A tire abnormal state early warning method and system based on multi-sensor fusion

Abstract

The application relates to the field of tire monitoring technology and discloses a tire abnormal state early warning method and system based on multi-sensor fusion, which comprises the following steps: performing real-time threshold detection on the original acoustic emission signal of a tire to generate a synchronous trigger instruction; performing synchronous data acquisition on an acceleration sensor and an air pressure sensor to obtain synchronous vibration waveforms and synchronous air pressure data; performing time domain analysis on the synchronous vibration waveforms to extract a repeated impact pulse sequence, and performing peak decoupling calibration on the repeated impact pulse sequence to obtain an impact peak sequence; performing periodic fluctuation deconstruction on the impact peak sequence, and constructing a vibration characteristic spectrum; correlating and positioning the vibration characteristic spectrum with the original acoustic emission signal, eliminating suspected damage points, and obtaining effective damage sources; and performing fatigue expansion risk assessment on the effective damage sources to obtain a state abnormality early warning signal; the application can improve the efficiency of the tire abnormal state early warning based on multi-sensor fusion.

Description

Technical Field

[0001] This invention relates to the field of tire monitoring technology, and in particular to a method and system for early warning of abnormal tire conditions based on multi-sensor fusion. Background Technology

[0002] Existing tire abnormality warning technologies mostly use a single sensor for data acquisition and status monitoring. They rely solely on a single type of signal feature to judge the tire's operating status, which cannot fully capture the complex physical changes that occur during tire operation. They also lack sensitivity in recognizing early, weak damage-related signals of the tire, and are prone to missing potential abnormal information due to the one-sidedness of signal detection, making it difficult to achieve early detection of tire abnormalities.

[0003] Existing technologies lack a comprehensive multi-sensor data collaborative processing system and standardized sensor synchronization triggering and data acquisition mechanisms. Data collected by different sensors suffers from spatiotemporal misalignment, and the collected multi-source data is only subjected to simple surface analysis without in-depth feature fusion and correlation verification. This results in significant deviations in tire damage location and poor removal of false damage points. Furthermore, there is a lack of accurate quantitative assessment capabilities for the fatigue propagation process of tire damage, leading to low reliability and reference value of early warning results. Therefore, improving the accuracy, timeliness, and effective identification of damage sources in tire abnormality early warning systems has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a tire abnormality early warning method and system based on multi-sensor fusion to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a tire abnormality early warning method based on multi-sensor fusion, comprising:

[0006] P1. Perform real-time threshold detection on the original acoustic emission signal of the tire, and generate a synchronization trigger command for the tire based on the detection result;

[0007] P2. Based on the synchronous trigger command, synchronous data acquisition is performed on the acceleration sensor and air pressure sensor inside the tire to obtain the synchronous vibration waveform and synchronous air pressure data of the tire.

[0008] P3. Perform time-domain analysis on the synchronous vibration waveform to extract the repetitive impact pulse sequence generated by the tire during the ground imprint period, and perform peak decoupling calibration on the repetitive impact pulse sequence to obtain the impact peak sequence of the tire.

[0009] P4. Perform periodic fluctuation deconstruction on the impact peak sequence, and perform feature fusion on the obtained fluctuation feature components to construct the vibration feature spectrum of the tire.

[0010] P5. The vibration feature spectrum is correlated with the original acoustic emission signal for localization, and the suspected damage points obtained by localization are eliminated as false points based on the synchronous air pressure data to obtain the effective damage source of the tire.

[0011] P6. Perform fatigue propagation risk assessment on the effective damage source to obtain an abnormal condition warning signal for the tire.

[0012] In a preferred embodiment, the step of performing real-time threshold detection on the tire's raw acoustic emission signal and generating a synchronization trigger command for the tire based on the detection result includes:

[0013] The original acoustic emission signal of the tire is obtained, and the background noise and power frequency interference of the original acoustic emission signal are removed to obtain the filtered acoustic emission signal of the tire.

[0014] The filtered acoustic emission signal is windowed and framed to obtain the acoustic emission signal frame sequence of the tire;

[0015] The acoustic emission signal frame sequence is subjected to energy integration mapping, and the peak point of the mapped frame energy integration spectrum is locked to obtain the instantaneous energy jump point of the tire.

[0016] Instantaneous frequency analysis is performed on the acoustic emission signal segment sequence corresponding to the instantaneous energy jump point to obtain the impact characteristic frequency of the tire;

[0017] Spatial correlation weighting is applied to the impact characteristic frequency points to generate the synchronous triggering command for the tire.

