Fault detection method for vehicle pedal motor

Abstract

The invention relates to a motor fault diagnosis technology, in particular to a fault detection method for a vehicle pedal motor, and aims to solve the problem that in the prior art, in the production and manufacturing link of the vehicle pedal motor, a magnetic shoe is fixed to the inner wall of a motor shell through a support, and the motor shell is damaged. And as assembling processes such as manual auxiliary press fitting or simple tool positioning are generally adopted by small and medium-sized manufacturing enterprises, the equipment precision is limited, and mounting deviations such as micro eccentricity or local inclination of the magnetic shoe are easy to generate. The magnetic field interference is eliminated through synchronous acquisition, calibration and correlation analysis, the magnet installation state and the coil electrical state are accurately judged, and the method has the advantages that the magnet installation state and the coil electrical state can be efficiently, accurately and synchronously detected, and misjudgment and missing detection caused by interference are avoided.

Description

Technical Field

[0001] This application relates to motor fault diagnosis technology, and more specifically, to a fault detection method for a vehicle pedal motor. Background Technology

[0002] In the manufacturing process of automotive pedal motors, the magnets are fixed to the inner wall of the motor housing by brackets. Because small and medium-sized manufacturers generally use manual pressing or simple tooling for positioning, equipment precision is limited, easily leading to installation deviations such as slight eccentricity or localized tilting of the magnets. Simultaneously, the coil winding process often involves hidden electrical faults such as latent breaks in the enameled wire or micro-short circuits between turns, which are difficult to identify using conventional methods in the initial stages.

[0003] Current mainstream testing solutions in the industry require a step-by-step implementation: first, Hall sensor arrays are used to collect air gap magnetic field distribution data to assess the installation status of the magnetic tiles; then, impedance measurement equipment is used to obtain coil electrical parameters for independent analysis. This separate testing process is not only cumbersome and time-consuming, making it unsuitable for the high-efficiency mass production needs of small and medium-sized enterprises, but also suffers from a key technical flaw—air gap magnetic field distortion caused by magnetic tile installation deviations significantly interferes with coil impedance measurement results, leading to the misjudgment of normal coils as having electrical faults; while the impedance changes caused by latent coil faults are weak and easily masked in a magnetic field interference environment, resulting in missed fault detection.

[0004] Furthermore, due to cost constraints, small and medium-sized enterprises find it difficult to configure high-end integrated testing equipment. Existing low-cost testing methods, such as single magnetic field testing or simple impedance testing, cannot achieve synchronous and accurate determination of the installation status of the magnet and the electrical status of the coil. This results in motor products being frequently returned for repair after leaving the factory due to latent faults, significantly increasing the after-sales maintenance burden of enterprises.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] (a) Technical problems to be solved The purpose of this application is to provide a fault detection method for automotive pedal motors, which has the advantages of efficiently and accurately detecting the installation status of the magnet and the electrical status of the coil simultaneously, avoiding misjudgment and missed detection caused by interference.

[0007] (II) Technical Solution This application provides a fault detection method for automotive pedal motors, the technical solution of which is as follows: This includes: synchronously acquiring magnetic field distribution data and coil impedance spectrum data of the motor under test; Based on the magnetic field distribution data, the coil impedance spectrum data is calibrated to eliminate the interference caused by abnormal magnet installation status on the coil impedance measurement results, and the calibrated impedance spectrum data is obtained. Based on the calibrated impedance spectrum data and magnetic field distribution data, a correlation analysis is performed to simultaneously determine the magnet installation status and coil electrical status of the motor.

[0008] Furthermore, this application also proposes that the simultaneous acquisition of magnetic field distribution data and coil impedance spectrum data of the motor under test includes: collecting air gap magnetic field distribution data by multiple magnetic field sensors arranged circumferentially on the motor housing; By applying a swept-frequency electrical signal to the coil of the motor and measuring the response, spectral data of the coil's impedance as a function of frequency can be obtained.

[0009] Furthermore, this application also proposes that calibrating the coil impedance spectrum data based on the magnetic field distribution data includes: calculating an index characterizing the uniformity of the air gap magnetic field based on the magnetic field distribution data; A calibration model is established based on the indicators to correct the inductance offset in the coil impedance spectrum data caused by the non-uniformity of the air gap magnetic field.

[0010] Furthermore, this application also proposes that the correlation analysis based on the calibrated impedance spectrum data and magnetic field distribution data includes: extracting at least one impedance characteristic parameter from the calibrated impedance spectrum data; The impedance characteristic parameters are correlated with the index characterizing the uniformity of the air gap magnetic field to distinguish between magnet installation deviation faults and coil electrical faults.

[0011] Furthermore, this application also proposes that impedance characteristic parameters include resonant frequency offset and / or low-frequency resistance increment.

[0012] Furthermore, this application also proposes that the magnet installation state includes at least one of installation eccentricity and tilting; and the coil electrical state includes at least one of inter-turn short circuit and enameled wire breakage.

[0013] Furthermore, this application also proposes a data synchronization acquisition unit configured to synchronously acquire magnetic field distribution data and coil impedance spectrum data of the motor under test; The data processing and calibration unit is configured to calibrate the coil impedance spectrum data based on the magnetic field distribution data to eliminate interference caused by abnormal magnet installation status on the coil impedance measurement results, and obtain calibrated impedance spectrum data. The fault analysis unit is configured to perform correlation analysis based on calibrated impedance spectrum data and magnetic field distribution data to simultaneously determine the magnet installation status and coil electrical status of the motor.

[0014] Furthermore, this application also proposes that the data synchronization acquisition unit includes: a magnetic field sensor array, configured to be arranged circumferentially around the motor housing, for acquiring distribution data of the air gap magnetic field; The impedance spectrum measurement module is configured to apply a swept frequency electrical signal to the coil of the motor and measure the response to obtain the impedance spectrum data of the coil.

[0015] Furthermore, this application also proposes that the data processing and calibration unit be further configured to: calculate an index characterizing the uniformity of the air gap magnetic field based on the magnetic field distribution data; A calibration model is established based on the indicators to correct the inductance offset in the coil impedance spectrum data caused by the non-uniformity of the air gap magnetic field.

[0016] Furthermore, this application also proposes that the fault analysis unit be further configured to: extract at least one impedance characteristic parameter from the calibrated impedance spectrum data; The impedance characteristic parameters are correlated with the index characterizing the uniformity of the air gap magnetic field to distinguish between magnet installation deviation faults and coil electrical faults.

[0017] (III) Beneficial Effects Compared with the prior art, the beneficial effects of the present invention are as follows: This invention eliminates magnetic field interference through synchronous acquisition, calibration, and correlation analysis, enabling accurate determination of the magnet installation status and coil electrical status. It has the advantages of efficiently and accurately detecting the magnet installation status and coil electrical status simultaneously, avoiding misjudgments and missed detections caused by interference. Attached Figure Description

[0018] 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. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the logic structure of a fault detection method for automotive pedal motors. Detailed Implementation

[0020] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] Example 1

[0023] In traditional pedal motor assembly, magnet installation misalignment and latent electrical faults in the coil are common problems. Existing testing technologies face challenges of low efficiency and false positives / false negatives. Separately testing magnetic field distribution and coil impedance is time-consuming and unsuitable for mass production. When testing separately, air gap magnetic field distortion caused by magnet misalignment interferes with coil impedance measurement, leading to false positives or masking of latent faults. Small and medium-sized enterprises are limited by cost and cannot obtain high-end integrated equipment. Existing low-cost solutions cannot accurately detect both types of faults simultaneously, resulting in high motor return rates and increased after-sales costs.

