Automobile wire harness short circuit fault detection method, system, device and medium

Abstract

The invention provides an automobile wire harness short circuit fault detection method, system and device and a medium, and the method comprises the steps: extracting the transient characteristic quantity of a current signal of each monitoring node, and obtaining the amplitude attenuation characteristics of the current signal of each monitoring node in a wire harness propagation process based on the mutual information intensity inversion of the transient characteristic quantity between adjacent monitoring nodes; further identifying an abnormal wire harness path which is obviously influenced by common-mode interference based on the fluctuation difference degree of the amplitude of the common-mode current between adjacent monitoring nodes in spatial distribution, and constructing a common-mode interference topological graph which represents the abnormal state of the wire harness in combination with the response intensity of the common-mode current; and performing fuzzy reasoning on the short-circuit fault probability of each monitoring node according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topological map to obtain the fault membership degree of each monitoring node, and performing short-circuit fault correlation positioning on the target automobile wire harness through all the fault membership degrees. By adopting the scheme of the invention, the associated positioning of the short-circuit fault of the automobile wire harness under the common-mode interference condition can be realized.

Description

Technical Field

[0001] This application relates to the field of circuit detection technology. More specifically, this application relates to a method, system, device, and medium for detecting short - circuit faults in automotive wiring harnesses. Background Art

[0002] In the context of the increasing complexity of modern automotive electronic systems, the wiring harness, as a key carrier for signal and power transmission, its operating safety is directly related to the stability and functional reliability of the entire vehicle. Short - circuit faults are one of the most common and harmful fault types in automotive wiring harnesses. If not accurately located in time, it may lead to system anomalies, function failures, and even safety accidents. How to quickly and accurately detect and locate wiring harness short - circuit faults in a complex electromagnetic environment has become an important research direction for automotive electronic safety assurance.

[0003] In the prior art, resistance measurement, voltage offset analysis, or pulse reflection methods are often relied on to detect short - circuit faults in automotive wiring harnesses. However, under actual working conditions, due to the complex structure of the wiring harness, dense branch nodes, and the widespread influence of common - mode interference signals, traditional methods are difficult to effectively distinguish the current characteristic changes caused by faults from the fluctuations and disturbances brought by common - mode interference, easily leading to misjudgment and missed detection. Especially in an environment with strong common - mode interference, the transient response of the wiring harness current signal is masked, the propagation path is severely distorted, and the electrical characteristic analysis loses its reference significance, making it difficult to accurately lock the fault location. Therefore, how to achieve the associated positioning of short - circuit faults in automotive wiring harnesses under common - mode interference conditions has become a difficult problem faced by the industry. Summary of the Invention

[0004] This application provides a method, system, device, and medium for detecting short - circuit faults in automotive wiring harnesses, which can achieve the associated positioning of short - circuit faults in automotive wiring harnesses under common - mode interference conditions.

[0005] In the first aspect, this application provides a method for detecting short - circuit faults in automotive wiring harnesses, including the following steps: Set monitoring nodes at the key - path branch nodes of the target automotive wiring harness, and collect the current signals at each monitoring node through current sensors; Perform time - frequency processing on the current signals collected at each monitoring node, extract the mutation - associated feature components representing the sharp rise of the current in the current signals as transient feature quantities, reconstruct the propagation channels of the current signals between each branch based on the mutual - information intensity of the transient feature quantities between adjacent monitoring nodes, and then inversely obtain the amplitude attenuation characteristics of the current signals of each monitoring node during the propagation process in the wiring harness; Synchronously collect the common - mode current signals between each monitoring node and the reference ground terminal, identify the abnormal wiring harness paths significantly affected by common - mode interference based on the fluctuation difference degree of the common - mode current amplitudes between adjacent monitoring nodes in the spatial distribution, and then combine the common - mode current response intensity to construct a common - mode interference topology map representing the abnormal state of the wiring harness; Based on the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topological map, fuzzy inference is performed on the short-circuit fault probability of each monitoring node to obtain the fault membership degree of each monitoring node. The short-circuit fault correlation positioning of the target automotive wiring harness is carried out through the fault membership degrees of all monitoring nodes.

[0006] Preferably, time-frequency processing is performed on the current signals collected by each monitoring node, and the mutation correlation feature components representing the sharp rise characteristics of the current in the current signals are extracted as transient feature quantities, specifically including: Perform band-pass filtering on the current signals of each monitoring node to remove low-frequency noise and high-frequency interference; Perform time-frequency decomposition on the current signals after band-pass filtering through wavelet transform to obtain frequency components at different time scales; Identify the time nodes of current amplitude mutation in the time-frequency domain, and extract the peak feature, rising edge slope feature, and energy mutation feature of the frequency components corresponding to the time nodes; Take the combined vector of the peak feature, rising edge slope feature, and energy mutation feature as the transient feature quantity representing the sharp rise characteristics of the current.

[0007] Preferably, based on the mutual information intensity of transient feature quantities between adjacent monitoring nodes, the propagation channels of current signals between each branch are reconstructed, and then the amplitude attenuation characteristics of the current signals of each monitoring node during the propagation in the wiring harness are inversely obtained, specifically including: Determine the mutual information coefficient of transient feature quantities between every two adjacent monitoring nodes; Select the node pairs with mutual information coefficients higher than the set threshold, and extract the current amplitude ratio between the node pairs as the attenuation factor of the current signal; Reconstruct the propagation path of the current signal through all the attenuation factors and the transient current of the monitoring nodes; Determine the amplitude attenuation characteristics of the current signals of each monitoring node during the propagation in the wiring harness according to the inversion signal intensity corresponding to each monitoring node in the propagation path.

