status of the component, and marks them as the core internal operation data. It also records the intuitive status of the corresponding component and mating parts, including the surface damage area value and the actual clearance value between the component and the mating parts, and marks them as the core external operation data.

[0039] After data collection, a preliminary screening is performed. Based on a large amount of historical measured data, components whose core parameters fluctuate within twice the limit range under normal operating conditions are marked as numerical limit ranges. Those exceeding the limit range are likely caused by data acquisition equipment malfunctions and have no analytical value. These limit ranges are then compared and analyzed with the collected data.

[0040] If any part of the internal or external core operating data exceeds the numerical limit, it will be directly removed. The remaining valid data will be aggregated and marked as a regional fault dataset to provide a basis for subsequent analysis.

[0041] The internal risk analysis module extracts the core internal operation data from the regional fault dataset, including vibration frequency values and rotational speed fluctuation values, obtains the corresponding parts of the historical operation data of the corresponding components in the past 3 months, extracts the historical vibration frequency values, historical rotational speed fluctuation values, and the standard operating condition data of the corresponding components at a frequency of once a week. The standard operating condition data specifically includes the normal operating parameter range calibrated by the manufacturer;

[0042] Sort the historical operation data in chronological order. According to the formula: Time - series weighted comprehensive value = (the current acquisition value × a) + (the historical average value in the past 1 week × b) + (the historical average value in the 1 - 2 weeks × c) + (the historical average value in the 2 - 3 weeks × d) / (a + b + c + d), where a, b, c, and d represent the weights assigned to the recent data according to time. a represents the weight of the data acquired this time, and can take the value of 0.7 because real - time data reflects the current state most directly and its weight is higher than the sum of historical data. b represents the weight of the data in the past 1 week and can take the value of 0.6. c represents the weight of the data in the 1 - 2 weeks and can take the value of 0.3. d represents the weight of the data in the 2 - 3 weeks and can take the value of 0.1. After adding it to the core internal operation data acquired this time and taking the average value, the time - series weighted comprehensive value is obtained. The time - series weighted comprehensive value is used to weaken the interference of long - term data and highlight the changes in the recent operating state;

[0043] Compare the time - series weighted comprehensive value with the standard value corresponding to the standard operating condition data, and analyze the difference rate between the two through the formula: Difference rate = (Time - series weighted comprehensive value - Standard value) / Standard value × 100%); Retrieve the preset internal state qualification threshold for joint analysis with the difference rate. It is indicated that the difference rate ≤ ±5% is qualified:

[0044] If the difference rate exceeds the internal state qualification threshold, it is determined that there is a risk in the internal operation, and an internal operation state judgment signal is generated; After generating the internal operation judgment signal, a secondary judgment is made based on the numerical range by which the difference rate exceeds the internal state qualification threshold;

[0045] When the difference rate exceeds 5% - 10% of the internal state qualification threshold, it is judged as a mild abnormality;

[0046] When the difference rate exceeds 10% - 20% of the internal state qualification threshold, it is judged as a moderate abnormality

[0047] When the difference rate exceeds 20% or more of the internal state qualification threshold, it is judged as a severe abnormality;

[0048] If the difference rate does not exceed the internal state qualification threshold, a normal signal is generated.

[0049] Embodiment 2:

[0050] The external defect multi-level analysis module extracts core external operation data from the regional fault dataset, including the surface damage area value and the actual clearance value between the component and the mating parts. The surface damage area value directly reflects the degree of external damage to the component body, while the actual clearance value reflects the assembly fit between the component and the mating parts.

[0051] The surface damage area value is compared with the standard allowable damage area to obtain the ratio, which is marked as the damage deviation. The standard allowable damage area comparison is expressed as the manufacturer's calibration, such as the maximum allowable damage area of ​​the drive shaft surface is 5mm².