[0018] In a preferred embodiment, the step of performing energy integration mapping on the acoustic emission signal frame sequence and locking the peak points of the mapped frame energy integration spectrum to obtain the instantaneous energy jump point of the tire includes:

[0019] Extract the instantaneous amplitude sequence of the acoustic emission signal frame sequence;

[0020] The instantaneous amplitude sequence is subjected to energy normalization and accumulation to obtain the frame energy value of the acoustic emission signal frame sequence, wherein the calculation formula of the frame energy value is as follows:

[0021] ;

[0022] in, Indicates the first The frame energy value of a sequence of acoustic emission signal frames. No. The start time of a sequence of acoustic emission signal frames. This indicates the frame duration of the acoustic emission signal frame sequence. This represents the integration time variable, and its value ranges from [from 1 to 1]. consecutive time points, This indicates that the i-th acoustic emission signal frame sequence is at time t. The instantaneous amplitude;

[0023] Based on the frame energy value, the time-domain integral feature of the acoustic emission signal frame sequence is constructed to obtain the frame energy integral spectrum of the tire;

[0024] Peak search is performed on the frame energy integral spectrum to obtain the instantaneous energy jump point of the tire.

[0025] In a preferred embodiment, the step of synchronously acquiring data from the acceleration sensor and air pressure sensor inside the tire based on the synchronous trigger command to obtain the synchronous vibration waveform and synchronous air pressure data of the tire includes:

[0026] The system receives the synchronization trigger command and, based on the rising edge of the synchronization trigger command, activates the high-speed sampling buffer of the acceleration sensor to obtain the original acceleration sampling stream of the tire.

[0027] Based on the rising edge of the synchronous trigger command, the pressure transient capture of the air pressure sensor is initiated to obtain the original transient air pressure value of the tire;

[0028] The original acceleration sample stream is timestamped and aligned to obtain the aligned acceleration waveform segment of the tire;

[0029] Pressure pulsation decoupling is performed on the original transient air pressure value to obtain the transient air pressure fluctuation sequence of the tire;

[0030] The aligned acceleration waveform segment and the transient air pressure fluctuation sequence are fused using multi-source data to obtain the synchronous vibration waveform and synchronous air pressure data of the tire.

[0031] In a preferred embodiment, the step of performing time-domain analysis on the synchronous vibration waveform to extract the repetitive impact pulse sequence generated by the tire during the contact patch period, and performing peak decoupling calibration on the repetitive impact pulse sequence to obtain the impact peak sequence of the tire, includes:

[0032] The vibration envelope curve of the tire is obtained by outlining the time domain envelope of the synchronous vibration waveform.

[0033] The ground imprint window is used to delineate the vibration envelope curve to obtain the ground imprint vibration segment of the tire.

[0034] The impact transient positioning of the ground imprint vibration segment is performed to obtain the impact pulse train of the tire;

[0035] The peak amplitude of the impact pulse train is marked, and the original peak amplitude set obtained by marking is traced by path mapping to obtain the impact source path distribution of the tire.

[0036] Based on the impact source path distribution, the original peak amplitude set is corrected for energy attenuation to obtain the impact peak sequence of the tire.

[0037] In a preferred embodiment, the step of marking the peak amplitude of the impact pulse train and performing path mapping tracing on the original peak amplitude set obtained by marking to obtain the impact source path distribution of the tire includes:

[0038] The impact pulse train is time-anchored to obtain the impact pulse time-scale sequence of the tire;

[0039] Based on the impact pulse time-stamped sequence, the impact pulse train is time-delay matched to obtain the impact arrival time delay of the tire;

[0040] Spatial location inversion is performed on the impact arrival delay to obtain the spatial location point of the impact source of the tire;

[0041] The propagation path of the impact source is fitted by the spatial positioning point of the impact source to obtain the impact source path distribution of the tire.

[0042] In a preferred embodiment, the step of periodically deconstructing the impact peak sequence and fusing the deconstructed wave characteristic components to construct the vibration characteristic spectrum of the tire includes:

[0043] Cyclostationary demodulation of the impact peak sequence yields the amplitude modulation envelope of the tire.

[0044] The amplitude modulation envelope is unwrapped in phase to restore the phase distribution of the amplitude modulation envelope in the circumferential space of the tire, thereby obtaining the circumferential phase spectrum of the tire.

[0045] By projecting energy flow onto the circumferential phase spectrum, the nodal energy load distribution of the tire is obtained;

[0046] Based on the geometric connectivity of the tire, the energy load distribution of the nodes is topologically interpolated to obtain the vibration characteristic spectrum of the tire.

