A method and system for monitoring wheel loosening based on wheel speed signal order analysis

Abstract

This invention provides a wheel loosening monitoring method and system based on wheel speed signal order analysis. Utilizing the inherent iso-angular sampling characteristics of signals collected by existing vehicle wheel speed sensors, a core software algorithm converts non-stationary vibration signals in the time domain into stationary signals in the angular domain, completely eliminating the interference of speed fluctuations on fault feature identification. Simultaneously, the algorithm establishes a quantified wheel loosening judgment standard, enabling graded monitoring, precise location, and real-time alarm of faults. Furthermore, the system is designed with calibration, reset, and disabling logic for related algorithms to adapt to different vehicle driving conditions and maintenance scenarios, effectively reducing false alarm rates and providing end-to-end pure software algorithm support for vehicle driving safety.

Description

Technical Field

[0001] This invention relates to the field of vehicle safety monitoring, and more specifically, to a wheel loosening monitoring method and system based on wheel speed signal order analysis. It is applicable to real-time monitoring of wheel loosening in various motor vehicles (passenger cars, commercial vehicles, etc.) and is recommended to be integrated into the vehicle's electronic control system. Background Technology

[0002] As the core load-bearing component of a vehicle, the tightness of its connection directly determines driving safety. Loose wheels are often caused by loose bolts, rusted bolts and nuts, or worn wheel hubs. Initially, there are no obvious external symptoms, but as the loosening worsens, it can lead to vehicle swerving, abnormal braking, and in severe cases, wheel detachment, causing serious traffic accidents. Therefore, real-time and accurate monitoring of wheel loosening is of great significance for preventing traffic accidents and ensuring driving safety.

[0003] Existing wheel loosening detection technologies mainly fall into three categories: First, manual inspection, which involves periodically checking wheel bolt torque and observing wheel hub clearance. This method is inefficient, subjective, and cannot achieve real-time monitoring, making it difficult to detect early minor loosening. Second, monitoring methods using hardware sensors, which collect vibration signals by installing vibration sensors on the wheel hub or frame and analyze signal characteristics to identify loosening. However, vibration sensors are expensive to install, easily affected by road bumps, engine vibrations, etc., and their monitoring accuracy is greatly affected by the environment. Furthermore, they require additional hardware and have poor adaptability. Third, pure software / algorithm-based monitoring solutions based on wheel speed characteristic signals, which identify tire loosening by monitoring the periodic abnormal characteristics of wheel speed signals and send real-time warning signals to the driver.

[0004] Existing wheel speed signal monitoring methods suffer from problems such as incomplete signal noise filtering, inaccurate feature extraction, and single threshold determination, resulting in low monitoring sensitivity and high false alarm rate, making it difficult to meet the needs of complex operating conditions during actual vehicle operation (such as speed change, turning, and uneven road surface). Summary of the Invention

[0005] This invention addresses the technical problems existing in the prior art by providing a method and system for monitoring wheel loosening based on wheel speed signal order analysis.

[0006] According to a first aspect of the present invention, a method for detecting wheel loosening based on wheel speed signal order analysis is provided, comprising: During vehicle operation, wheel speed signals are acquired for any wheel of the vehicle, and the wheel speed signals are preprocessed to obtain preprocessed wheel speed signals. The preprocessed wheel speed signal is converted into an instantaneous wheel rotation frequency signal, and abnormal peak values ​​are corrected for the instantaneous wheel rotation frequency signal. Based on the time-domain instantaneous wheel rotation frequency signal corrected for abnormal peak values, an angle-domain wheel rotation frequency signal is generated. The angular domain wheel rotation frequency signal is converted into an order spectrum. The order features characterizing wheel loosening are extracted from the order spectrum. The order features are weighted to obtain the current comprehensive energy features. Based on the current comprehensive energy characteristics, determine whether the wheel is loose and the severity of the loosening.

[0007] Based on the above technical solution, the present invention can also be improved as follows.

[0008] Optionally, the wheel speed signal is preprocessed to obtain a preprocessed wheel speed signal, including: Real-time analysis of wheel speed signal waveform, automatic identification of signal abrupt changes, excessive noise, and abnormal waveform interruption, and troubleshooting of abnormal situations; The wheel speed signal after interference investigation is subjected to wavelet denoising and window smoothing filtering to obtain the preprocessed wheel speed signal.