[0052] Find the standard clearance range for this part, take the middle value of the standard clearance range as the reference value, subtract the reference value from the actual clearance value, and then divide by the reference value. Mark the resulting value as the clearance deviation. A positive value indicates that the clearance is too large, and a negative value indicates that the clearance is too small. The standard clearance is expressed as the standard clearance between the engine mount and the engine in the corresponding external connecting parts of the component, which is 0.2-0.5mm.

[0053] Retrieve preset deviation levels, which are categorized by deviation magnitude as follows: ≤20% is marked as mild, 20% < ≤50% is marked as moderate, and >50% is marked as severe; obtain the corresponding damage deviation and gap deviation levels.

[0054] The higher of the two grades is taken as the result of the component's external condition analysis. For example, if the damage deviation is mild and the clearance deviation is moderate, then the result of the component's external condition analysis is moderate deviation.

[0055] If both conditions are mild or below, the highest actual level shall be used.

[0056] Two sets of matching data are retrieved separately: the corresponding surface damage area value and the actual clearance value with the mating parts. These include the design parameters of the mating parts and the historical wear data of the mating parts. The design parameters of the mating parts include standard dimensions and material strength, which are obtained from the manufacturer's technical drawings. The historical wear data of the mating parts includes the wear amount corresponding to the cumulative mileage, which is stored in the vehicle maintenance database.

[0057] The difference between the actual clearance and the standard clearance in the external operating core data is the actual wear amount of the mating parts. If the difference is positive, it means that the wear of the parts has led to an increase in the clearance. If the difference is negative and the absolute value does not exceed the allowable installation error, it is considered that there is no effective wear amount and is calculated as 0.

[0058] This is used to obtain the actual wear of mating parts. The actual wear of mating parts is compared with the wear resistance limit in the design parameters. The standard allowable damage area and standard clearance are calculated based on the material properties and structural strength of the components. Combined with the changing trend of historical wear data, the wear growth rate of the past 10,000 kilometers is calculated. Wear growth rate = (current wear amount - previous wear amount) / (current mileage - previous mileage) × 10000. This is used to determine whether there is abnormal wear of mating parts and obtain the analysis results of external parts.

[0059] After retrieving the preset wear rate increase threshold, the design wear resistance limit threshold, the actual wear amount, and the wear rate increase, a joint analysis is performed:

[0060] If the actual wear exceeds the design wear resistance limit threshold, it is directly determined that there is abnormal wear on the mating parts.

[0061] If the actual wear amount does not exceed the wear resistance limit threshold, but the wear rate is greater than the wear rate threshold, the wear rate threshold is expressed as 0.1 mm / 10,000 km. Based on the actual test statistics of 500 vehicles of the same type, the normal wear rate is ≤0.1 mm / 10,000 km. Therefore, it is determined that there is abnormal wear, the wear rate is too fast, and it may cause failure in the short term.

[0062] If the actual wear amount does not exceed the wear resistance limit threshold and the wear rate is less than the wear rate threshold, then the mating parts are determined to have no abnormal wear.

[0063] The specific process of determining the overall external condition and generating signals reflecting the overall external condition of automotive components:

[0064] S1. Pair and verify the results of the external state analysis of the component with the results of the external part analysis:

[0065] As the first case: the external condition of the component is moderate or above, and the mating parts have abnormal wear. At the same time, the deviation level of the two is consistent with the wear impact level. For example, if the component has a moderate deviation, the clearance increase caused by the abnormal wear of the parts also corresponds to a moderate deviation; if the component has a severe deviation, the abnormal wear of the parts corresponds to a severe deviation. In this case, it is directly preliminarily determined that the overall external condition is abnormal.

[0066] As for the second case: only the external condition of the component deviates, including light / moderate / severe, but the part has no abnormal wear, or only the part has abnormal wear but the external condition of the component has a light or less deviation, or both are abnormal but the levels are inconsistent. For example, if the component has a moderate deviation and the abnormal wear of the part only corresponds to a light impact, then further verification is required.