[0047] In a preferred embodiment, the step of correlating the vibration feature spectrum with the original acoustic emission signal for localization, and based on the synchronized air pressure data, removing false damage points from the located suspected damage points to obtain the effective damage source of the tire, includes:

[0048] Spatial domain peak picking is performed on the vibration feature spectrum to obtain candidate damage nodes of the tire;

[0049] Based on the candidate damage nodes, the original acoustic emission signal is time-stamped to obtain the damage-related time of the tire;

[0050] Based on the damage correlation time, time-frequency atomic capture is performed on the original acoustic emission signal to obtain the damage acoustic emission fingerprint of the tire;

[0051] The synchronous air pressure data is subjected to pressure transient change screening to obtain the air pressure disturbance characteristic points of the tire;

[0052] The consistency of the damage acoustic emission fingerprint and the air pressure disturbance feature points is verified, and the spatiotemporal matching nodes obtained by the verification are targeted to remove false sources to obtain the effective damage source of the tire.

[0053] In a preferred embodiment, the step of assessing the fatigue propagation risk of the effective damage source to obtain an abnormal tire condition warning signal includes:

[0054] Damage parameters are reconstructed from the effective damage sources to obtain the damage feature tensor of the tire;

[0055] Based on the damage feature tensor, manifold feature learning is performed on the effective damage source to obtain the damage evolution manifold of the tire;

[0056] Rate fitting analysis was performed on the damage evolution manifold to obtain the fatigue propagation rate of the tire;

[0057] Based on the fatigue propagation rate, the remaining life of the effective damage source is inverted to obtain the estimated remaining life of the tire.

[0058] The estimated remaining lifespan is mapped to a warning level to obtain an abnormal tire condition warning signal.

[0059] To address the aforementioned problems, the present invention also provides a tire abnormality warning system based on multi-sensor fusion, the system comprising:

[0060] The acoustic emission threshold triggering module is used to perform real-time threshold detection on the original acoustic emission signal of the tire, and generate a synchronous triggering command for the tire based on the detection result.

[0061] The multi-sensor synchronous acquisition module is used to synchronously acquire data from the acceleration sensor and air pressure sensor inside the tire based on the synchronous trigger command, so as to obtain the synchronous vibration waveform and synchronous air pressure data of the tire.

[0062] The impact pulse decoupling calibration module is used to perform time-domain analysis on the synchronous vibration waveform to extract the repetitive impact pulse sequence generated by the tire during the ground imprint period, and to perform peak decoupling calibration on the repetitive impact pulse sequence to obtain the impact peak sequence of the tire.

[0063] The periodic fluctuation feature fusion module is used to perform periodic fluctuation deconstruction on the impact peak sequence, and to perform feature fusion on the obtained fluctuation feature components to obtain the vibration feature spectrum of the tire.

[0064] The damage source association and verification module is used to associate and locate the vibration feature spectrum with the original acoustic emission signal, and to remove false points of suspected damage points obtained by locating them based on the synchronous air pressure data, so as to obtain the effective damage source of the tire.

[0065] The fatigue propagation early warning module is used to assess the fatigue propagation risk of the effective damage source and obtain an abnormal condition warning signal for the tire.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] 1. This technology achieves precise synchronous triggering of multiple sensors through real-time threshold detection of acoustic emission signals, enabling precise spatiotemporal alignment of data collected by acceleration and air pressure sensors. Combined with techniques such as time-domain analysis, peak decoupling calibration, and periodic fluctuation deconstruction, it can accurately extract the impact characteristics of the tire contact patch during the contact patch period and complete feature fusion construction. The resulting vibration feature spectrum can comprehensively and accurately characterize the vibration state of the tire during operation, significantly improving the completeness and accuracy of tire damage-related feature extraction, and laying a reliable feature foundation for damage source identification.

[0068] 2. This technology locates the effective damage source by correlating the vibration characteristic spectrum with the original acoustic emission signal and combining it with synchronous air pressure data to accurately eliminate false damage points. Then, through damage parameter reconstruction and manifold feature learning, it completes the fatigue expansion risk assessment of the effective damage source, accurately inverts the tire's remaining life and completes the early warning level mapping, significantly improving the pertinence and scientific nature of tire abnormality early warning. At the same time, the multi-stage feature verification and fusion mechanism improves the reliability of the early warning signal, realizing timely and accurate early warning of tire abnormality, and providing professional and effective data support and decision-making basis for the safe operation of tires. Attached Figure Description

[0069] Figure 1 This is a flowchart illustrating a tire abnormality early warning method based on multi-sensor fusion, provided in an embodiment of the present invention.