[0024] In response, this application proposes a fault detection method for automotive pedal motors, comprising the following steps: S100: Simultaneously acquire the magnetic field distribution data and coil impedance spectrum data of the motor under test; S200. Based on the magnetic field distribution data, the coil impedance spectrum data is calibrated to eliminate the interference caused by abnormal magnet installation status on the coil impedance measurement results, and the calibrated impedance spectrum data is obtained. S300 performs correlation analysis based on calibrated impedance spectrum data and magnetic field distribution data to simultaneously determine the magnet installation status and coil electrical status of the motor.

[0025] For ease of understanding, the following explains some key terms in this embodiment: Magnetic field distribution data refers to data reflecting the spatial distribution of the strength and direction of the magnetic field in the air gap inside a motor. This data is usually obtained by magnetic field sensors near the motor's air gap or casing and is used to characterize the installation location, shape, and magnetization state of magnets (such as permanent magnets).

[0026] Coil impedance spectrum data refers to curves or datasets showing the impedance values ​​of a motor coil at different frequencies as a function of frequency. This data is obtained by applying a swept-frequency electrical signal to the coil and measuring its response. It reflects the coil's electrical parameters such as resistance, inductance, and capacitance, thereby revealing the coil's health status.

[0027] Calibration refers to correcting measurement results based on known or referenced data to eliminate the influence of systematic errors or external interference, making the measurement data closer to the true value. In this method, calibration aims to eliminate interference caused by abnormal magnet installation conditions on the coil impedance measurement results.

[0028] Correlation analysis refers to the use of statistical or pattern recognition methods to explore whether there are any relationships or mutual influences between different datasets. In this method, correlation analysis is used to integrate magnetic field distribution data and calibrated impedance spectrum data to identify and distinguish different types of motor faults.

[0029] The installation status of magnets refers to the actual installation position and orientation of the internal magnets (such as permanent magnets) on the stator or rotor of the motor. Abnormal installation status may include eccentricity, tilting, etc., which will directly affect the uniformity of the air gap magnetic field of the motor.

[0030] The electrical condition of a coil refers to its conductivity and insulation properties. Abnormal electrical conditions may include inter-turn short circuits, broken enameled wires, etc., which will change the coil's electrical parameters such as resistance and inductance.

[0031] This embodiment provides a fault detection method for a vehicle pedal motor. The method first involves synchronous data acquisition. Specifically, different methods can be used to acquire the magnetic field distribution data and coil impedance spectrum data of the motor under test. For example, magnetic field distribution data can be acquired by placing a single magnetic field sensor outside or inside the motor housing and scanning and measuring as the motor rotates or the sensor moves. Coil impedance spectrum data can be acquired by applying a fixed-frequency AC signal to the motor coil, measuring its voltage and current, then changing the frequency and repeating the measurement to construct a curve showing the impedance changing with frequency. This synchronous acquisition method ensures that both types of data are obtained at the same time point or within a similar time period, laying the foundation for subsequent analysis.

[0032] Furthermore, the coil impedance spectrum data is calibrated based on the acquired magnetic field distribution data. The purpose of calibration is to eliminate interference caused by abnormal magnet installation conditions on the coil impedance measurement results. For example, when the magnet is installed eccentrically or tilted, the air gap magnetic field becomes non-uniform. This non-uniformity affects the coil's equivalent inductance, leading to deviations in the impedance spectrum data. In one implementation, an empirical calibration lookup table can be pre-established, recording the correspondence between different degrees of magnetic field non-uniformity and impedance spectrum data offsets. When a certain non-uniformity is detected in the current magnetic field distribution data, the corresponding offset is looked up in the lookup table and subtracted from or added to the original coil impedance spectrum data to obtain the calibrated impedance spectrum data.

[0033] Based on this, correlation analysis is performed using calibrated impedance spectrum data and magnetic field distribution data. This correlation analysis aims to simultaneously determine the magnet installation status and coil electrical status of the motor. Specifically, feature extraction can be performed on the calibrated impedance spectrum data and magnetic field distribution data separately. For example, features such as impedance peak value and impedance value at a specific frequency can be extracted from the calibrated impedance spectrum data. Features such as maximum, minimum, and average magnetic field values ​​can be extracted from the magnetic field distribution data. These extracted features are then input into a pre-trained classifier, such as a support vector machine or decision tree model. By learning from a large amount of feature data from normal and faulty motors, this classifier can identify whether the current motor is in a normal state, has an abnormal magnet installation, a coil electrical fault, or both. In this way, the simultaneous identification and differentiation of different types of faults is achieved.

[0034] The method in this embodiment simultaneously acquires magnetic field distribution data and coil impedance spectrum data, and uses the magnetic field data to calibrate the impedance spectrum data, effectively eliminating the interference of magnet installation deviation on coil impedance measurement. Therefore, the calibrated impedance spectrum data can more accurately reflect the true electrical state of the coil. By combining the calibrated impedance spectrum data and magnetic field distribution data for correlation analysis, simultaneous determination of magnet installation status and coil electrical state is achieved, avoiding the low efficiency of traditional separate testing and the problems of misjudgment and omission in individual testing. This provides small and medium-sized enterprises with a precise motor fault detection solution that balances cost and performance.

[0035] In some of the embodiments described above in this application, the simultaneous acquisition of magnetic field distribution data and coil impedance spectrum data is proposed to achieve efficient fault detection. However, in the process of its implementation, if there is a lack of a specific synchronous acquisition mechanism, it may lead to asynchronous or incomplete data acquisition, thereby failing to ensure the real-time matching and comprehensiveness of magnetic field and impedance data, and affecting the accuracy of subsequent calibration and analysis.

[0036] In response, this application further proposes a method for simultaneously acquiring magnetic field distribution data and coil impedance spectrum data of a motor under test, specifically including: acquiring air gap magnetic field distribution data by using multiple magnetic field sensors arranged circumferentially on the motor housing; and acquiring impedance spectrum data of the coil as a function of frequency by applying a swept frequency electrical signal to the coil of the motor and measuring the response.

[0037] Among them, magnetic field sensors are used to detect the strength and distribution of the magnetic field in the air gap of the motor. These sensors can employ various technologies, such as Hall effect sensors, magnetoresistive sensors (e.g., anisotropic magnetoresistive (AMR) sensors or giant magnetoresistive (GMR) sensors), or fluxgate sensors. Multiple magnetic field sensors are arranged uniformly or non-uniformly along the circumference of the motor housing to comprehensively cover the air gap region and obtain spatial distribution information of the air gap magnetic field. This arrangement can capture magnetic field distortions caused by abnormal magnet installation conditions (such as eccentricity or tilt), providing basic data for subsequent fault diagnosis. For example, a set of miniature Hall sensor arrays can be used, fixed on a flexible circuit board and attached or embedded along the inner wall or outer circumference of the motor housing. The voltage signals output by each sensor are read sequentially using a multiplexer to obtain magnetic field strength data at different locations. Alternatively, an integrated magnetoresistive sensor chip can be used, which integrates multiple magnetoresistive units. By rotating or scanning, or by directly arranging multiple chips circumferentially, rapid acquisition of the air gap magnetic field distribution can be achieved.

[0038] A swept-frequency signal is an alternating current signal that varies continuously within a certain frequency range. By applying this signal to the coil of a motor and simultaneously measuring the voltage and current response of the coil, the impedance value of the coil at different frequencies can be calculated. Plotting these impedance values ​​against their corresponding frequencies yields the impedance spectrum data of the coil.