[0067] S2. Further verification: Retrieve the usage environment data of the component and the lubrication status data of the mating parts. The usage environment data includes whether the vehicle has been driven on rough roads for extended periods and whether there is a history of collisions or repairs. The lubrication status data of the mating parts includes the oil change interval and the current remaining grease level.

[0068] If only the external part is deviated, check whether there is external force damage. If so, the deviation is an isolated damage and is not judged as a comprehensive anomaly. If there is no external force factor, the deviation value needs to be recalculated. If the deviation still exists, it is recorded as a single deviation level.

[0069] If only the parts are abnormally worn, check the lubrication status. If the lubrication is insufficient, it is a maintenance problem and the part is considered abnormal. If the lubrication is normal, check whether the material of the part is qualified and it is still considered an abnormal part.

[0070] If the levels are inconsistent, check whether the deviation calculation is accurate and whether the standard value matches the current component model. If the inconsistency persists after recalibration, use the level with the more severe impact as the reference.

[0071] S3. Judgment based on external condition acceptance threshold: The external condition acceptance threshold clearly defines severe or moderate deviation plus abnormal wear of parts as unacceptable, and the final judgment is made accordingly:

[0072] If the external condition of a component is severely deviated, regardless of whether the part is abnormally worn, or if the external condition is moderately deviated and the part is abnormally worn, regardless of whether the levels are completely consistent, and after verification it is confirmed that the wear is related to the deviation, then the overall external condition is deemed unqualified.

[0073] If the external condition of a component is moderately deviated but the parts are not abnormally worn, or slightly deviated regardless of whether the parts are worn, or if the parts are only abnormally worn but the components are not significantly deviated, then the overall external condition is deemed to be qualified.

[0074] S4. Generate external comprehensive status performance signals: Based on the final judgment result, generate four levels of signals accordingly:

[0075] Acceptable: The external condition of the component has a slight or lesser deviation, and the parts have no abnormal wear, or only a slight abnormality that does not affect use, and a normal signal is generated; corresponding to no or slight impact, no immediate action is required;

[0076] Minor nonconformities: The external condition of the component shows slight deviations, and the part exhibits abnormal wear, but this does not reach the combination of moderate deviation and wear, thus generating a minor abnormality signal; subsequent changes need to be monitored, and regular re-inspections should be conducted.

[0077] Moderate non-conformance: The external condition of the component is moderately deviated, and the part has abnormal wear, which meets the non-conformance conditions in the pass / fail threshold, generating a moderate abnormality signal; repair needs to be arranged in the near future;

[0078] Severe non-conformity: The external condition of the component is severely deviated. Regardless of whether the part is worn, a severe abnormal signal is generated, and the machine must be stopped and repaired immediately to avoid aggravating the vibration failure.

[0079] After acquiring internal operating status judgment signals and external comprehensive status performance signals, the integrated feedback module performs causal link analysis:

[0080] If the internal abnormality is minor and the external abnormality is normal, the root cause of the fault should be determined first as internal parameter fluctuations, such as momentary unstable engine speed.

[0081] If the external condition is moderate or worse while the internal condition is normal, the root cause is external defects and problems with mating parts, such as vibration caused by aging of the rubber on the machine feet.

[0082] If both internal and external parts are abnormal and the abnormality levels are the same, it is determined to be a coordinated failure, such as internal bearing wear causing vibration, which in turn aggravates the wear of external mating parts.

[0083] Using time as the horizontal axis and the difference rate and deviation value of internal and external abnormal indicators as the vertical axis, an abnormal indicator trend curve is constructed. The curve is fitted and the slope is calculated. A slope > 0 indicates an aggravated abnormality, and a slope ≤ 0 indicates a stable abnormality. Linear fitting is used, with the horizontal axis representing time in days and the vertical axis representing the abnormal indicators as standardized values. For example, a difference rate of 5% corresponds to a value of 5, and a deviation of 20% corresponds to a value of 20. The fitting formula is y = kx + b, where k is the slope. k > 0.5 is considered an aggravated abnormality. Based on statistics from nearly 100 fault cases, when k > 0.5, the probability of the fault worsening within one month reaches 80%.