[0008] Preferably, based on the fluctuation difference degree of the spatial distribution of the common-mode current amplitude between adjacent monitoring nodes, the abnormal wiring harness paths significantly affected by common-mode interference are identified, and then a common-mode interference topological map representing the abnormal state of the wiring harness is constructed in combination with the common-mode current response intensity, specifically including: Determine the fluctuation difference degree of the spatial distribution of the common-mode current amplitude between every two adjacent monitoring nodes; Set a difference degree threshold, and identify the wiring harness paths with fluctuation difference degrees exceeding the difference degree threshold as abnormal paths significantly affected by common-mode interference; Construct a common-mode interference topological map representing the abnormal state of the wiring harness with the abnormal paths as nodes and the common-mode current response intensity as the edge weight.

[0009] Preferably, according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topology map, fuzzy reasoning is performed on the short-circuit fault probability of each monitoring node to obtain the fault membership degree of each monitoring node, which specifically includes: For each monitoring node, determine the initial membership degree characteristics of the short-circuit fault mode of the monitoring node according to the connection strength of the monitoring node in the common-mode interference topology map and the amplitude attenuation characteristics corresponding to the monitoring node; Construct a fuzzy rule base for node short-circuit fault detection; Through the fuzzy rule base, reason and evaluate the short-circuit fault probability of the monitoring node to obtain the fault evaluation value of the monitoring node; According to the initial membership degree characteristics, perform fuzzy fusion reasoning on the fault evaluation value to obtain the fault membership degree of the monitoring node, and further obtain the fault membership degree of each monitoring node.

[0010] Preferably, the current sensor is specifically a Hall current sensor.

[0011] Preferably, the common-mode current signals between each monitoring node and the reference ground terminal are synchronously collected by a common-mode current sensor.

[0012] In a second aspect, the present application provides an automotive wiring harness short-circuit fault detection system, including: An acquisition module, configured to set monitoring nodes at key path branch nodes of a target automotive wiring harness, and collect current signals at each monitoring node through a current sensor; A processing module, configured to perform time-frequency processing on the current signals collected by each monitoring node, extract the mutation correlation feature components representing the sharp rise characteristics of the current in the current signals as transient feature quantities, reconstruct the propagation channels of the current signals between each branch based on the mutual information intensity of the transient feature quantities between adjacent monitoring nodes, and further inversely obtain the amplitude attenuation characteristics of the current signals of each monitoring node during the propagation process in the wiring harness; The processing module is further configured to synchronously collect the common-mode current signals between each monitoring node and the reference ground terminal, identify the abnormal wiring harness paths significantly affected by common-mode interference based on the fluctuation difference degree of the spatial distribution of the common-mode current amplitudes between adjacent monitoring nodes, and further construct a common-mode interference topology map representing the abnormal state of the wiring harness in combination with the common-mode current response intensity; An execution module, configured to perform fuzzy reasoning on the short-circuit fault probability of each monitoring node according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topology map to obtain the fault membership degree of each monitoring node, and perform short-circuit fault correlation positioning on the target automotive wiring harness through the fault membership degrees of all monitoring nodes.

[0013] In a third aspect, the present application provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned automotive wiring harness short-circuit fault detection method.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned automotive wiring harness short-circuit fault detection method is implemented.

[0015] The technical solutions provided by the disclosed embodiments of the present application have the following beneficial effects: In the embodiments of the present application, monitoring nodes are set at the key path branch nodes of the target automotive wiring harness, and current sensors are used to collect current signals at each monitoring node. The current signals collected at each monitoring node are subjected to time-frequency processing, and the mutation correlation feature components characterizing the sharp rise of the current in the current signal are extracted as transient feature quantities. Based on the mutual information intensity of the transient feature quantities between adjacent monitoring nodes, the propagation channels of the current signals between each branch are reconstructed, and then the amplitude attenuation characteristics of the current signals of each monitoring node during the wiring harness propagation process are inversely obtained. The common-mode current signals between each monitoring node and the reference ground end are synchronously collected, and the abnormal wiring harness paths significantly affected by common-mode interference are identified based on the fluctuation difference of the common-mode current amplitudes between adjacent monitoring nodes in the spatial distribution. Furthermore, a common-mode interference topology map characterizing the abnormal state of the wiring harness is constructed by combining the common-mode current response intensity. The short-circuit fault probability of each monitoring node is fuzzy-inferred according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topology map, and the fault membership degree of each monitoring node is obtained. The short-circuit fault correlation positioning of the target automotive wiring harness is performed through the fault membership degrees of all monitoring nodes.