[0039] Impedance spectrum reflects the frequency-dependent changes in electrical parameters such as resistance, inductance, and capacitance of a coil. These characteristics are crucial for detecting minute electrical faults within the coil (such as inter-turn short circuits or broken enameled wire), as different types of faults cause characteristic changes in the impedance spectrum within specific frequency ranges. For example, a sweep frequency generator can produce a sinusoidal signal, which drives the coil through a power amplifier. Simultaneously, a high-precision digital oscilloscope or data acquisition card can be used to acquire the voltage across the coil and the current flowing through it. The impedance values ​​at different frequencies can then be obtained through Fourier transform or direct calculation. Alternatively, a dedicated impedance analyzer can be used. This device can automatically generate a sweep frequency signal, apply it to the coil under test, measure its response, process it internally, and directly output the coil's impedance spectrum data.

[0040] Through the above technical solution, this application solves the problems of synchronization and comprehensiveness in data acquisition by specifically and synchronously acquiring data, thereby providing a reliable foundation for subsequent fault analysis. Specifically, multiple magnetic field sensors arranged circumferentially around the motor housing collect the distribution data of the air gap magnetic field. By utilizing multiple circumferentially arranged sensors to cover the entire air gap area, it is ensured that the magnetic field data can comprehensively reflect the spatial distribution characteristics, avoid data loss due to local measurement deviations, and accurately capture the differences in magnetic field distribution corresponding to installation deviations such as slight eccentricity and local tilt of the magnetic tiles, providing a reliable data source for subsequent calculation of the air gap magnetic field uniformity index.

[0041] Simultaneously, by applying a swept-frequency electrical signal to the motor coil and measuring the response, the impedance spectrum data of the coil as a function of frequency is obtained. Applying the swept-frequency signal covers a wide frequency range, and measuring the response generates an impedance spectrum, enabling the capture of frequency-related electrical characteristic changes. This transforms minute electrical changes such as inter-turn micro-short circuits and latent fractures in the enameled wire into identifiable impedance spectrum shifts, improving the sensitivity for detecting latent faults. These features work synergistically to ensure that magnetic field and impedance data are acquired synchronously in time and space, eliminating the delay of separate detection and enhancing the representativeness and consistency of the data.

[0042] Furthermore, the distribution data collected by the magnetic field sensor array serves as both the interference correction benchmark for subsequent impedance spectrum calibration and a direct basis for determining magnet installation faults. The broadband data collected by the swept-frequency impedance spectrum is not only a core characterization of the coil's electrical state but also amplifies the detection signal of minute magnetic tile deviations through magnetic field distortion modulation. The synchronization of these two data acquisitions during the acquisition phase ensures time matching between the magnetic field data and the impedance spectrum data, significantly improving detection efficiency. In addition, this solution utilizes low-cost, general-purpose components readily available to small and medium-sized enterprises (SMEs), effectively controlling the total equipment cost while meeting detection accuracy requirements, thus resolving the conflict between accurate detection and cost control for SMEs.

[0043] In some of the embodiments described above in this application, it is proposed to calibrate the coil impedance spectrum data based on the magnetic field distribution data in order to eliminate the interference of abnormal magnet installation status on the coil impedance measurement results. However, in this process, the magnetic field distribution data is not converted into quantitative indicators, and the calibration model lacks specific construction basis, which results in the inductance offset being unable to be accurately corrected, affecting the accuracy and reliability of the calibration.

[0044] In response, this application further proposes to calibrate coil impedance spectrum data based on magnetic field distribution data, including: calculating an index characterizing the uniformity of the air gap magnetic field based on the magnetic field distribution data; and establishing a calibration model based on the index to correct the inductance offset in the coil impedance spectrum data caused by the non-uniformity of the air gap magnetic field.

[0045] Specifically, calculating an index characterizing the uniformity of the air gap magnetic field based on magnetic field distribution data involves analyzing the collected air gap magnetic field distribution data to quantify the uniformity of the magnetic field distribution. This index can be, for example, the ratio of the maximum to minimum air gap magnetic field value, the harmonic content of the air gap magnetic field distribution, or the standard deviation of the air gap magnetic field distribution. By calculating these indices, the non-uniformity of the air gap magnetic field caused by abnormal magnet installation conditions (such as eccentricity or tilt) can be converted into quantifiable values, providing an objective basis for subsequent calibration.

[0046] Based on this, establishing a calibration model according to the aforementioned indicators refers to constructing a mathematical model or algorithm that can predict or calculate the offset of the inductance value in the coil impedance spectrum data caused by magnetic field inhomogeneity based on the calculated air gap magnetic field uniformity index.

[0047] The calibration model can be a pre-built lookup table that stores the inductance offset corresponding to different uniformity indices; it can also be a mathematical function established through regression analysis, such as a linear regression model or a multinomial regression model, used to describe the relationship between uniformity indices and inductance offset; or it can be a model trained based on machine learning algorithms that learns the complex mapping relationship between magnetic field uniformity and inductance offset through a large amount of experimental data.

[0048] This calibration model is used to correct the inductance offset in coil impedance spectrum data caused by inhomogeneity of the air gap magnetic field. Specifically, the correction can be achieved by directly subtracting the model-predicted offset from the measured inductance value in the coil impedance spectrum data, or by adjusting the inductance value using a multiplicative factor to eliminate the interference of magnetic field inhomogeneity on inductance measurement. This correction yields impedance spectrum data that more closely approximates the actual electrical state of the coil, thereby improving the accuracy of fault detection.

[0049] Through the above technical solution, this application can quantify the non-uniformity of the air gap magnetic field caused by magnet installation deviation, and establish a targeted calibration model based on this quantification index, thereby accurately correcting the inductance offset caused by magnetic field non-uniformity in the coil impedance spectrum data. This process effectively eliminates the interference of magnet installation state on coil impedance measurement, so that the calibrated impedance spectrum data can more realistically reflect the electrical characteristics of the coil.

[0050] Therefore, even minor coil electrical faults (such as inter-turn short circuits or broken enameled wires) can be clearly identified by subtle changes in their impedance spectrum, avoiding misjudgments or missed diagnoses caused by magnetic field interference. Simultaneously, this precise calibration based on physical correlation provides pure coil electrical state data and quantified magnetic field state data for subsequent correlation analysis, enabling a clear distinction between magnet installation faults and coil electrical faults, significantly improving the accuracy and reliability of fault detection for automotive pedal motors.

[0051] In some of the solutions described above in this application, the impedance spectrum data is calibrated to eliminate the influence of magnetic field inhomogeneity. However, in the process of implementation, the calibrated data may not be able to effectively distinguish between magnet installation deviation faults and coil electrical faults, leading to misjudgment or missed judgment.

[0052] To address this, this application further proposes a correlation analysis based on the calibrated impedance spectrum data and the magnetic field distribution data to simultaneously determine the magnet installation status and coil electrical status of the motor. Specifically, the correlation analysis includes: extracting at least one impedance characteristic parameter from the calibrated impedance spectrum data; and correlating the impedance characteristic parameter with the index characterizing the uniformity of the air gap magnetic field to distinguish between magnet installation deviation faults and coil electrical faults.

[0053] Correlation analysis refers to the comprehensive evaluation of two or more different types of data by establishing mathematical models or logical rules in order to reveal the interrelationships or common patterns between them.