[0009] Optionally, converting the preprocessed wheel speed signal into a wheel instantaneous rotation frequency signal includes:

[0010] in, This is the instantaneous rotational frequency signal of the wheel, measured in Hz. 0 represents the collected wheel speed signal, in km / h. The actual rolling diameter of the wheel, in meters; Correcting abnormal peak values ​​in the instantaneous wheel rotation frequency signal includes: Automatically screen for abrupt peaks and out-of-range jump points in the instantaneous rotation frequency signal of the wheel based on the 3σ criterion; Abnormal signal segments whose signal amplitude exceeds the threshold in the instantaneous wheel rotation frequency signal are directly removed. For the signal gaps left after removal, linear interpolation is used to fill in the missing data to obtain the corrected time-domain instantaneous wheel rotation frequency signal.

[0011] Optionally, generating the angle-domain wheel frequency signal based on the time-domain wheel instantaneous frequency signal corrected for abnormal peaks includes: Based on the corrected time-domain instantaneous wheel rotation frequency signal Calculate the instantaneous wheel rotation angle at time t during the vehicle's movement. The calculation formula is as follows:

[0012] in, Let be the instantaneous wheel rotation angle at time t, in rad. Let τ be the instantaneous rotational frequency signal of the wheel at time τ; instantaneous turning angle of the wheel Perform angle domain resampling to generate angle domain wheel rotation frequency signal .

[0013] Optionally, the instantaneous steering angle of the wheel Perform angle domain resampling to generate angle domain wheel rotation frequency signal ,include: Set the number of sampling points per wheel revolution Calculate the order resolution Rank; Will Equal-time sampled signal converted to Equal-angle type signals, generate equal-angle sequences Where N is the total number of wheel rotations per unit time. It refers to the i-th time. For a moment Time-domain signal, It refers to the j-th angle. Indicates angle Angle domain signal; The instantaneous wheel rotation frequency signal in the time domain is obtained by applying the cubic spline interpolation algorithm. Interpolating to an equal-angle sequence automatically generates the instantaneous wheel rotation frequency signal in the angle domain. .

[0014] Optionally, the step of converting the angular domain wheel rotation frequency signal into an order spectrum and extracting the order features characterizing wheel loosening from the order spectrum includes: The generated angle-domain wheel rotation frequency signal is subjected to a fast Fourier transform to convert the angle-domain signal into an order-domain signal, generating an order spectrum of wheel rotation. The horizontal axis of the order spectrum is set to order 7-20, which covers the main characteristic orders caused by wheel loosening. Different orders are denoted as m, m=7, 8, 9...20. The vertical axis is the signal vibration amplitude, denoted as E(m), which represents the vibration energy corresponding to each order.

[0015] Optionally, the weighted processing of features of each order to obtain the current comprehensive energy feature includes: E synthetical = +

[0016] Among them, E synthetical Based on the current comprehensive energy characteristics, The m-th order vibration energy gain coefficient. E0(m) is the energy gain coefficient of the nth order vibration, and E0(m) is the relative vibration amplitude to the reference.

[0017] Optionally, the step of determining whether the wheel is currently loose and the severity of the loosening based on the current comprehensive energy characteristics includes: When E synthetical <E base At that time, the tires were not loose; When E base <E synthetical <AE base At this time, the tire may show signs of loosening or other abnormal vibrations, and a signal Signal1 will be sent to the outside. When AE base <E synthetical <BE base At that time, the tire had become loose and was generating significant abnormal vibrations, sending out Signal2 and the corresponding loose tire position signal. When BE base <E synthetical <CE base At this time, the tire has become severely loose and is at risk of falling off, so a Signal3 signal and the corresponding location of the loose tire are sent to the outside world. Among them, E base The baseline threshold for the order characteristics under normal conditions where the wheels are not loose is A, B, and C are all preset parameters, which are calibrated and confirmed through actual vehicle performance, and A < B < C.

[0018] Optional, baseline threshold E base Obtain it through the following methods: Under normal conditions where the wheels are not loose, the wheel speed signal acquisition unit collects wheel speed signals from each of the four wheels of the vehicle individually, with each wheel being acquired synchronously. After data acquisition, the wheel speed signals of each wheel are processed using an order analysis algorithm to extract the order features of the normal wheel speed signals. These features are then weighted to obtain the baseline threshold E of the order features for each wheel under normal conditions where it is not loose. base .