[0016] It can be seen that in this application, the short - circuit fault probability of the monitoring node is fuzzily inferred by monitoring the amplitude - attenuation characteristics and the common - mode interference topology map corresponding to the monitoring node, and the fault membership degree of the monitoring node is obtained, thereby completing the associated positioning of the short - circuit fault of the target automotive wiring harness. First, by performing time - frequency processing on the current signal, transient feature quantities characterizing the sudden - onset characteristics of the fault are extracted, highlighting the short - time characteristics of the abnormal current rise, effectively enhancing the recognition sensitivity of the fault signal. Secondly, based on the mutual - information intensity of the transient feature quantities between adjacent monitoring nodes, the propagation channels of the current signals between branches are reconstructed, and the amplitude - attenuation characteristics of the current signals of each monitoring node during the propagation in the wiring harness are inversely obtained. The amplitude - attenuation characteristics can reflect the energy - attenuation process of the fault signal from the source point to each monitoring node, revealing the characteristics of the abnormal current propagation path, and improving the spatial positioning accuracy of the short - circuit fault in the complex wiring harness structure, thereby enhancing the spatial judgment ability of the fault location. Then, based on the fluctuation difference degree of the spatial distribution of the common - mode current amplitudes between adjacent monitoring nodes, the abnormal wiring harness paths significantly affected by common - mode interference are identified. Furthermore, combined with the common - mode current response intensity, a common - mode interference topology map characterizing the abnormal state of the wiring harness is constructed. By analyzing the spatial fluctuation difference of the common - mode current amplitudes between adjacent monitoring nodes, the abnormal paths significantly affected by common - mode interference are identified, effectively avoiding the interference of the common - mode signal on the fault - feature recognition. Combining the common - mode current response intensity to construct the interference topology map can comprehensively characterize the interference - sensitive areas of the wiring harness, providing a reliable interference - background criterion for fault location, thereby enhancing the accuracy and stability of the positioning. Finally, the short - circuit fault probability of the monitoring node is fuzzily inferred by the amplitude - attenuation characteristics and the common - mode interference topology map corresponding to the monitoring node, and then the associated positioning of the short - circuit fault of the target automotive wiring harness is carried out. It can realize the associated analysis of multi - feature and multi - path information. Through the global analysis of the membership degree, the precise positioning of the wiring - harness short - circuit fault is realized, significantly improving the anti - interference ability of the automotive - wiring - harness short - circuit - fault detection. In summary, the solution of this application can realize the associated positioning of the automotive - wiring - harness short - circuit fault under the condition of common - mode interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is an exemplary flowchart of a method for detecting a short - circuit fault of an automotive wiring harness according to some embodiments of the present application; Figure 2 is a schematic diagram of an application scenario of a system for detecting a short - circuit fault of an automotive wiring harness according to some embodiments of the present application; Figure 3 is a schematic flowchart for determining a common - mode interference topology map according to some embodiments of the present application; Figure 4 is a schematic structural diagram of a system for detecting a short - circuit fault of an automotive wiring harness according to some embodiments of the present application; Figure 5It is a structural schematic diagram of a computer device for implementing a method for detecting a short circuit fault in an automobile wiring harness according to some embodiments of the present application. DETAILED DESCRIPTION

[0018] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1 , which is an exemplary flow chart of a method for detecting a short circuit fault of an automobile wiring harness according to some embodiments of the present application, and the method for detecting a short circuit fault of an automobile wiring harness mainly comprises the following steps: In step 101, monitoring nodes are set at the critical path branch nodes of the target vehicle wiring harness, and current signals at each monitoring node are collected by current sensors.

[0020] It should be noted that the critical path branch nodes in this application refer to the locations where the current propagation paths branch or merge in the wiring harness topology structure. These nodes have a significant impact on the propagation direction and intensity of the fault signal. The critical path branch nodes in this application specifically include the branch points where the main wiring harness is connected to multiple branch wiring harnesses, connector connection points, and relay branch ports. These locations are the key control points where the current signal propagation paths diverge or converge.

[0021] In some embodiments, reference Figure 2 As shown, the figure is a schematic diagram of the application scenario of the automobile wiring harness short-circuit fault detection system shown in some embodiments of the present application, which includes three main components: an acquisition device, a server and a data storage device. The acquisition device is responsible for collecting the current signal and common-mode current signal at each monitoring node, and sending the collected current signal to the server through the communication network. The automobile wiring harness short-circuit fault detection system is running in the server, and the server stores the detection results in the data storage device and visualizes them.

[0022] In step 102, the current signal collected by each monitoring node is subjected to time-frequency processing, and the mutation-related characteristic components in the current signal that characterize the sharp rise characteristics of the current are extracted as transient characteristic quantities. The propagation channels of the current signals between the branches are reconstructed based on the mutual information strength of the transient characteristic quantities between adjacent monitoring nodes, and then the amplitude attenuation characteristics of the current signals of each monitoring node during the propagation process in the wire harness are inverted.

[0023] In some embodiments, the current signal collected by each monitoring node is subjected to time-frequency processing, and the mutation-related characteristic component representing the sharp rise of the current in the current signal is extracted as the transient characteristic quantity, which can be implemented by the following steps: Band-pass filter the current signal of each monitoring node to remove low-frequency noise and high-frequency interference; The current signal after band-pass filtering is subjected to time-frequency decomposition through wavelet transform to obtain frequency components at different time scales; Identify the time nodes where the current amplitude suddenly changes in the time-frequency domain, and extract the peak feature, rising edge slope feature, and energy mutation feature of the frequency components corresponding to the time nodes; Take the combined vector of the peak feature, rising edge slope feature, and energy mutation feature as the mutation correlation feature component characterizing the rapid rise of the current, which is used as the transient feature quantity.

[0024] It should be noted that the frequency components in this application refer to the periodic components contained in the current signal within different frequency ranges, which are used to characterize the energy distribution and change characteristics of the current signal at each frequency scale; the transient feature quantity in this application is a feature that measures the strength of the mutation characteristics of the current signal in a short time, and is used to characterize the performance intensity and response mode of the rapid rise of the current caused by the short-circuit fault in the time-frequency domain.