[0084] Based on the initial vibration anomaly level of the non-conforming item, a fault root cause feedback signal is generated, including the root cause type, anomaly level, and development trend. After receiving the feedback signal, the fault detection center triggers the corresponding emergency plan according to the anomaly level: for minor anomalies, only an on-board terminal warning is sent; for moderate anomalies, a maintenance reminder is sent, and the maximum vehicle speed is limited; for severe anomalies, the engine power is forcibly reduced, an emergency stop prompt is issued, and information on nearby repair shops is pushed.

[0085] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0086] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0087] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A vehicle vibration fault detection and analysis system, characterized in that, This includes a fault detection center, which is connected to a multi-source data acquisition module, an internal risk analysis module, an external defect multi-level analysis module, and a comprehensive feedback module. The specific process is as follows: The multi-source data acquisition module performs real-time dynamic data acquisition around the selected data acquisition range, and summarizes and marks the obtained internal and external core operating data into a regional fault dataset. The internal risk analysis module performs a linkage analysis based on core internal operating data, historical operating data, and standard operating condition data to generate internal operating status judgment signals. The external defect multi-level analysis module acquires core external operating data and performs hierarchical analysis and joint comparison based on the collection area of ​​the corresponding component to generate an external comprehensive status performance signal. After acquiring internal operating status judgment signals and external comprehensive status performance signals, the integrated feedback module constructs an abnormal indicator trend curve to obtain the fault root cause feedback signal.

2. The automotive vibration fault detection and analysis system according to claim 1, characterized in that, The multi-source data acquisition module processes the regional fault dataset as follows: The vibration frequency and rotational speed fluctuation values, which reflect the core indicators of the internal mechanical operation status of the component, are recorded and marked as internal operation core data. The intuitive status of the corresponding component and mating parts, including the surface damage area and the actual clearance with the mating parts, are recorded and marked as external operation core data. Based on a large amount of historical measured data, data without analytical value are statistically filtered out, and the remaining valid data are summarized and marked as regional fault dataset.

3. The automotive vibration fault detection and analysis system according to claim 1, characterized in that, The process by which the intrinsic risk analysis module judges and analyzes internal operating status signals is as follows: The intrinsic risk analysis module extracts core internal operating data from the regional fault dataset, obtains historical values ​​of vibration frequency and speed fluctuation from the historical operating data of the corresponding component over the past three months, as well as the standard operating condition data of the corresponding component, sorts the historical operating data in chronological order, and obtains the time-weighted comprehensive value according to the formula.

4. The automotive vibration fault detection and analysis system according to claim 3, characterized in that, The time-weighted composite value is compared with the standard value corresponding to the standard operating condition data. The difference rate is obtained through a formula. The preset internal state qualification threshold is retrieved and the difference rate is analyzed together. When the difference rate exceeds the internal state qualification threshold by 5%-10%, it is judged as a slight anomaly; when the difference rate exceeds the internal state qualification threshold by 10%-20%, it is judged as a moderate anomaly; when the difference rate exceeds the internal state qualification threshold by 20% or more, it is judged as a severe anomaly; if the difference rate does not exceed the internal state qualification threshold, a normal signal is generated.

5. The automotive vibration fault detection and analysis system according to claim 1, characterized in that, The analysis process of the external comprehensive state performance signal by the external defect multi-level analysis module is as follows: The external defect multi-level analysis module extracts the core external operation data from the regional fault dataset, extracts the surface damage area value and the actual clearance value between the surface damage area and the mating parts, compares the surface damage area value with the standard allowable damage area to obtain the ratio, and marks it as the damage deviation; retrieves the standard clearance range for the part, takes the middle value of the standard clearance range as the reference value, subtracts the reference value from the actual clearance value, and divides it by the reference value, marking the resulting value as the clearance deviation.