[0019] According to a second aspect of the present invention, a wheel loosening monitoring system based on wheel speed signal order analysis is provided, comprising: The acquisition module is used to acquire wheel speed signals for any wheel of the vehicle during vehicle operation, and to preprocess the wheel speed signals to obtain preprocessed wheel speed signals. The conversion module is used to convert the preprocessed wheel speed signal into a wheel instantaneous rotation frequency signal, correct abnormal peaks in the wheel instantaneous rotation frequency signal, and convert the time-domain wheel instantaneous rotation frequency signal after abnormal correction into an angle-domain wheel rotation frequency signal. The extraction module is used to convert the wheel rotation frequency signal in the angle domain into an order spectrum, extract the order features representing wheel loosening from the order spectrum, and perform weighted processing on each order feature to obtain the current comprehensive energy feature. The judgment module is used to determine whether the wheel is loose and the severity of the loosening based on the current comprehensive energy characteristics.

[0020] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the processor is configured to implement the steps of a wheel loosening monitoring method based on wheel speed signal order analysis when executing a computer management program stored in the memory.

[0021] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having a computer management program stored thereon, which, when executed by a processor, implements the steps of a wheel loosening monitoring method based on wheel speed signal order analysis.

[0022] This invention provides a wheel loosening monitoring method and system based on wheel speed signal order analysis. Utilizing the inherent iso-angular sampling characteristics of signals collected by existing vehicle wheel speed sensors, a core software algorithm converts non-stationary vibration signals in the time domain into stationary signals in the angular domain, completely eliminating the interference of speed fluctuations on fault feature identification. Simultaneously, the algorithm establishes a quantified wheel loosening judgment standard, enabling graded monitoring, precise location, and real-time alarm of faults. Furthermore, the system is designed with calibration, reset, and disabling logic for related algorithms to adapt to different vehicle driving conditions and maintenance scenarios, effectively reducing false alarm rates and providing end-to-end pure software algorithm support for vehicle driving safety. Attached Figure Description

[0023] Figure 1 A flowchart of a wheel loosening monitoring method based on wheel speed signal order analysis is provided as an embodiment of the present invention; Figure 2 A schematic diagram of the features at each order mentioned; Figure 3 This is a schematic diagram of a wheel loosening monitoring system based on wheel speed signal order analysis, provided as an embodiment of the present invention.

[0024] Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Figure 5 This is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined with each other to form feasible technical solutions. Such combinations are not constrained by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0026] The core algorithm principle of the wheel loosening monitoring method based on wheel speed signal order analysis provided by this invention is to utilize the uniform sampling characteristics of wheel speed angle signals (the wheel speed sensor triggers a count once for each tooth detected on the wheel hub gear ring, and the central angle corresponding to a single tooth is fixed). The wheel speed signal is regarded as an angular domain vibration signal. Through angular domain conversion algorithm and order analysis algorithm, the non-stationary signal in the time domain is converted into a stationary signal in the angular domain, so that the vibration characteristics caused by wheel loosening are fixed at a specific order and are not affected by speed fluctuations. Then, the signal preprocessing algorithm removes interference, extracts order features, and combines with the quantization judgment algorithm to achieve accurate identification and graded alarm of wheel loosening.

[0027] The core logic of the order analysis algorithm is as follows: without referring to time-domain features, using the wheel rotation angle as the reference axis, regardless of the rotational speed, the vibration characteristics generated by wheel loosening always correspond to a fixed order. Specifically, the first order corresponds to one vibration per revolution of the wheel, the second order corresponds to two vibrations per revolution of the wheel, and so on. The magnitude of the order reflects the number and severity of bolt loosening, and the degree of loosening can be quantified by the order amplitude.

[0028] Figure 1 To illustrate a flowchart of a wheel loosening monitoring method based on wheel speed signal order analysis provided in an embodiment of the present invention, as shown below... Figure 1 As shown, the method includes the following steps: Step 1: During vehicle operation, acquire wheel speed signals for any wheel of the vehicle, and preprocess the wheel speed signals to obtain preprocessed wheel speed signals.

[0029] Understandably, real-time vehicle speed monitoring automatically records data once the vehicle speed steadily increases to within the 20-100 km / h range and remains stable for 10 seconds, acquiring the wheel speed signal of each wheel to avoid signal loss due to trigger delays. It should be noted that the following steps process the wheel speed signal of any single wheel, individually assessing the looseness of each wheel. For each wheel, during the acquisition process, the wheel speed signal waveform is analyzed in real time, automatically identifying abnormalities such as signal abrupt changes, excessive noise, and waveform interruptions. If an abnormality is detected, acquisition is immediately paused, and an interference investigation prompt is output. After eliminating interference, the system automatically restarts the acquisition process.