[0025] In specific implementation, first, band-pass filtering is performed on the current signals of each monitoring node to remove low-frequency noise and high-frequency interference, which can be achieved in the following manner, i.e., for the current signals collected by each monitoring node, a band-pass filter is used to filter the current signals. The upper and lower limit frequencies of the band-pass filter can be set to 20 Hz to 20 kHz. Through band-pass filtering, the low-frequency operating condition fluctuation noise and high-frequency electromagnetic interference in the current signals can be filtered out. Secondly, time-frequency decomposition is performed on the current signals after band-pass filtering through wavelet transform to obtain frequency components at different time scales, which can be achieved in the following manner, i.e., discrete wavelet transform processing is performed on the filtered current signals. The Daubechies db4 wavelet basis function can be selected, and multi-level decomposition (4 to 6 levels) is used to divide the current signals into multiple sub-band signals according to time scales. Each sub-band corresponds to a different frequency range, and the decomposition results are used as the frequency components at different time scales. Then, the time nodes with sudden changes in the current amplitude in the time-frequency domain are identified, and the peak feature, rising edge slope feature, and energy mutation feature of the frequency components corresponding to the time nodes are extracted, which can be achieved in the following steps, i.e., by analyzing the amplitude changes of wavelet coefficients at each scale, the time points where the current signals have sudden changes in each sub-band are identified. The mutation points can be obtained by setting a threshold for the coefficient amplitude change rate, which will not be elaborated here. Further, the amplitude peak value, rising edge slope, and the change amplitude of the wavelet energy near this moment of the frequency components corresponding to the time nodes are extracted. The amplitude peak value is used as the peak feature in this application, the rising edge slope is used as the rising edge slope feature in this application, and the change amplitude of the wavelet energy near this moment is used as the energy mutation feature in this application. Finally, the combined vector of the peak feature, rising edge slope feature, and energy mutation feature is used as the transient feature quantity characterizing the sharp rise of the current, which can be achieved in the following manner, i.e., for each monitoring node, all the extracted features are combined into a vector, and the obtained vector is used as the transient feature quantity in this application.

[0026] In some embodiments, based on the mutual information intensity of the transient feature quantities between adjacent monitoring nodes, the propagation channels of the current signals between each branch are reconstructed, and then the amplitude attenuation characteristics of the current signals of each monitoring node during the propagation in the wire harness are inversely obtained, which can be achieved in the following steps: Determine the mutual information coefficient of the transient feature quantities between every two adjacent monitoring nodes; Select the node pairs with mutual information coefficients higher than the set threshold, and extract the current amplitude ratio between the node pairs as the attenuation factor of the current signal; Reconstruct the propagation path of the current signal through all the attenuation factors and the transient current of the monitoring nodes; Determine the amplitude attenuation characteristics of the current signals of each monitoring node during the propagation in the wire harness according to the inverse signal intensity corresponding to each monitoring node in the propagation path.

[0027] It should be noted that the mutual information coefficient in this application is an index to measure the amount of associated information between two nodes and is used to reflect the strength of the statistical dependence relationship; the attenuation factor in this application refers to the proportion of the amplitude change of the current signal during the propagation process between two monitoring nodes and is used to characterize the loss degree of the current signal energy during transmission along the path; the transient current in this application refers to the non-steady-state current response that is instantaneously generated and has a rapidly changing amplitude when the circuit state changes suddenly; the inverse signal strength in this application refers to the theoretical current amplitude that the monitoring node should have calculated based on the propagation path and the attenuation factor; the amplitude attenuation characteristic in this application is a characteristic to measure the degree of weakening of the current signal strength during the propagation process.

[0028] When specifically implemented, first, to determine the mutual information coefficient of the transient characteristic quantities between every two adjacent monitoring nodes can be achieved by the following method, that is: the similarity of the transient characteristic quantities between every two adjacent monitoring nodes can be used as the mutual information coefficient of the transient characteristic quantities between every two adjacent monitoring nodes; second, to select the node pairs with the mutual information coefficient higher than the set threshold and extract the ratio of the current amplitudes between the node pairs as the attenuation factor of the current signal can be achieved by the following method, that is: it should be noted that the threshold value in this embodiment can be set according to historical test data, which will not be elaborated here. Screen out the node pairs with strong correlation from all adjacent monitoring nodes according to the preset threshold, and calculate the ratio of the peak values of the transient current signals of each pair of nodes, and use this ratio as the attenuation factor of the corresponding path current signal; then, to reconstruct the propagation path of the current signal through all the attenuation factors and the transient current of the monitoring nodes can be achieved by the following method, that is: construct a directed graph with the monitoring nodes as the graph vertices and the attenuation factors as the edge attributes, and restore the propagation path of the current signal using the principle of the minimum attenuation path. It should be further noted that the propagation path in this application is an ideal propagation path; finally, to determine the amplitude attenuation characteristic of the current signal of each monitoring node during the propagation process in the wire harness according to the inverse signal strength corresponding to each monitoring node in the propagation path can be achieved by the following method, that is: inversely deduce the signal propagation strength of each node in the directed graph based on the cumulative attenuation of the path of each node and the transient current intensity of the starting node, compare the signal propagation strength with the actually measured signal, and use the comparison result as the amplitude attenuation characteristic of the current signal of the monitoring node during the propagation process in the wire harness.