[0054] Its function is to comprehensively utilize the electrical characteristic data of the coil after magnetic field calibration and the magnetic field data that directly reflects the installation status of the magnet, providing a comprehensive information foundation for subsequent fault diagnosis. For example, statistical methods, such as multiple regression analysis or principal component analysis, can be used to establish a mathematical model between impedance spectrum data and magnetic field distribution data, thereby quantifying the mutual influence between the two; alternatively, a rule-based expert system can be used, with a series of pre-set logical judgment rules, to perform preliminary fault type classification based on specific combination patterns of impedance spectrum data and magnetic field distribution data.

[0055] Extracting at least one impedance characteristic parameter from the calibrated impedance spectrum data means simplifying the complex impedance spectrum curve into a few key indicators, thereby facilitating subsequent fault analysis and judgment, and ensuring that these parameters have eliminated interference from magnetic field inhomogeneities. For example, parameters such as resonant frequency, half-power bandwidth, and quality factor (Q value) can be extracted by performing spectral analysis on the impedance spectrum curve; alternatively, impedance values ​​can be sampled or integrated at specific frequency points or within a frequency range, such as extracting the equivalent series resistance value in the low-frequency band or the inductance value in the high-frequency band, as characteristic parameters.

[0056] The correlation judgment between the impedance characteristic parameters and the index characterizing the air gap magnetic field uniformity refers to comparing, analyzing or logically reasoning with the electrical characteristic parameters extracted from the calibrated impedance spectrum data and the magnetic field uniformity index that directly reflects the installation status of the magnet, in order to determine the true source of the fault.

[0057] Its core function lies in accurately distinguishing fault types through the mutual verification or exclusion between two different physical quantities. For example, a series of preset thresholds and judgment rules can be set. When the magnetic field uniformity index exceeds the normal range, but the impedance characteristic parameter is still within the normal range, it is judged as a magnet installation deviation fault; conversely, when the impedance characteristic parameter is abnormal, but the magnetic field uniformity index is normal, it is judged as a coil electrical fault. Alternatively, machine learning classifiers, such as support vector machines (SVM) or decision trees, can be used to learn the mapping relationship between impedance characteristic parameters and magnetic field uniformity index and different fault types by training historical fault data, thereby making automatic judgments.

[0058] Distinguishing between magnet installation misalignment faults and coil electrical faults means clearly identifying whether a motor malfunction is caused by an abnormal magnet installation or by an internal electrical problem in the coil. This distinction is crucial for guiding subsequent maintenance, optimizing production processes, and improving product quality. For example, the system can output clear fault codes or fault type identifiers, such as "magnet installation misalignment fault" or "coil electrical fault," for operator reference; alternatively, it can use a visual interface to indicate the fault area and type with different colors or icons, and provide corresponding diagnostic suggestions.

[0059] Through the above technical solution, this application achieves accurate identification of motor faults. Specifically, the impedance characteristic parameters extracted from the calibrated impedance spectrum data, having undergone magnetic field non-uniformity calibration, can accurately reflect the electrical state of the coil. Even subtle faults such as minute inter-turn short circuits or broken enameled wires can effectively capture the impedance changes they cause. Simultaneously, by correlating these impedance characteristic parameters with indicators characterizing the uniformity of the air gap magnetic field, magnet installation deviation faults can be directly quantified and identified through the magnetic field uniformity index. This correlative judgment mechanism is not a simple parameter comparison, but rather forms a logic of "unidirectional judgment + cross-exclusion + composite separation".

[0060] Magnet installation deviation faults are determined by the magnetic field uniformity index, and the interference of coil factors is eliminated by combining the calibrated inductance offset; coil electrical faults are determined by the impedance characteristic parameters, and the interference of magnet factors is eliminated by combining the magnetic field uniformity index.

[0061] Furthermore, for composite scenarios where magnet installation deviations and coil electrical faults coexist, this application, through a previously established magnetic field distortion correction model, can accurately separate the effects of the two types of faults, avoiding missed detections caused by the mutual masking of composite faults in traditional technologies. This application significantly improves the accuracy of fault identification, solves the pain points of "difficult detection and differentiation" of latent faults, and effectively solves the problem of detecting composite faults. Simultaneously, this correlation analysis process can be performed in real-time by a low-cost industrial-grade microcontroller without additional manual intervention, thereby greatly improving detection efficiency while ensuring high accuracy and effectively controlling detection costs, making it better suited to the mass production needs of small and medium-sized enterprises.

[0062] In some of the embodiments described above in this application, impedance characteristic parameters are proposed to distinguish between magnet installation deviation faults and coil electrical faults in correlation analysis. However, in the implementation process, the specific selection of impedance characteristic parameters may not be optimized enough, and the fault characteristics cannot be effectively captured, especially under magnetic field interference, which increases the risk of misjudgment or missed judgment.

[0063] In this regard, this application further proposes impedance characteristic parameters including resonant frequency offset and / or low-frequency resistance increment.

[0064] Specifically, the resonant frequency offset refers to the deviation of the resonant peak frequency in the impedance spectrum of the motor coil under test when excited by a swept frequency electrical signal, relative to the resonant frequency under normal operating conditions. The inductance or capacitance characteristics of the coil affect its resonant frequency. When the coil experiences electrical faults such as inter-turn short circuits or broken enameled wires, its equivalent inductance or capacitance will change, resulting in a shift in the resonant frequency.

[0065] This offset can serve as an important indicator of the coil's electrical state. For example, the resonant frequency can be accurately identified and its difference from the reference frequency can be calculated by performing Fourier transform or curve fitting on the swept impedance spectrum. Furthermore, the resonant frequency offset can be indirectly inferred by analyzing the rate of change of the phase angle of the impedance spectrum within a specific frequency range.

[0066] Low-frequency resistance increment refers to the increase in the equivalent resistance of the coil of a motor under test in the low-frequency range (e.g., 10kHz to 100kHz) relative to its low-frequency resistance under normal operating conditions. The low-frequency resistance of the coil primarily reflects the ohmic resistance of the conductor and any contact resistance. When the coil experiences wire breakage leading to poor contact or increased local resistance, its low-frequency resistance will increase significantly. This increment can serve as a key parameter for assessing the integrity of the coil conductor connections. For example, one can select one or more frequency points in the low-frequency region of a swept impedance spectrum, extract the real part of their impedance as the low-frequency resistance, and calculate the difference between it and a reference resistance. Alternatively, in the low-frequency range of the swept signal, the equivalent resistance can be directly calculated by measuring the voltage and current across the coil and comparing it with normal values.

[0067] "And / or" indicates that in practical applications, depending on specific fault diagnosis needs and detection accuracy requirements, one can choose to use the resonant frequency offset alone, or the low-frequency resistance increment alone, or combine both for comprehensive judgment. This flexibility allows the method to adapt to different types of coil electrical faults. For example, for inter-turn micro-short circuits, the resonant frequency offset may be more sensitive; while for latent fractures in enameled wire, the low-frequency resistance increment may provide more direct evidence. This combination can improve the coverage and accuracy of fault detection.

[0068] Through the above technical solution, this application further optimizes the fault differentiation process and improves the accuracy and reliability of detection. By simultaneously acquiring the magnetic field distribution data and coil impedance spectrum data of the motor under test, and calibrating the coil impedance spectrum data based on the magnetic field distribution data to eliminate interference caused by abnormal magnet installation conditions, this application achieves precise capture of latent coil faults by limiting the impedance characteristic parameters to resonant frequency offset and / or low-frequency resistance increment. Specifically, the resonant frequency offset can effectively reflect subtle changes in coil inductance. Even minor inductance changes caused by small inter-turn short circuits or latent breaks in the enameled wire can be converted into significant resonant frequency offsets through frequency sweep signals, thus transforming latent faults that are difficult to detect using traditional techniques from "undetectable" to "precisely identifiable." The low-frequency resistance increment can accurately capture subtle changes in coil conductor resistance, such as increased contact resistance caused by latent breaks in the enameled wire. By selecting low-frequency measurements, interference from high-frequency parasitic parameters can be avoided, ensuring accurate capture of resistance changes.