[0030] Then, the signal is denoised and smoothed. Specifically, a wavelet denoising algorithm with a db4 wavelet basis and 3-4 level decomposition is used. The algorithm decomposes the signal into high-frequency detail components and low-frequency approximation components, automatically removes noise components in the high-frequency detail components, retains low-frequency approximation components, and reconstructs an effective signal that retains the periodic characteristics of wheel rotation, thus completely eliminating noise caused by electromagnetic interference and random vibrations of extreme road surfaces.

[0031] The denoised signal is automatically processed by a filtering algorithm with a built-in fixed filtering window parameter (5-10 sampling points) to further eliminate minor fluctuations in the signal, making the wheel speed signal waveform smoother and providing accurate input for the subsequent instantaneous frequency extraction algorithm.

[0032] Step 2: Convert the preprocessed wheel speed signal into an instantaneous wheel rotation frequency signal, and correct any abnormal peak values ​​in the instantaneous wheel rotation frequency signal.

[0033] Understandably, the dimensions of the preprocessed wheel speed signal are converted to the instantaneous wheel rotation frequency (unit: Hz). The conversion formula is fixed by the algorithm and automatically calls parameters for calculation. The conversion formula is as follows:

[0034] in, The instantaneous rotational frequency signal of the wheel (Hz). 0 represents the collected wheel speed signal (km / h). The actual rolling diameter of the wheel (m) is determined by calibration of the actual vehicle.

[0035] In this embodiment of the invention, an abnormal peak detection model is constructed using the 3σ criterion to automatically identify abnormal peaks in the instantaneous rotation frequency signal of the wheel and remove abnormal signal segments where the instantaneous rotation frequency changes beyond the limit. For the signal gaps after removing the abnormal parts, the algorithm calls the linear interpolation method to fill in the missing data, ensuring that the instantaneous rotation frequency signal is continuous, complete, and without breaks, thus avoiding errors in the subsequent angle calculation algorithm.

[0036] Step 3: Generate the wheel frequency signal in the angle domain based on the time-domain wheel instantaneous frequency signal after anomaly correction.

[0037] Understandably, this step, by calculating the instantaneous rotation angle and applying a resampling algorithm to the angle domain, transforms the non-stationary signal in the time domain into a stationary signal in the angle domain. This is the core of the order analysis algorithm, and the specific process is as follows: First, the instantaneous wheel rotation angle is calculated: based on the corrected time-domain instantaneous wheel rotation frequency signal. Calculate the instantaneous wheel rotation angle at time t during the vehicle's movement. The calculation method is as follows:

[0038] The accuracy of the angle calculation must be ensured during the calculation process; among which, Let t be the instantaneous rotation angle of the wheel at time t (rad). Let τ be the instantaneous wheel rotation frequency (Hz) at time τ, and the integration interval is automatically set by the algorithm from time 0 to time t.

[0039] Then, the instantaneous wheel rotation angle in the angle domain. Angular domain resampling is performed. The purpose of the resampling algorithm is to convert instantaneous frequency signals with equal time intervals into angular domain signals with equal angular intervals, thus completely eliminating drastic signal fluctuations caused by vehicle speed fluctuations. The specific algorithm operation is as follows: The algorithm has built-in key resampling parameters, setting the number of sampling points per revolution. To balance the accuracy and computational efficiency of order analysis, the algorithm automatically calculates the order resolution. The resolution must be determined to ensure accurate capture of minute changes in wheel looseness; Equal-time sampled signal converted to Equal-angle type signals, generate equal-angle sequences Where N is the total number of wheel rotations per unit time. It refers to the i-th time. For a moment Time-domain signal, It refers to the j-th angle. Indicates angle The angle domain signal. A cubic spline interpolation algorithm is applied to the instantaneous frequency conversion signal in the time domain. Interpolating to the aforementioned equal-angle sequence automatically generates an angle-domain frequency conversion signal. This ensures that the interpolated signal is undistorted and can fully preserve the vibration characteristics of the loose wheel.

[0040] Step 4: Convert the wheel rotation frequency signal in the angle domain into an order spectrum, extract the order features that characterize wheel loosening from the order spectrum, and perform weighted processing on each order feature to obtain the current comprehensive energy feature.