[0029] It should be noted that, in the solution of this application, by reconstructing the propagation channels of current signals among various branches based on the mutual information intensity of transient feature quantities between adjacent monitoring nodes, the problem in the prior art of being difficult to accurately determine the current propagation path inside a complex wire harness can be solved. Traditional methods usually rely on fixed topological structures or single-point amplitude analysis, ignoring the statistical correlation between signals, resulting in inaccurate path identification and affecting the fault location effect. However, in the solution of this application, the correlation degree of features between adjacent nodes is quantified by calculating the mutual information coefficient, weak-correlation paths are eliminated, and effective propagation channels are accurately screened, realizing the dynamic adaptive identification of propagation paths and significantly improving the accuracy of path reconstruction. Further, the current amplitude ratio is extracted as the attenuation factor to quantitatively describe the energy of the current signal during the propagation process, which can solve the defect that traditional technologies cannot effectively reflect the signal attenuation change, enhance the ability to capture the signal energy attenuation law inside the wire harness, and combine the attenuation factors of the entire network with transient current data to reconstruct the current propagation path and invert the amplitude attenuation characteristics of each node, so as to provide a quantitative evaluation of propagation intensity and strengthen the spatial distribution analysis of fault signals.

[0030] In step 103, the common-mode current signals between each monitoring node and the reference ground terminal are synchronously collected, and the abnormal wire harness paths significantly affected by common-mode interference are identified based on the fluctuation difference degree of the common-mode current amplitudes between adjacent monitoring nodes in the spatial distribution. Furthermore, a common-mode interference topology map characterizing the abnormal state of the wire harness is constructed in combination with the common-mode current response intensity.

[0031] It should be noted that the reference ground terminal in this application refers to the grounding point that serves as the current return path and potential reference in the automotive electrical system; the common-mode current signal in this application refers to the current component that commonly appears in all conductors in the automotive wire harness relative to the reference ground terminal; it should also be noted that in this application, the common-mode current signals between each monitoring node and the reference ground terminal are synchronously collected by a common-mode current sensor.

[0032] In some embodiments, referring to Figure 3 As shown in the figure, which is a schematic flow chart for determining the common-mode interference topology map in some embodiments of this application. In this embodiment, the abnormal wire harness paths significantly affected by common-mode interference are identified based on the fluctuation difference degree of the common-mode current amplitudes between adjacent monitoring nodes in the spatial distribution. Furthermore, the common-mode interference topology map characterizing the abnormal state of the wire harness can be constructed in combination with the common-mode current response intensity by the following steps: In step 1031, the fluctuation difference degree of the common-mode current amplitudes between every two adjacent monitoring nodes in the spatial distribution is determined; In step 1032, a difference degree threshold is set, and the wire harness paths with a fluctuation difference degree exceeding the difference degree threshold are identified as abnormal paths significantly affected by common-mode interference; In step 1033, a common-mode interference topology map characterizing the abnormal state of the wire harness is constructed with the abnormal path as the node and the common-mode current response intensity as the edge weight.

[0033] It should be noted that the fluctuation difference degree in this application is an index to measure the difference degree of the statistical distribution consistency of the common-mode current amplitudes of adjacent monitoring nodes in the local spatial region; the common-mode interference topology map in this application is a graph structure reflecting the interference distribution relationship in the automotive wire harness.

[0034] In specific implementation, first, the fluctuation difference degree of the common-mode current amplitudes between every two adjacent monitoring nodes in the spatial distribution can be realized by the following method, that is: for every two adjacent monitoring nodes, perform amplitude statistical analysis on the common-mode current signals collected by each pair of adjacent monitoring nodes to obtain the mean square deviations corresponding to the two adjacent monitoring nodes, and use the absolute difference between the two mean square deviations as the fluctuation difference degree of the common-mode current amplitudes between the two adjacent monitoring nodes in the spatial distribution, so as to obtain the fluctuation difference degree of the common-mode current amplitudes between every two adjacent monitoring nodes in the spatial distribution; second, set a difference degree threshold, and the wire harness path with the fluctuation difference degree exceeding the difference degree threshold can be identified as an abnormal path significantly affected by common-mode interference by the following method, that is: set a difference degree threshold obtained based on experience or statistical analysis, and screen out the wire harness paths with the fluctuation significantly exceeding this threshold, and use them as the abnormal paths significantly affected by common-mode interference; then, a common-mode interference topology map characterizing the abnormal state of the wire harness is constructed with the abnormal path as the node and the common-mode current response intensity as the edge weight by the following method, that is: the screened abnormal paths can be used as the nodes of the graph, and the normalized value of the absolute difference of the common-mode current response intensities between the corresponding monitoring nodes is used as the edge weight to construct a weighted topology map, and this topology map is used as the common-mode interference topology map characterizing the abnormal state of the wire harness in this application.

[0035] It should be noted that by introducing the fluctuation difference degree of the common-mode current amplitudes in the spatial distribution, the local change intensity of the common-mode interference between adjacent nodes is accurately quantified in the solution of this application, which can solve the problem of inaccurate identification of abnormal paths caused by the lack of spatial distribution analysis in the prior art. By using the mean square deviation difference as the difference degree index, the wire harness paths significantly affected by interference can be effectively identified, avoiding the positioning error caused by relying on the overall current level. Further, by combining the common-mode current response intensity to construct the interference topology map, the graph structure modeling of the interference strength relationship between abnormal paths is realized, which not only improves the accuracy of interference source positioning, but also enhances the diagnostic resolution of the wire harness system for common-mode interference.

[0036] In step 104, according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topology map, fuzzy inference is performed on the short-circuit fault probability of each monitoring node to obtain the fault membership degree of each monitoring node, and the target automotive wiring harness is subjected to short-circuit fault correlation positioning through the fault membership degrees of all monitoring nodes.