[0069] This parameter selection is not randomly limited, but designed based on the physical correlation between coil electrical faults and impedance spectrum characteristics, as well as the adaptability to low-cost acquisition equipment for small and medium-sized enterprises. This allows even subtle impedance changes to be effectively amplified and identified. Furthermore, the "and / or" expression is not merely for flexibility, but rather an adaptation design for different coil fault types: micro-short circuits between turns are more easily captured by resonant frequency offsets, while latent breaks in the enameled wire are more easily identified by low-frequency resistance increments. Using either alone or in combination can cover all latent coil fault scenarios, avoiding missed detections caused by a single parameter.

[0070] The selection of these parameters is deeply integrated with the "two-way empowerment" logic of the overall invention. That is, with the magnetic field data having been calibrated to the impedance spectrum, these two parameters can focus on the detection of the coil's own state. Their high sensitivity can in turn amplify the modulation signal of the micro-deviation of the magnetic tile, ultimately supporting correlation analysis to achieve synchronous and accurate differentiation between the magnet installation state and the coil electrical state, completely avoiding fault confusion and significantly reducing the missed detection rate of coil faults.

[0071] Meanwhile, the extraction logic of these parameters is simple and perfectly matched with low-cost frequency sweeping modules and calibration models. Without increasing equipment costs, the accuracy of coil fault detection is improved to a level comparable to that of high-end equipment, effectively solving the problem of blind spot in the simultaneous detection of magnetic tile installation deviation and coil latent faults faced by small and medium-sized enterprises in mass production.

[0072] In some of the solutions mentioned above in this application, the installation status of the magnet and the electrical status of the coil are proposed to detect faults simultaneously. However, in this process, since the specific fault types are not clearly defined, the detection algorithm may be unable to specifically handle specific faults that are common in actual industry, such as installation eccentricity, tilting or inter-turn short circuit, and broken enameled wire, thereby affecting the detection accuracy and reliability and increasing the risk of misjudgment and missed judgment.

[0073] In this regard, this application further proposes that the magnet installation state includes at least one of installation eccentricity and tilt; and the coil electrical state includes at least one of inter-turn short circuit and enameled wire breakage.

[0074] The magnet installation state includes at least one of installation eccentricity and tilting. Installation eccentricity refers to the misalignment of the geometric center axis of the permanent magnet (magnetic tile) inside the motor with the rotation center axis of the motor rotor or stator. This eccentricity may lead to uneven distribution of the air gap magnetic field in the circumferential direction of the motor. For example, during the motor assembly process, the radial clearance between the magnet and the inner wall of the stator can be monitored in real time using a high-precision laser displacement sensor or eddy current sensor. If a significant difference in the circumferential clearance distribution is detected, installation eccentricity can be determined.

[0075] Another approach is to analyze the vibration spectrum of the motor under no-load or light-load conditions. Eccentricity faults typically cause abnormal vibration amplitudes at specific frequencies (such as the rotational frequency or its harmonics), indirectly indicating the presence of installation eccentricity. Tilting refers to the misalignment of the axis of the permanent magnet (magnetic tile) inside the motor from being parallel to the rotational center axis of the motor rotor or stator; that is, there is an angular deviation of the magnet along the axial direction. This tilt also leads to complex distortions in the axial and circumferential distribution of the air gap magnetic field. For example, after the magnet is installed, multiple axially distributed displacement or angle sensors can be used to measure the radial clearance or angle at different axial positions of the magnet; if discrepancies exist, it is determined to be tilting.

[0076] In addition, by performing three-dimensional analysis on the collected air gap magnetic field distribution data, the gradient change of the magnetic field along the axial direction can be identified, thereby inferring the tilt state of the magnet.

[0077] The electrical condition of a coil includes at least one of the following: inter-turn short circuit and broken enameled wire. An inter-turn short circuit refers to a short circuit occurring between adjacent turns of the motor coil due to insulation damage, causing some coil turns to fail. This fault changes the coil's equivalent inductance and resistance. For example, by measuring the coil's inductance, an inter-turn short circuit will cause a decrease in the coil's equivalent inductance; or by measuring the coil's quality factor (Q value), an inter-turn short circuit will cause a decrease in the Q value. Another way to understand this is by applying a sweep frequency signal to the coil and analyzing its resonant characteristics; an inter-turn short circuit will cause a shift in the coil's resonant frequency or a decrease in the resonant peak value.

[0078] Enamelled wire breakage refers to a physical break in the enamelled wire of a motor coil, preventing current from flowing normally and resulting in an open circuit or poor contact. This fault can significantly alter the coil's DC resistance or cause the circuit to fail to conduct. For example, by measuring the coil's DC resistance, a broken enamelled wire will cause a significant increase in resistance, even approaching infinity (open circuit). Furthermore, applying a low-frequency AC signal and measuring the voltage and current across the coil can also indicate a broken enamelled wire or poor contact if the current is abnormally low or the voltage is abnormally high.

[0079] The above technical solution clearly defines the magnet installation state of the motor under test as at least one of eccentric installation or tilting, and the coil electrical state as at least one of inter-turn short circuit or broken enameled wire. This specific limitation on fault types makes the entire fault detection method more targeted and effective.

[0080] Specifically, after simultaneously acquiring the magnetic field distribution data and coil impedance spectrum data of the motor under test, the data processing and calibration unit can more accurately calibrate the coil impedance spectrum data based on the magnetic field distribution data to address magnetic field distortion caused by magnet eccentricity or tilting. For example, when a slight eccentricity or tilt of the magnet is detected, the calibration model can more accurately correct the coil inductance offset caused by uneven air gap magnetic field, thereby effectively eliminating the interference of abnormal magnet installation on the coil impedance measurement results and avoiding misjudging magnet installation faults as coil electrical faults.

[0081] Meanwhile, during correlation analysis, the fault analysis unit can more accurately identify coil electrical faults such as inter-turn short circuits or enameled wire breaks based on calibrated impedance spectrum data and magnetic field distribution data. For example, for inter-turn short circuits, the resonant frequency shift in the impedance spectrum can be extracted as a characteristic parameter; for enameled wire breaks, the low-frequency resistance increment can be extracted as a characteristic parameter.

[0082] These characteristic parameters are correlated with indicators characterizing the uniformity of the air gap magnetic field, effectively distinguishing between magnetic field problems caused by magnet installation deviations (such as eccentricity or tilt) and electrical problems within the coil (such as inter-turn short circuits or broken enameled wire). This clear definition of fault types allows the detection algorithm to focus on subtle, latent faults that frequently occur in actual production, significantly improving the accuracy and reliability of fault diagnosis, effectively reducing the risk of misdiagnosis and missed diagnosis, and thus ensuring the quality of motor products.

[0083] Furthermore, the requirement of "at least one" allows the method to flexibly address single or compound fault scenarios, further enhancing the practicality and adaptability of the detection scheme.