[0041] Understandably, this step converts the wheel speed angle domain frequency signal into an order spectrum and extracts the order features of wheel loosening. The specific algorithm logic is as follows: Step 4.1, Order spectrum generation: This involves generating the interpolated angle-domain frequency-converted signal. A Fast Fourier Transform (FFT) is performed to convert the angle domain signal into an order domain signal, generating an order spectrum of wheel rotation. The horizontal axis of the order spectrum is set by the system to order 7-20, which covers the main characteristic orders of wheel loosening. Different orders are denoted as m (m=7, 8, 9...20). The vertical axis represents the signal vibration amplitude, denoted as E(m), which represents the vibration energy corresponding to each order. The algorithm automatically performs amplitude normalization processing on the generated order spectrum, converting the amplitude of each order into a ratio relative to the reference data E0(m) (self-learning data), which is used as the vibration feature of tire loosening.

[0042] Step 4.2, Extraction of wheel loosening order features: The core feature of wheel loosening is the periodic collision vibration between the wheel hub and the tire mounting surface. This vibration is strongly correlated with the wheel rotation period, and its order features have a clear regularity. The system compares the data with the benchmark data.

[0043] Research has revealed that in the order spectrum of wheel speed signals, features of orders 1-6 are primarily related to the vibration characteristics of the road surface and the vehicle suspension itself. Among these, the order features influenced by the vehicle suspension itself can be confirmed through calibration within the same vehicle model (as the suspension structure, materials, and damping characteristics are consistent throughout production). After confirming the suspension-related influences, the system normalizes the features within the aforementioned orders, extracting features related to road conditions for road surface monitoring.

[0044] The 7th to 10th orders are the main characteristics for monitoring tire looseness: under normal conditions, the amplitude of the 7th order is stable and the fluctuation range is small; when the wheel is loose, the collision vibration between the wheel hub and the brake disc is synchronized with the wheel rotation, resulting in a significant increase in the amplitude of the 7th order, which serves as the core basis for judging wheel looseness.

[0045] Auxiliary features: Harmonic orders 11-20 generally exhibit different amplitude energies depending on the degree and number of loose wheel nuts, which can be used to assist in determining whether the tire is loose and the degree of looseness. In a more specific embodiment, the energy features of different orders can be weighted according to the actual situation of the calibrated vehicle, and the system can determine whether the current comprehensive energy features meet the looseness criteria and issue a looseness warning.

[0046] In this embodiment of the invention, order features of orders 7-10 and 11-20 related to wheel loosening are mainly extracted. The energy features of each order before and after tire loosening are as follows: Figure 2 As shown.

[0047] Subsequently, the extracted order features are quantitatively analyzed to determine the level of wheel looseness and trigger the corresponding alarm, specifically including: The overall order vibration energy of the tire is calculated using a weighted algorithm and used to determine the degree of tire looseness. E synthetical = +

[0048] The above The m-th order vibration energy gain coefficient. E is the energy gain coefficient of the nth order vibration. synthetical This represents the current overall energy characteristics.

[0049] Step 5: Based on the current comprehensive energy characteristics, determine whether the wheel is loose and the severity of the loosening.

[0050] Understandably, based on the current comprehensive energy characteristics after weighted processing, the loosening of the wheel is quantitatively determined, with a built-in four-level quantitative determination standard: When E synthetical <E base At that time, the tires showed no signs of loosening or loosening, and the system did not send any signals. When E base <E synthetical <AE base At this time, the tire may become loose or experience other abnormal vibrations (dynamic balance issues), and the system will send a signal Signal1. When AE base <E synthetical <BE base At that time, the tire had become loose and was generating significant abnormal vibrations. The system then sent out Signal2 and the corresponding signal indicating the location of the loose tire. When BE base <E synthetical <CE base At this point, the tire has become severely loose and is at risk of falling off. The system sends out Signal3 and the corresponding signal indicating the location of the loose tire.

[0051] Among them, E base The baseline threshold for the order characteristics under normal conditions where the wheels are not loose is A, B, and C are all preset parameters, which are calibrated and confirmed through actual vehicle performance, and A < B < C.

[0052] In one embodiment of the present invention, the reference threshold E base Obtain it through the following methods: Under normal conditions where the wheels are not loose, the wheel speed signal acquisition unit collects wheel speed signals from each of the four wheels of the vehicle individually, with each wheel being acquired synchronously. After data acquisition, the order features of the normal wheel speed signal are extracted using an order analysis algorithm and weighted to obtain the baseline threshold E of the order features under normal conditions where the wheels are not loose. base .