[0037] In some embodiments, the fuzzy inference of the short-circuit fault probability of each monitoring node according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topology map to obtain the fault membership degree of each monitoring node can be realized by the following steps: For each monitoring node, determine the initial membership degree characteristics of the short-circuit fault mode of the monitoring node according to the connection strength of the monitoring node in the common-mode interference topology map and the amplitude attenuation characteristics corresponding to the monitoring node; Construct a fuzzy rule base for node short-circuit fault detection; Through the fuzzy rule base, the short-circuit fault probability of the monitoring node is inferred and evaluated to obtain the fault evaluation value of the monitoring node; According to the initial membership degree characteristics, fuzzy fusion inference is performed on the fault evaluation value to obtain the fault membership degree of the monitoring node, and then the fault membership degrees of each monitoring node are obtained.

[0038] It should be noted that the connection strength in this application is used to measure the association degree of the monitoring node with other nodes in the common-mode interference topology map; the initial membership degree characteristics in this application are used to quantify the fuzzy expression of the connection strength and amplitude attenuation characteristics of the monitoring node on the possibility of short-circuit faults; the fuzzy rule base in this application is a set of rules that quantify the influence of the node connection strength and amplitude attenuation characteristics on the short-circuit fault probability; the fault membership degree of this application is a fuzzy probability index that measures the possibility of a short-circuit fault occurring in the monitoring node.

[0039] In specific implementation, for each monitoring node, first, the initial membership degree feature of the short - circuit fault mode of the monitoring node can be determined according to the connection strength of the monitoring node in the common - mode interference topology map and the amplitude attenuation characteristic corresponding to the monitoring node, which can be implemented in the following way: normalize the connection strength of the monitoring node in the common - mode interference topology map and the amplitude attenuation characteristic corresponding to the monitoring node, and then, for the normalization results of these two quantization indexes, apply the triangular membership function to convert the connection strength and the amplitude attenuation characteristic into membership degree values of the short - circuit fault mode. The membership degree values can reflect the fuzzy degree of the node fault possibility. Further, perform a cross - operation on the two output membership degree values and use the cross - operation result as the initial membership degree feature of the short - circuit fault mode of the monitoring node; Second, constructing a fuzzy rule base for node short - circuit fault detection can be implemented in the following way: obtain the fault test data obtained from pre - experiments, combine the empirical knowledge in the field of wiring harness fault diagnosis and the fault test data, refine and summarize the corresponding fuzzy rules, such as "if the connection strength is high and the amplitude attenuation is significant, then the fault probability is high", and use the rule - base management tool to formally store all the refined fuzzy rules, and ensure that the logic between the rules is complete and conflict - free. The set of formally stored rules is used as the fuzzy rule base for node short - circuit fault detection in this application. It should be noted that the rule set in this application can cover various short - circuit fault modes and support the subsequent fuzzy reasoning process to realize the associated detection and evaluation of multi - node short - circuit faults; Then, the short - circuit fault probability of the monitoring node is inferred and evaluated through the fuzzy rule base to obtain the fault evaluation value of the monitoring node, which can be implemented in the following way: convert the amplitude attenuation characteristic of the monitoring node and the connection strength in the common - mode interference topology map into corresponding membership degree values through a pre - defined membership function, then use the Mamdani fuzzy inference engine to match the fuzzy rules in the fuzzy rule base one by one, calculate the output fuzzy set according to the activation degree of the pre - conditions of the fuzzy rules, and then form an overall fuzzy output by aggregating the output fuzzy sets of all activated rules. Further, use the defuzzification method to convert the fuzzy output into a specific fault evaluation value. The fault evaluation value can reflect the probability of the short - circuit fault of the monitoring node; Finally, according to the initial membership degree feature, perform fuzzy fusion inference on the fault evaluation value to obtain the fault membership degree of the monitoring node, which can be implemented in the following way: the initial membership degree feature and the fault evaluation value can be respectively mapped to a unified membership degree space through the membership function, adopt the weighted fusion algorithm, and combine the pre - set weight coefficients to perform weighted fusion on the initial membership degree and the fault evaluation value. The weight distribution reflects their importance in fault determination, balances the contributions of each feature to the final fault membership degree, and then converts the weighted fusion result into a specific value through the defuzzification method, and use the specific value as the fault membership degree of the monitoring node. The fault membership degree can accurately reflect the possibility of the node short - circuit fault; Repeat the above steps to obtain the fault membership degrees of the remaining monitoring nodes.

[0040] It should be noted that, in the solution of this application, by fusing the amplitude attenuation feature and the connection strength in the common-mode interference topology map, a membership degree feature reflecting the node fault risk is established. Combining with the fuzzy rule base, a system inference for non-linear fault modes is realized. Then, through the fuzzy fusion inference mechanism, multi-source feature information is synthesized, solving the problem of insufficient accuracy of fault location affected by a single feature in the existing methods, effectively improving the accuracy and anti-interference ability of the multi-node short-circuit fault probability estimation, and enhancing the fault location stability and credibility of the wire harness system under complex coupling interference.

[0041] In some embodiments, the short-circuit fault correlation location of the target automotive wire harness through the fault membership degrees of all monitoring nodes can be realized by the following steps: Screen out the monitoring nodes whose fault membership exceeds the preset threshold as candidate fault nodes; Determine the positional relationship of the candidate fault nodes in the automotive wire harness topology map, and then combine the amplitude attenuation feature during the wire harness propagation process to determine the set of fault nodes with the highest fault probability; Based on the fault membership degrees of the monitoring nodes in the set of fault nodes, correlate and locate the path section of the short-circuit fault in the target automotive wire harness.

[0042] It should be noted that in this application, the preset threshold can be adaptively determined by a fixed empirical value or the statistical quantile based on the membership degrees of all nodes, without specific limitation.