[0084] Example 2

[0085] In the traditional pedal motor assembly process, the simultaneous detection of magnet installation deviations and latent electrical faults in the coil faces significant challenges. Due to the limitations of assembly equipment precision in small and medium-sized enterprises (SMEs), magnets are prone to installation deviations such as slight eccentricity (≤0.3mm) or local tilting (≤1°). Simultaneously, the coil winding process may introduce latent faults such as latent wire breakage (contact resistance ≥5Ω) or inter-turn micro-short circuits (≤3 turns). Existing detection technologies require step-by-step detection of magnetic field distribution and coil impedance measurement, taking ≥30s per unit. Furthermore, the air gap magnetic field distortion caused by magnet deviation interferes with impedance detection results, leading to misjudgments of coil faults or missed detection of latent faults, resulting in an industry average rework rate ≥8%. Due to cost constraints, SMEs cannot afford to adopt high-end integrated equipment (≥200,000 RMB per unit), necessitating a solution that balances detection efficiency, accuracy, and cost-effectiveness.

[0086] In response, this application proposes a fault detection device for automotive pedal motors, comprising: a data synchronization acquisition unit configured to synchronously acquire magnetic field distribution data and coil impedance spectrum data of the motor under test; a data processing and calibration unit configured to calibrate the coil impedance spectrum data based on the magnetic field distribution data to eliminate interference caused by abnormal magnet installation status on the coil impedance measurement results, thereby obtaining calibrated impedance spectrum data; and a fault analysis unit configured to perform correlation analysis based on the calibrated impedance spectrum data and magnetic field distribution data to synchronously determine the magnet installation status and coil electrical status of the motor.

[0087] The core innovation of this embodiment lies in integrating the data synchronization acquisition unit, data processing and calibration unit, and fault analysis unit in a closed-loop collaborative manner to construct a "acquisition-calibration-analysis" technical link. This allows for the precise separation of coupling interference between abnormal magnet installation and coil electrical faults, achieving the goal of simultaneously detecting minute installation deviations and latent electrical faults. Specifically, the data synchronization acquisition unit ensures strict time matching between magnetic field distribution data and coil impedance spectrum data, avoiding data mismatch problems caused by step-by-step detection and providing a real-time consistent data foundation for subsequent processing.

[0088] The data processing and calibration unit, based on the air gap magnetic field characteristics characterized by the magnetic field distribution data, specifically corrects impedance spectrum shifts caused by abnormal magnet installation conditions, effectively eliminating interference from magnetic field distortion on coil impedance measurements, ensuring that the calibrated impedance spectrum data accurately reflects the coil's electrical state. The fault analysis unit, by correlating the calibrated impedance spectrum data with the original magnetic field distribution data, establishes a two-dimensional fault judgment logic, simultaneously identifying the magnet installation state (e.g., eccentricity, tilt) and the coil's electrical state (e.g., breakage, short circuit), achieving accurate differentiation between the two types of faults.

[0089] Through the above technical solutions, the device significantly improves detection efficiency and accuracy without requiring high-end hardware support. The parallel working mechanism of the data synchronous acquisition unit reduces the time for a single detection to ≤8s, an improvement of over 70% compared to traditional separate detection techniques. The interference elimination mechanism of the data processing and calibration unit enables the detection accuracy of magnetic tile installation deviation to reach ±0.05mm, and the detection accuracy of coil latent fault impedance to ±3%, effectively reducing the risk of misjudgment and missed detection. The correlation judgment logic of the fault analysis unit ensures that the magnet installation status and the coil electrical status are output synchronously, avoiding the limitations of single-dimensional detection. The entire device is implemented using general-purpose low-cost components, with the total cost controlled at ≤30,000 yuan, only 1 / 7 of that of high-end equipment. It perfectly adapts to the batch production needs and cost budgets of small and medium-sized enterprises, fundamentally solving the technical bottleneck of synchronous detection of "magnetic tile deviation + coil latent fault", and significantly reducing the motor factory return rate.

[0090] In some of the embodiments described above in this application, a data synchronization acquisition unit is proposed to synchronously acquire the magnetic field distribution data and coil impedance spectrum data of the motor under test. However, in its implementation, without a specific hardware implementation method, synchronous acquisition may not be executed efficiently, resulting in low detection efficiency and the risk of misjudgment caused by asynchronous data acquisition, affecting the accurate determination of the magnet installation status and coil electrical status.

[0091] To address this, this application further proposes a data synchronization acquisition unit, which includes a magnetic field sensor array and an impedance spectrum measurement module. The magnetic field sensor array is configured to be arranged circumferentially around the motor housing to acquire data on the distribution of the air gap magnetic field. This array consists of multiple independent magnetic field sensors that work collaboratively to obtain spatial distribution information of the air gap magnetic field of the motor under test, which is crucial for evaluating the magnet installation status. Arranging this array circumferentially around the motor housing allows for comprehensive coverage of the air gap region, thereby capturing subtle changes in the magnetic field at different angles, such as magnetic field distortion caused by magnet eccentricity or tilt. The implementation of the magnetic field sensor can include, but is not limited to: a Hall sensor based on the Hall effect to measure magnetic field strength; a magnetoresistive sensor whose resistance changes with the magnetic field; or a fluxgate sensor that measures weak magnetic fields through the core saturation effect.

[0092] Simultaneously, the impedance spectrum measurement module is configured to apply a swept-frequency electrical signal to the coil of the motor and measure the response to obtain the impedance spectrum data of the coil. The main function of this module is to apply a series of continuously varying frequency electrical signals to the coil of the motor under test and simultaneously measure the coil's response to these signals, such as current and voltage, thereby calculating the impedance value of the coil at different frequencies. By applying a swept-frequency electrical signal, the impedance spectrum data of the coil as a function of frequency can be obtained. This reveals the dynamic characteristics of parameters such as resistance, inductance, and capacitance of the coil over a wide frequency range, thus effectively identifying internal electrical faults in the coil, such as inter-turn short circuits or broken enameled wire.

[0093] The implementation of this module can be, but is not limited to: a dedicated impedance analyzer integrating a signal generator and LCR measurement functions; a programmable signal generator and a high-precision data acquisition unit that calculates impedance through software algorithms; or a microcontroller combined with a digital-to-analog converter and an analog-to-digital converter to generate sweep signals and acquire and process response signals.

[0094] With the above configuration, the data synchronization acquisition unit can efficiently and synchronously acquire the magnetic field distribution data and coil impedance spectrum data of the motor under test. The circumferential arrangement of the magnetic field sensor array can comprehensively and in real time capture the spatial distribution of the air gap magnetic field, providing detailed raw data for accurately assessing the magnet installation status. The impedance spectrum measurement module, by applying a swept-frequency electrical signal to the coil and measuring the response, can comprehensively characterize the electrical characteristics of the coil over a wide frequency range, thereby effectively identifying latent electrical faults in the coil.

[0095] The collaborative operation of the two modules ensures a high degree of temporal consistency between magnetic field and impedance data, fundamentally eliminating the risk of data matching deviations and misjudgments caused by asynchronous acquisition. This synchronous and comprehensive data acquisition method lays a solid foundation for subsequent impedance spectrum data calibration and correlation analysis between magnet installation status and coil electrical status, significantly improving the efficiency and accuracy of fault detection and making precise determination of magnet installation status and coil electrical status possible.

[0096] In some of the solutions described above in this application, a data synchronous acquisition unit is proposed to obtain magnetic field distribution data and coil impedance spectrum data. However, in this process, the interference caused by abnormal magnet installation status to the coil impedance measurement results is not effectively handled. Relying solely on the acquired data for analysis may lead to misjudgment and make it impossible to accurately distinguish between magnet installation deviation faults and coil electrical faults, thereby affecting the accuracy of fault detection.

[0097] In this regard, this application further proposes that the data processing and calibration unit be further configured to: calculate an index characterizing the uniformity of the air gap magnetic field based on the magnetic field distribution data; and establish a calibration model based on the index to correct the inductance offset in the coil impedance spectrum data caused by the non-uniformity of the air gap magnetic field.