[0053] Specifically, the algorithm incorporates vehicle driving condition determination logic, setting the core monitoring condition to a speed range of 20-100 km / h; that is, when the vehicle speed exceeds this range, the system enters a disabled state and stops subsequent calculations. The tire loosening detection system based on the order algorithm does not restrict the smoothness of vehicle driving; that is, under rapid acceleration and deceleration conditions, the uniform sampling characteristics of the wheel speed signal are unaffected, and the quality of the underlying signal required for order analysis remains unaffected. For turning and uphill / downhill conditions, the system can achieve condition identification based on the vehicle's own operating signals. The system is generally not disabled except in the event of extreme abnormal signals.

[0054] Under normal conditions where the wheels are not loose, the algorithm controls the wheel speed signal acquisition unit to collect wheel speed signals from each of the four wheels of the vehicle individually. The algorithm is set to collect and record data from each wheel synchronously for 20-30 minutes. After the acquisition is completed, the order characteristics of the normal wheel speed signals are extracted through an order analysis algorithm (retaining the amplitude reference values ​​from the 7th to the 20th order), and stored in the algorithm's non-volatile memory as the reference threshold for subsequent wheel loosening fault determination, thus completing the system's self-learning.

[0055] When the vehicle's instrument panel or other HMI interactive devices receive the different signals mentioned above, they need to display different information to prompt the driver. Upon receiving Signal 1, the HMI device can display a text prompt reminding the driver to check the tires when appropriate. Upon receiving Signal 2, the HMI device illuminates a warning light and indicates the location of the abnormal tire, and can provide a text prompt explaining the current fault type. Upon receiving Signal 3, the corresponding warning light should flash frequently and be accompanied by a buzzer, and the text message should clearly state that there is a serious tire fault and the vehicle must stop immediately.

[0056] To adapt to different vehicle driving conditions and maintenance scenarios, reduce false alarm rates, and extend the stability of algorithm operation, the software incorporates system calibration, reset, and disabling algorithm logic, as detailed below: System initial calibration self-learning: After the monitoring system completes a reset operation and starts running, the algorithm automatically starts a 20-30 minute continuous calibration process, controls the vehicle to drive at 20-100km / h, collects wheel speed signals of the four wheels without looseness, extracts the reference order features of each wheel through the order analysis algorithm, and stores them in the algorithm's non-volatile database as a permanent reference for subsequent fault judgment until the algorithm receives a reset command.

[0057] System Reset Algorithm: When the algorithm detects that the vehicle has undergone maintenance operations such as tire replacement, wheel hub replacement, bolt replacement, brake disc replacement (which can be triggered manually or by signal linkage from the vehicle diagnostic system), it will automatically prompt that a system reset is required. After receiving the reset command, the algorithm will clear the original benchmark data and automatically re-collect benchmark data according to the requirements of the working condition screening and benchmark calibration algorithm module to complete a new round of calibration and ensure the accuracy of the monitoring algorithm.

[0058] See Figure 3 This paper illustrates a wheel loosening monitoring system based on wheel speed signal order analysis, comprising: The acquisition module 301 is used to acquire wheel speed signals for any wheel of the vehicle during the vehicle's operation, and to preprocess the wheel speed signals to obtain preprocessed wheel speed signals. The conversion module 302 is used to convert the preprocessed wheel speed signal into a wheel instantaneous rotation frequency signal, correct abnormal peaks in the wheel instantaneous rotation frequency signal, and convert the time-domain wheel instantaneous rotation frequency signal after abnormal correction into an angle-domain wheel rotation frequency signal. The extraction module 303 is used to convert the wheel rotation frequency signal in the angle domain into an order spectrum, extract the order features representing wheel loosening from the order spectrum, and perform weighted processing on each order feature to obtain the current comprehensive energy feature. The judgment module 304 is used to determine whether the wheel is loose and the severity of the loosening based on the current comprehensive energy characteristics.

[0059] It is understood that the wheel loosening monitoring system based on wheel speed signal order analysis provided by the present invention corresponds to the wheel loosening monitoring method based on wheel speed signal order analysis provided in the foregoing embodiments. The relevant technical features of the wheel loosening monitoring system based on wheel speed signal order analysis can be referred to the relevant technical features of the wheel loosening monitoring method based on wheel speed signal order analysis, and will not be repeated here.

[0060] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, it implements a wheel loosening monitoring method based on wheel speed signal order analysis.

[0061] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by the present invention. (See diagram below.) Figure 5As shown, this embodiment provides a computer-readable storage medium 500, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, it implements a wheel loosening monitoring method based on wheel speed signal order analysis.