6. The automotive vibration fault detection and analysis system according to claim 5, characterized in that, Retrieve preset deviation levels, which are categorized by deviation magnitude as follows: deviation ≤ 20% is marked as mild, deviation 20% < deviation ≤ 50% is marked as moderate, and deviation > 50% is marked as severe. Obtain the corresponding levels of damage deviation and clearance deviation. Take the higher of the two levels as the result of the component's external condition analysis. For example, if the damage deviation is mild and the clearance deviation is moderate, the result of the component's external condition analysis is moderate deviation. If both are mild or below, the highest actual level is used.

7. The automotive vibration fault detection and analysis system according to claim 6, characterized in that, Two sets of matching data are retrieved separately: the corresponding surface damage area value and the actual clearance value with the mating parts. These include the design parameters of the mating parts and the historical wear data of the mating parts. Based on the difference between the actual clearance and the standard clearance in the external operating core data, the actual wear amount of the mating parts is obtained. The actual wear amount of the mating parts is compared with the wear resistance limit in the design parameters. Combined with the changing trend of the historical wear data, the wear rate is obtained according to the formula.

8. The automotive vibration fault detection and analysis system according to claim 7, characterized in that, The preset wear rate threshold, the design wear resistance limit threshold, the actual wear amount, and the wear rate are retrieved and analyzed together: if the actual wear amount exceeds the design wear resistance limit threshold, it is directly determined that there is abnormal wear on the mating parts; if the actual wear amount does not exceed the wear resistance limit threshold, but the wear rate is greater than the wear rate threshold, it is determined that there is abnormal wear; if the actual wear amount does not exceed the wear resistance limit threshold and the wear rate is less than the wear rate threshold, it is determined that there is no abnormal wear on the mating parts. The specific process for determining the overall external condition and generating the overall external condition performance signal of automotive components is as follows: If the external condition of a component is at a moderate or higher level of deviation, and there is abnormal wear on the external parts, and the deviation levels of the two are consistent, then the overall external condition is determined to be abnormal. If there is only a single data deviation, or the deviation levels are inconsistent, further verification is required. Finally, combined with the external condition qualification threshold, the overall external condition performance signal of the automotive component is generated, including normal / slightly abnormal / moderately abnormal / severely abnormal.

9. The automotive vibration fault detection and analysis system according to claim 1, characterized in that, The analysis process of the integrated feedback module in obtaining internal operating status judgment signals and external comprehensive status performance signals is as follows: an abnormal indicator trend curve is constructed with time as the horizontal axis and the difference rate and deviation value of internal and external abnormal indicators as the vertical axis. The curve is fitted and the slope is calculated. A slope > 0 indicates that the abnormality is aggravated, and a slope ≤ 0 indicates that the abnormality is stable. Combined with the initial vibration abnormality degree of the non-conforming item, a fault root cause feedback signal is generated.

10. A method for detecting and analyzing automotive vibration faults, used in the automotive vibration fault detection and analysis system according to any one of claims 1-9, characterized in that, Includes the following steps: S1: The fault detection center generates a detection command, retrieves the overall vibration qualification threshold of the vehicle, completes the macroscopic evaluation by comparing real-time vibration data with the threshold, locates the component area corresponding to the non-conforming item, and selects the data collection range. S2: The multi-source data acquisition module collects internal and external core operating data around the selected area, and after filtering out invalid data, it summarizes and marks it as a regional fault dataset. S3: The internal risk analysis module generates internal status judgment signals by linking historical and standard data, and the external defect multi-level analysis module generates external comprehensive status performance signals by determining the deviation level and wear. S4: The integrated feedback module combines two types of signals through causal link and trend curve analysis to generate a fault root cause feedback signal. The fault detection center triggers the corresponding emergency plan according to the level of abnormality.

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