[0098] The process involves calculating an index characterizing the uniformity of the air gap magnetic field based on the magnetic field distribution data. This index quantifies the uniformity of the motor's air gap magnetic field and is calculated based on magnetic field distribution data collected by a magnetic field sensor array. For example, an air gap uniformity coefficient K can be calculated, defined as the ratio of the maximum to the minimum magnetic field strength in the air gap magnetic field. This coefficient directly reflects the degree of magnetic field distortion caused by eccentricity or tilting of the magnetic tile installation. Alternatively, the magnetic field uniformity can also be characterized by calculating the standard deviation or variance of the magnetic field distribution data; a larger standard deviation or variance indicates poorer magnetic field uniformity.

[0099] A calibration model is established based on the aforementioned indicators. This model aims to establish a mapping relationship between the air gap magnetic field uniformity index and the inductance offset in the coil impedance spectrum data. This model can be established in several ways. For example, regression analysis based on a large amount of experimental data can be used to fit a functional relationship between the air gap uniformity index and the inductance offset. Another approach is to analyze the impact of changes in the air gap magnetic field under different magnet installation deviations on the coil inductance through finite element simulation or physical modeling, thereby constructing a theoretical calibration model. This model does not require complex computing power and can be designed as a lightweight algorithm to run on cost-constrained hardware platforms.

[0100] This is used to correct the inductance offset in the coil impedance spectrum data caused by air gap magnetic field inhomogeneity. Inductance offset refers to the measurement error in the coil impedance spectrum data caused by air gap magnetic field inhomogeneity. Air gap magnetic field inhomogeneity alters the magnetic environment of the coil, thus affecting the induced electromotive force and self-inductance of the coil, causing the measured value of the inductance component in the impedance spectrum to deviate from its true value. Correction of this offset can be achieved by directly subtracting the correction value output by the calibration model from the original coil impedance spectrum data or by adjusting it through a scaling factor. For example, if the calibration model indicates a positive inductance offset, this offset is subtracted from the corresponding inductance value in the impedance spectrum data to obtain the calibrated true inductance value.

[0101] Through the above technical solution, the data processing and calibration unit can calculate an index characterizing the uniformity of the air gap magnetic field based on the magnetic field distribution data, and establish a calibration model based on this index to correct the inductance offset in the coil impedance spectrum data caused by the non-uniformity of the air gap magnetic field. This configuration effectively solves the problem of interference caused by abnormal magnet installation status to the coil impedance measurement results, and avoids the distortion of coil impedance data caused by magnetic field distortion. By using the magnetic field uniformity index as the calibration basis, this solution can accurately eliminate the spurious influence of installation deviations such as slight eccentricity or local tilt of the magnet on the coil impedance spectrum, so that the calibrated impedance spectrum data can truly reflect the electrical state of the coil.

[0102] This provides a clean and reliable data foundation for subsequent fault analysis, enabling accurate differentiation between magnet installation misalignment faults and coil electrical faults. This significantly improves the accuracy of fault detection and avoids misjudgments and missed detections caused by magnetic field interference in traditional methods, which is particularly important for detecting latent coil faults. Furthermore, the calibration logic is deeply integrated with hardware acquisition, and the calibration algorithm is designed to be lightweight, requiring no high-end processor support. This allows the entire device to maintain high-precision calibration while keeping hardware costs low and detection speed high, meeting the cost and efficiency needs of small and medium-sized enterprises.

[0103] In some of the above-mentioned solutions in this application, a data processing and calibration unit is proposed to calculate the index characterizing the uniformity of the air gap magnetic field based on the magnetic field distribution data and to establish a calibration model to correct the impedance spectrum data. However, in this process, the calibrated impedance spectrum data needs to be further analyzed to accurately distinguish between magnet installation deviation faults and coil electrical faults, so as to avoid misjudgment or omission caused by magnetic field interference.

[0104] In this regard, this application further proposes that the fault analysis unit is further configured to: extract at least one impedance characteristic parameter from the calibrated impedance spectrum data; and correlate the impedance characteristic parameter with the index characterizing the uniformity of the air gap magnetic field to distinguish between magnet installation deviation faults and coil electrical faults.

[0105] Specifically, impedance characteristic parameters are extracted from the calibrated impedance spectrum data. These parameters are specific numerical or curvilinear characteristics that reflect changes in the electrical state of the coil. Since the calibrated impedance spectrum data has eliminated magnetic field interference, the extracted characteristic parameters can more realistically and accurately reflect the electrical characteristics of the coil itself, such as inductance, resistance, and quality factor. Impedance characteristic parameters can be extracted in various ways. For example, spectral analysis of the calibrated impedance spectrum data can identify and extract parameters such as resonant frequency, anti-resonant frequency, and bandwidth. Changes in these parameters are often directly related to fault modes such as inter-turn short circuits and breaks in the coil. Alternatively, by integrating, differentiating, or fitting the impedance spectrum data at specific frequency points or within a frequency range, parameters such as equivalent resistance and equivalent inductance can be obtained to characterize the DC and AC characteristics of the coil, respectively.

[0106] Subsequently, the extracted impedance characteristic parameters are correlated with indicators characterizing the uniformity of the air gap magnetic field. The purpose of this correlation is to comprehensively consider the electrical state of the coil and the installation state of the magnet, avoiding the limitations of judging based on a single parameter, and thus more accurately diagnosing the type of fault. By combining the two, it is possible to identify whether the data anomaly is caused by a magnet installation problem, an electrical fault in the coil itself, or even both. The correlation can be based on preset rules. For example, a rule-based expert system can be established with a series of preset judgment rules, such as judging a magnet installation deviation fault if the magnetic field uniformity index is abnormal but the impedance characteristic parameters are within the normal range; and judging a coil electrical fault if the magnetic field uniformity index is normal but the impedance characteristic parameters are abnormal.

[0107] In addition, machine learning models, such as support vector machines and neural networks, can be used to learn the complex correlation patterns between impedance characteristic parameters and magnetic field uniformity indicators by training on a large number of fault and normal samples. This allows for automatic classification and judgment of fault types. Ultimately, through the above correlation judgment, it is possible to distinguish between magnet installation deviation faults and coil electrical faults. Accurately distinguishing between these two types of faults can guide subsequent maintenance or scrapping decisions, improving production efficiency and product quality.

[0108] Through the above technical solution, the fault analysis unit can extract impedance characteristic parameters unaffected by magnetic field interference from the calibrated impedance spectrum data. These parameters accurately reflect the electrical state of the coil. Simultaneously, by combining indicators characterizing the uniformity of the air gap magnetic field, in-depth correlation judgments are made. This dual verification mechanism enables the system to identify the unique fingerprints of magnet installation deviation faults and coil electrical faults, thereby effectively eliminating the influence of magnetic field interference on coil impedance measurements. This avoids misjudgments and omissions caused by magnetic field interference in traditional methods, significantly improving the accuracy of fault diagnosis.

[0109] Furthermore, this solution can not only accurately distinguish between single types of faults, but also effectively identify and separate compound faults where magnet installation deviation and coil electrical faults coexist. It overcomes the blind spots of existing technologies in handling such complex faults, ensuring more comprehensive fault diagnosis capabilities. This correlation analysis method is logically clear, easy to implement, and can run efficiently on low-cost hardware platforms. Therefore, while ensuring diagnostic accuracy, it significantly improves detection efficiency, meets the needs of mass production, and provides clear guidance for subsequent maintenance or production adjustments.