[0062] The wheel loosening monitoring method and system based on wheel speed signal order analysis provided in this invention has the following beneficial effects: (1) The algorithm has strong adaptability and completely eliminates the interference of speed fluctuation: The core software algorithm utilizes the natural equal angle sampling characteristics of wheel speed signal and converts the non-stationary vibration signal in the time domain into a stationary signal in the angle domain through the order analysis algorithm. This breaks through the limitation that the traditional FFT analysis algorithm is only applicable to stationary signals, and completely solves the problems of spectral broadening and frequency ambiguity caused by vehicle speed fluctuation. This ensures that the fault characteristics of wheel loosening are always fixed at a specific order, and achieves accurate fault identification.

[0063] (2) Quantitative grading judgment algorithm with strong fault handling: The four-level quantitative judgment algorithm normalizes the abnormal vibration characteristics of different orders and the abnormal order, and divides wheel loosening into four levels: normal, slight, moderate and severe. Each level corresponds to a clear handling suggestion. The algorithm automatically matches the judgment result and prompts, avoiding over-repair or untimely repair, which not only ensures vehicle driving safety, but also reduces maintenance costs.

[0064] (3) The algorithm has good real-time performance and accurate fault location: The four wheels adopt independent parallel acquisition and analysis algorithms. The signal processing is automatically completed by the software algorithm. The response time from abnormal signal acquisition to effective alarm is controlled within 3-5 minutes, realizing real-time early warning of faults. At the same time, the algorithm can accurately locate the fault wheel number, providing clear direction for on-site handling and greatly improving fault handling efficiency.

[0065] (4) The algorithm is highly versatile and easy to mass-produce: The algorithm does not rely on dedicated hardware equipment and can be adapted to the wheel speed signal acquisition unit and data processing module of existing vehicles. It does not require additional dedicated detection equipment, which greatly reduces the deployment and industrialization costs of the system. The algorithm supports automatic calibration, reset and disable, and is compatible with various motor vehicles such as passenger cars and commercial vehicles. It can cope with various scenarios such as flat roads, bad roads, and vehicle maintenance, effectively avoid false alarms, and improve the practicality and reliability of the algorithm.

[0066] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0067] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0068] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0069] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0070] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0071] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0072] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for monitoring wheel loosening based on wheel speed signal order analysis, characterized in that, include: During vehicle operation, wheel speed signals are acquired for any wheel of the vehicle, and the wheel speed signals are preprocessed to obtain preprocessed wheel speed signals. The preprocessed wheel speed signal is converted into an instantaneous wheel rotation frequency signal, and abnormal peak values ​​are corrected for the instantaneous wheel rotation frequency signal. Based on the time-domain instantaneous wheel rotation frequency signal corrected for abnormal peak values, an angle-domain wheel rotation frequency signal is generated. The angular domain wheel rotation frequency signal is converted into an order spectrum. The order features characterizing wheel loosening are extracted from the order spectrum. The order features are weighted to obtain the current comprehensive energy features. Based on the current comprehensive energy characteristics, determine whether the wheel is loose and the severity of the loosening.

2. The wheel loosening monitoring method according to claim 1, characterized in that, The wheel speed signal is preprocessed to obtain a preprocessed wheel speed signal, including: Real-time analysis of wheel speed signal waveform, automatic identification of signal abrupt changes, excessive noise, and abnormal waveform interruption, and troubleshooting of abnormal situations; The wheel speed signal after interference investigation is subjected to wavelet denoising and window smoothing filtering to obtain the preprocessed wheel speed signal.

3. The wheel loosening monitoring method according to claim 1, characterized in that, The step of converting the preprocessed wheel speed signal into an instantaneous wheel rotation frequency signal includes: in, This is the instantaneous rotational frequency signal of the wheel, measured in Hz. 0 represents the collected wheel speed signal, in km / h. The actual rolling diameter of the wheel, in meters; Correcting abnormal peak values ​​in the instantaneous wheel rotation frequency signal includes: Automatically screen for abrupt peaks and out-of-range jump points in the instantaneous rotation frequency signal of the wheel based on the 3σ criterion; Abnormal signal segments whose signal amplitude exceeds the threshold in the instantaneous wheel rotation frequency signal are directly removed. For the signal gaps left after removal, linear interpolation is used to fill in the missing data to obtain the corrected time-domain instantaneous wheel rotation frequency signal.