[0043] Specifically, when implementing, determining the positional relationship of the candidate fault nodes in the automotive wire harness topology map, and then combining the amplitude attenuation feature during the wire harness propagation process to determine the set of fault nodes with the highest fault probability can be realized by the following steps, that is: the candidate fault nodes can be mapped in the predefined automotive wire harness topology map to identify the branch, branch point and relative connection position where they are located, and then identify the connected paths between each candidate node. For each connected path, compare the amplitude attenuation feature corresponding to the connected path with the preset amplitude attenuation threshold, and then use the connected path whose amplitude attenuation feature exceeds the amplitude attenuation threshold as the candidate fault path. Then, use all the monitoring nodes corresponding to all the candidate fault paths to form a set as the set of fault nodes with the highest fault probability; finally, based on the fault membership degrees of the monitoring nodes in the set of fault nodes, correlating and locating the path section of the short-circuit fault in the target automotive wire harness can be realized in the following way, that is: calculate the average fault membership degree between two adjacent monitoring nodes with a connection relationship, and then compare the average fault membership degree with the preset fault membership degree threshold, and use the connected path corresponding to the average fault membership degree greater than the fault membership degree threshold as the path section of the short-circuit fault in the target automotive wire harness. It should be noted that the fault membership degree threshold in this application can be set according to the test data, which will not be elaborated here.

[0044] On the other hand, in some embodiments, the present application provides an automotive wiring harness short-circuit fault detection system. Refer to Figure 4 , which is a schematic structural diagram of the automotive wiring harness short-circuit fault detection system shown in some embodiments of the present application. The automotive wiring harness short-circuit fault detection system 400 includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described as follows: The acquisition module 401. In the present application, the acquisition module 401 is mainly used to set monitoring nodes at the key path branch nodes of the target automotive wiring harness, and collect current signals at each monitoring node through current sensors; The processing module 402. In the present application, the processing module 402 is used to perform time-frequency processing on the current signals collected at each monitoring node, extract the mutation correlation feature components that characterize the sharp rise characteristics of the current in the current signals as transient feature quantities, reconstruct the propagation channels of the current signals between each branch based on the mutual information intensity of the transient feature quantities between adjacent monitoring nodes, and then inversely obtain the amplitude attenuation characteristics of the current signals of each monitoring node during the wiring harness propagation process; In the present application, the processing module 402 is also used to synchronously collect the common-mode current signals between each monitoring node and the reference ground terminal, identify the abnormal wiring harness paths significantly affected by common-mode interference based on the fluctuation difference degree of the common-mode current amplitudes between adjacent monitoring nodes in the spatial distribution, and then combine the common-mode current response intensity to construct a common-mode interference topology map representing the abnormal state of the wiring harness; The execution module 403. In the present application, the execution module 403 is mainly used to perform fuzzy reasoning on the short-circuit fault probability of each monitoring node according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topology map, obtain the fault membership degree of each monitoring node, and perform short-circuit fault correlation positioning on the target automotive wiring harness through the fault membership degrees of all monitoring nodes.

[0045] In addition, the present application also provides a computer device. The computer device includes a memory and a processor. The memory stores code, and the processor is configured to obtain the code and execute the above-mentioned automotive wiring harness short-circuit fault detection method.

[0046] In some embodiments, refer to Figure 5 , which is a schematic structural diagram of the computer device for implementing the automotive wiring harness short-circuit fault detection method shown in some embodiments of the present application. The automotive wiring harness short-circuit fault detection method in the above embodiments can be implemented by Figure 5 the computer device shown. The computer device 500 includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0047] The processor 501 may be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0048] The communication bus 502 can be used to transfer information between the above components.

[0049] The memory 503 may be a read-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disks, or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 503 may exist independently and be connected to the processor 501 through the communication bus 502. The memory 503 may also be integrated with the processor 501.

[0050] Among them, the memory 503 is used to store the program code for executing the solution of this application, and is controlled by the processor 501 to execute. The processor 501 is used to execute the program code stored in the memory 503. The program code may include one or more software modules. The automotive wiring harness short-circuit fault detection method in the above embodiments can be implemented by one or more software modules in the program code of the processor 501 and the memory 503.

[0051] The communication interface 504, using any device such as a transceiver, is used to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0052] In a specific implementation, as an embodiment, a computer device may include multiple processors, and each of these processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. The processor here may refer to one or more devices, circuits, and / or processing cores for processing data (such as computer program instructions).

[0053] The above computer device may be a general-purpose computer device or a special-purpose computer device. In a specific implementation, the computer device may be a desktop computer, a laptop computer, a network server, a personal digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. The embodiments of the present application do not limit the type of the computer device.

[0054] In addition, the present application also provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the above-mentioned method for detecting a short-circuit fault of an automotive wiring harness is implemented.

[0055] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present application.

[0056] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these changes and modifications.

Claims

1. A method for detecting short - circuit faults in an automotive wiring harness, characterized in that, Including the following steps: Set monitoring nodes at the key path branch nodes of the target automotive wiring harness, and collect the current signals at each monitoring node through current sensors; Perform time-frequency processing on the current signals collected at each monitoring node, extract the mutation correlation feature components representing the characteristic of rapid current rise in the current signals as transient feature quantities, reconstruct the propagation channels of the current signals between each branch based on the mutual information intensity of the transient feature quantities between adjacent monitoring nodes, and then inversely obtain the amplitude attenuation characteristics of the current signals of each monitoring node during the propagation in the wiring harness; Synchronously collect the common-mode current signals between each monitoring node and the reference ground terminal, identify the abnormal wiring harness paths significantly affected by common-mode interference based on the fluctuation difference degree of the common-mode current amplitudes between adjacent monitoring nodes in the spatial distribution, and then construct a common-mode interference topology map representing the abnormal state of the wiring harness in combination with the common-mode current response intensity; Perform fuzzy reasoning on the short-circuit fault probability of each monitoring node according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topology map, obtain the fault membership degree of each monitoring node, and perform short-circuit fault correlation positioning on the target automotive wiring harness through the fault membership degrees of all monitoring nodes.