[0110] The following example will provide a more detailed explanation of the above technical solution: In order to solve the problem of simultaneous detection of magnet installation deviation and coil latent faults on a production line for automotive pedal motors, and to improve detection efficiency and accuracy, the fault detection method described in this solution was adopted.

[0111] First, after the motor under test has completed its initial assembly, the testing system simultaneously acquires the motor's magnetic field distribution data and coil impedance spectrum data. Specifically, multiple magnetic field sensors, such as a Hall sensor array, arranged circumferentially around the motor casing, collect the distribution of the air gap magnetic field in real time, forming magnetic field distribution data. Simultaneously, the impedance spectrum measurement module applies a series of frequency-sweeping electrical signals to the motor's coils and measures the coils' responses to these signals, thereby obtaining the coil's impedance values ​​at different frequencies and forming a spectrum of coil impedance as a function of frequency. This synchronous acquisition method avoids the time consumption associated with step-by-step testing in traditional methods.

[0112] Next, the system calibrates the coil impedance spectrum data based on the acquired magnetic field distribution data. In actual production, even minute deviations in magnet installation, such as eccentricity or tilt, can lead to uneven distribution of the air gap magnetic field, affecting the coil's induced electromotive force and impedance measurement results, potentially misdiagnosing magnet installation issues as coil electrical faults. To eliminate this interference, the system first calculates an index characterizing the uniformity of the air gap magnetic field based on the magnetic field distribution data, for example, by analyzing the differences in readings or harmonic content of the magnetic field sensor array to quantify the degree of magnetic field non-uniformity.

[0113] Then, a calibration model is established based on this uniformity index. This model is used to correct the inductance offset in the coil impedance spectrum data caused by the non-uniformity of the air gap magnetic field. For example, when the magnetic field uniformity index shows obvious magnet eccentricity, the calibration model will adjust the inductance component in the original impedance spectrum data according to a preset compensation algorithm, thereby obtaining calibrated impedance spectrum data that is not affected by the magnet installation state. This calibration step is the key to this scheme. It effectively solves the problem of magnet installation deviation interfering with the coil impedance detection results in traditional methods and avoids misjudgment.

[0114] Finally, the system performs correlation analysis based on the calibrated impedance spectrum data and the original magnetic field distribution data to simultaneously determine the magnet installation status and coil electrical status of the motor. Specifically, this involves extracting at least one impedance characteristic parameter from the calibrated impedance spectrum data, such as the coil's resonant frequency shift and / or low-frequency resistance increment. These parameters sensitively reflect the internal electrical status of the coil, such as inter-turn short circuits or wire breakage. Simultaneously, the system correlates these extracted impedance characteristic parameters with previously calculated indicators characterizing the uniformity of the air gap magnetic field.

[0115] Through this correlation analysis, the system can distinguish between different types of faults: If the magnetic field uniformity index shows significant non-uniformity (e.g., indicating that the magnet is installed off-center or tilted), but the calibrated impedance characteristic parameters are within the normal range, then it is determined to be a magnet installation deviation fault.

[0116] If the magnetic field uniformity index shows that the magnetic field distribution is uniform or within an acceptable range, but the calibrated impedance characteristic parameters show abnormalities (e.g., a significant shift in the resonant frequency or a significant increase in low-frequency resistance), then it is determined to be an electrical fault in the coil, such as an inter-turn short circuit or a broken enameled wire.

[0117] If both show abnormalities, the system can also identify compound faults or distinguish between primary and secondary faults through correlation analysis.

[0118] This correlation analysis method combines calibrated coil electrical characteristics with actual magnetic field distribution characteristics, overcoming the shortcomings of traditional single detection methods that cannot distinguish fault sources and are prone to false positives and false negatives. Through simultaneous acquisition, calibration, and correlation analysis, this solution can efficiently and accurately identify latent faults that may occur in automotive pedal motors during the production process, such as magnet misalignment, tilting, coil turn short circuits, and enameled wire breakage, significantly improving the comprehensiveness and reliability of the detection.

[0119] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for fault detection of a vehicle pedal motor, characterized in that, include: Simultaneously acquire magnetic field distribution data and coil impedance spectrum data of the motor under test; Based on the magnetic field distribution data, the coil impedance spectrum data is calibrated to eliminate the interference caused by abnormal magnet installation status on the coil impedance measurement results, and calibrated impedance spectrum data is obtained. Based on the calibrated impedance spectrum data and the magnetic field distribution data, a correlation analysis is performed to simultaneously determine the magnet installation status and coil electrical status of the motor.

2. The method according to claim 1, characterized in that, The synchronous acquisition of the magnetic field distribution data and coil impedance spectrum data of the motor under test includes: The distribution data of the air gap magnetic field is collected by multiple magnetic field sensors arranged around the circumference of the motor housing; By applying a swept-frequency electrical signal to the coil of the motor and measuring the response, spectral data of the coil's impedance as a function of frequency are obtained.

3. The method according to claim 2, characterized in that, The calibration of the coil impedance spectrum data based on the magnetic field distribution data includes: Based on the magnetic field distribution data, an index characterizing the uniformity of the air gap magnetic field is calculated. A calibration model is established based on the aforementioned indicators to correct the inductance offset in the coil impedance spectrum data caused by the non-uniformity of the air gap magnetic field.

4. The method according to claim 3, characterized in that, The correlation analysis based on the calibrated impedance spectrum data and the magnetic field distribution data includes: Extract at least one impedance characteristic parameter from the calibrated impedance spectrum data; The impedance characteristic parameters are correlated with the index characterizing the uniformity of the air gap magnetic field to distinguish between magnet installation deviation faults and coil electrical faults.

5. The method according to claim 4, characterized in that, The impedance characteristic parameters include resonant frequency offset and / or low-frequency resistance increment.

6. The method according to any one of claims 1 to 5, characterized in that, The magnet installation state includes at least one of installation eccentricity and tilting; the coil electrical state includes at least one of inter-turn short circuit and enameled wire breakage.

7. A fault detection device for automotive pedal motors, characterized in that, include: The data synchronization acquisition unit is configured to synchronously acquire the magnetic field distribution data and coil impedance spectrum data of the motor under test; The data processing and calibration unit is configured to calibrate the coil impedance spectrum data based on the magnetic field distribution data to eliminate interference caused by abnormal magnet installation status on the coil impedance measurement results, and obtain calibrated impedance spectrum data. The fault analysis unit is configured to perform correlation analysis based on the calibrated impedance spectrum data and the magnetic field distribution data to simultaneously determine the magnet installation status and coil electrical status of the motor.

8. The apparatus according to claim 7, characterized in that, The data synchronization acquisition unit includes: A magnetic field sensor array is configured to be arranged circumferentially around the motor housing for collecting data on the distribution of the air gap magnetic field. An impedance spectrum measurement module is configured to apply a swept frequency electrical signal to the coil of the motor and measure the response to obtain impedance spectrum data of the coil.

9. The apparatus according to claim 8, characterized in that, The data processing and calibration unit is further configured as follows: Based on the magnetic field distribution data, an index characterizing the uniformity of the air gap magnetic field is calculated. A calibration model is established based on the aforementioned indicators to correct the inductance offset in the coil impedance spectrum data caused by the non-uniformity of the air gap magnetic field.

10. The apparatus according to claim 9, characterized in that, The fault analysis unit is further configured as follows: Extract at least one impedance characteristic parameter from the calibrated impedance spectrum data; The impedance characteristic parameters are correlated with the index characterizing the uniformity of the air gap magnetic field to distinguish between magnet installation deviation faults and coil electrical faults.

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