4. The wheel loosening monitoring method according to claim 1, characterized in that, The step of generating an angle-domain wheel frequency signal based on the time-domain instantaneous wheel frequency signal corrected for abnormal peak values ​​includes: Based on the corrected time-domain instantaneous wheel rotation frequency signal Calculate the instantaneous wheel rotation angle at time t during the vehicle's movement. The calculation formula is as follows: in, Let be the instantaneous wheel rotation angle at time t, in rad. Let τ be the instantaneous rotational frequency signal of the wheel at time τ; instantaneous turning angle of the wheel Perform angle domain resampling to generate angle domain wheel rotation frequency signal .

5. The wheel loosening monitoring method according to claim 4, characterized in that, The instantaneous turning angle of the wheel Perform angle domain resampling to generate angle domain wheel rotation frequency signal ,include: Set the number of sampling points per wheel revolution Calculate the order resolution Rank; Will Equal-time sampled signal converted to Equal-angle type signals, generate equal-angle sequences Where N is the total number of wheel rotations per unit time. It refers to the i-th time. For a moment Time-domain signal, It refers to the j-th angle. Indicates angle Angular domain signal; The instantaneous wheel rotation frequency signal in the time domain is obtained by applying the cubic spline interpolation algorithm. Interpolating to an equal-angle sequence automatically generates the instantaneous wheel rotation frequency signal in the angle domain. .

6. The wheel loosening detection method according to claim 1, characterized in that, The process of converting the angular domain wheel rotation frequency signal into an order spectrum and extracting various order features characterizing wheel loosening from the order spectrum includes: The generated angle-domain wheel rotation frequency signal is subjected to a fast Fourier transform to convert the angle-domain signal into an order-domain signal, generating an order spectrum of wheel rotation. The horizontal axis of the order spectrum is set to order 7-20, which covers the main characteristic orders caused by wheel loosening. Different orders are denoted as m, m=7, 8, 9...

20. The vertical axis is the signal vibration amplitude, denoted as E(m), which represents the vibration energy corresponding to each order.

7. The wheel loosening monitoring method according to claim 1 or 6, characterized in that, The weighted processing of features at each order to obtain the current comprehensive energy features includes: E synthetical = + Among them, E synthetical Based on the current comprehensive energy characteristics, The m-th order vibration energy gain coefficient. E0(m) is the energy gain coefficient of the nth order vibration, and E0(m) is the relative vibration amplitude to the reference.

8. The wheel loosening monitoring method according to claim 1, characterized in that, Based on the current comprehensive energy characteristics, Determine whether the current wheel is loose and the severity of the looseness, including: When E synthetical <E base At that time, the tires were not loose; When E base <E synthetical <AE base At this time, the tire may show signs of loosening or other abnormal vibrations, and a signal Signal1 will be sent to the outside. When AE base <E synthetical <BE base At that time, the tire had become loose and was generating significant abnormal vibrations, sending out Signal2 and the corresponding loose tire position signal. When BE base <E synthetical <CE base At this point, the tire has become severely loose and is at risk of falling off, so a Signal3 signal with the corresponding location of the loose tire is sent out. Among them, E base The baseline threshold for the order characteristics under normal conditions where the wheels are not loose is A, B, and C are all preset parameters, which are calibrated and confirmed through actual vehicle performance, and A < B < C.

9. The wheel loosening monitoring method according to claim 8, characterized in that, Baseline threshold E base Obtain it through the following methods: Under normal conditions where the wheels are not loose, the wheel speed signal acquisition unit collects wheel speed signals for each of the four wheels of the vehicle individually, with each wheel being collected synchronously. After data acquisition, the wheel speed signals of each wheel are processed using an order analysis algorithm to extract the order features of the normal wheel speed signals. These features are then weighted to obtain the baseline threshold E of the order features for each wheel under normal conditions where it is not loose. base .

10. A wheel loosening monitoring system based on wheel speed signal order analysis, characterized in that, include: The acquisition module is used to acquire wheel speed signals for any wheel of the vehicle during vehicle operation, and to preprocess the wheel speed signals to obtain preprocessed wheel speed signals. The conversion module is used to convert the preprocessed wheel speed signal into a wheel instantaneous rotation frequency signal, correct abnormal peaks in the wheel instantaneous rotation frequency signal, and convert the time-domain wheel instantaneous rotation frequency signal after abnormal correction into an angle-domain wheel rotation frequency signal. The extraction module is used to convert the wheel rotation frequency signal in the angle domain into an order spectrum, extract the order features representing wheel loosening from the order spectrum, and perform weighted processing on each order feature to obtain the current comprehensive energy feature. The judgment module is used to determine whether the wheel is loose and the severity of the loosening based on the current comprehensive energy characteristics.

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