2. The method according to claim 1, wherein Performing time-frequency processing on the current signals collected at each monitoring node and extracting the mutation correlation feature components representing the characteristic of rapid current rise in the current signals as transient feature quantities specifically includes: Perform band-pass filtering on the current signals of each monitoring node to remove low-frequency noise and high-frequency interference; Perform time-frequency decomposition on the band-pass filtered current signals through wavelet transform to obtain frequency components at different time scales; Identify the time nodes of current amplitude mutation in the time-frequency domain, and extract the peak feature, rising edge slope feature and energy mutation feature of the frequency components corresponding to the time nodes; Take the combined vector of the peak feature, rising edge slope feature and energy mutation feature as the transient feature quantity representing the characteristic of rapid current rise.

3. The method according to claim 1, wherein Reconstructing the propagation channels of the current signals between each branch based on the mutual information intensity of the transient feature quantities between adjacent monitoring nodes, and then inversely obtaining the amplitude attenuation characteristics of the current signals of each monitoring node during the propagation in the wiring harness specifically includes: Determine the mutual information coefficient of the transient feature quantities between every two adjacent monitoring nodes; Select the node pairs with mutual information coefficients higher than the set threshold, and extract the current amplitude ratio between the node pairs as the attenuation factor of the current signal; Reconstruct the propagation path of the current signal through all the attenuation factors and the transient current of the monitoring nodes; Determine the amplitude attenuation characteristics of the current signals of each monitoring node during the propagation in the wiring harness according to the inverse signal intensity corresponding to each monitoring node in the propagation path.

4. The method according to claim 1, wherein Identifying the abnormal wiring harness paths significantly affected by common-mode interference based on the fluctuation difference degree of the common-mode current amplitudes between adjacent monitoring nodes in the spatial distribution, and then constructing a common-mode interference topology map representing the abnormal state of the wiring harness in combination with the common-mode current response intensity specifically includes: Determine the fluctuation difference degree of the common-mode current amplitudes between every two adjacent monitoring nodes in the spatial distribution; Set a difference degree threshold, and identify the wiring harness paths with fluctuation difference degrees exceeding the difference degree threshold as the abnormal paths significantly affected by common-mode interference; Construct a common-mode interference topology map representing the abnormal state of the wire harness with the abnormal path as the node and the common-mode current response intensity as the edge weight.

5. The method according to claim 1, wherein Fuzzy inference is performed on the short-circuit fault probability of each monitoring node according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topology map to obtain the fault membership degree of each monitoring node, which specifically includes: For each monitoring node, determine the initial membership degree characteristics of the monitoring node short-circuit fault mode according to the connection strength of the monitoring node in the common-mode interference topology map and the amplitude attenuation characteristics corresponding to the monitoring node; Construct a fuzzy rule base for node short-circuit fault detection; Infer and evaluate the short-circuit fault probability of the monitoring node through the fuzzy rule base to obtain the fault evaluation value of the monitoring node; Perform fuzzy fusion inference on the fault evaluation value according to the initial membership degree characteristics to obtain the fault membership degree of the monitoring node, and then obtain the fault membership degree of each monitoring node.

6. The method according to claim 1, characterized in that, The current sensor is specifically a Hall current sensor.

7. The method according to claim 1, characterized in that, Synchronously collect the common-mode current signals between each monitoring node and the reference ground terminal through the common-mode current sensor.

8. An automotive wiring harness short circuit fault detection system, characterized in that, It includes: An acquisition module, configured to set monitoring nodes at the key path branch nodes of the target vehicle wire harness, and collect the current signals at each monitoring node through the current sensor; A processing module, configured to perform time-frequency processing on the current signals collected by each monitoring node, extract the mutation correlation feature components representing the sharp rise characteristics of the current in the current signals as transient feature quantities, reconstruct the propagation channels of the current signals between each branch based on the mutual information intensity of the transient feature quantities between adjacent monitoring nodes, and then inversely obtain the amplitude attenuation characteristics of the current signals of each monitoring node during the propagation process in the wire harness; The processing module is further configured to synchronously collect the common-mode current signals between each monitoring node and the reference ground terminal, identify the abnormal wire harness paths significantly affected by common-mode interference based on the fluctuation difference degree of the spatial distribution of the common-mode current amplitudes between adjacent monitoring nodes, and then construct a common-mode interference topology map representing the abnormal state of the wire harness in combination with the common-mode current response intensity; An execution module, configured to perform fuzzy inference on the short-circuit fault probability of each monitoring node according to the amplitude attenuation characteristics corresponding to each monitoring node and the common-mode interference topology map to obtain the fault membership degree of each monitoring node, and perform short-circuit fault correlation positioning on the target vehicle wire harness through the fault membership degrees of all monitoring nodes.

9. A computer device, the computer device includes a memory and a processor, the memory stores code, characterized in that, The processor is configured to obtain the code and execute the vehicle wire harness short-circuit fault detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the vehicle wire harness short-circuit fault detection method according to any one of claims 1 to 7.

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