[0070] Figure 2 A functional block diagram of a tire abnormality early warning system based on multi-sensor fusion is provided in an embodiment of the present invention.

[0071] Figure 3 The first set of experimental curves for a tire abnormality early warning method based on multi-sensor fusion provided in an embodiment of the present invention is used to demonstrate the monitoring effect of the system under the parameter configuration of sampling frequency 20000Hz and threshold coefficient 1.2.

[0072] Figure 4 The second set of experimental curves for a tire abnormality early warning method based on multi-sensor fusion provided in an embodiment of the present invention is used to demonstrate the monitoring effect of the system under the parameter configuration of sampling frequency 20000Hz and threshold coefficient 1.3.

[0073] Figure 5 The third set of experimental curves for a tire abnormality warning method based on multi-sensor fusion provided in an embodiment of the present invention is used to demonstrate the monitoring effect of the system under the parameter configuration of sampling frequency 25000Hz and filtering window 7ms.

[0074] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0075] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0076] This application provides a tire abnormality warning method based on multi-sensor fusion. The executing entity of this multi-sensor fusion-based tire abnormality warning method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the multi-sensor fusion-based tire abnormality warning method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0077] Reference Figure 1 The diagram shown is a flowchart illustrating a tire abnormality warning method based on multi-sensor fusion according to an embodiment of the present invention. In this embodiment, the tire abnormality warning method based on multi-sensor fusion includes:

[0078] P1. Perform real-time threshold detection on the original acoustic emission signal of the tire, and generate a synchronization trigger command for the tire based on the detection result;

[0079] In this embodiment of the invention, the step of performing real-time threshold detection on the original acoustic emission signal of the tire and generating a synchronization trigger command for the tire based on the detection result includes:

[0080] The original acoustic emission signal of the tire is obtained, and the background noise and power frequency interference of the original acoustic emission signal are removed to obtain the filtered acoustic emission signal of the tire.

[0081] The filtered acoustic emission signal is windowed and framed to obtain the acoustic emission signal frame sequence of the tire;

[0082] The acoustic emission signal frame sequence is subjected to energy integration mapping, and the peak point of the mapped frame energy integration spectrum is locked to obtain the instantaneous energy jump point of the tire.

[0083] Instantaneous frequency analysis is performed on the acoustic emission signal segment sequence corresponding to the instantaneous energy jump point to obtain the impact characteristic frequency of the tire;

[0084] Spatial correlation weighting is applied to the impact characteristic frequency points to generate the synchronous triggering command for the tire.

[0085] The step of performing energy integration mapping on the acoustic emission signal frame sequence and locking the peak points of the mapped frame energy integration spectrum to obtain the instantaneous energy jump point of the tire includes:

[0086] Extract the instantaneous amplitude sequence of the acoustic emission signal frame sequence;

[0087] The instantaneous amplitude sequence is subjected to energy normalization and accumulation to obtain the frame energy value of the acoustic emission signal frame sequence, wherein the calculation formula of the frame energy value is as follows:

[0088] ;

[0089] in, Indicates the first The frame energy value of a sequence of acoustic emission signal frames. No. The start time of a sequence of acoustic emission signal frames. This indicates the frame duration of the acoustic emission signal frame sequence. This represents the integration time variable, and its range is from... arrive consecutive time points, Indicates the first A sequence of acoustic emission signal frames at time... The instantaneous amplitude;

[0090] Based on the frame energy value, the time-domain integral feature of the acoustic emission signal frame sequence is constructed to obtain the frame energy integral spectrum of the tire;

[0091] Peak search is performed on the frame energy integral spectrum to obtain the instantaneous energy jump point of the tire.

[0092] The raw acoustic emission signal continuously collected during tire operation is acquired, and targeted interference removal processing is performed on the raw acoustic emission signal. First, irrelevant background noise components in the raw acoustic emission signal are identified by signal characteristics and completely removed. Then, power frequency interference components in the raw acoustic emission signal are identified by signal frequency characteristics and accurately filtered out. After processing all interference components, a clean filtered acoustic emission signal of the tire is obtained.

[0093] The obtained filtered acoustic emission signal is subjected to windowed and framed signal processing. A fixed-duration signal window is selected to extract the continuous filtered acoustic emission signal segment by segment in chronological order. During extraction, the signal frames are ensured to be continuous and without overlap or omission. All extracted signal frames are arranged in chronological order to obtain a regular sequence of tire acoustic emission signal frames.

[0094] For the generated acoustic emission signal frame sequence, the signal amplitude corresponding to different time sampling points in each signal frame is fully extracted one by one. According to the arrangement order of the acoustic emission signal frame sequence and the time order within each signal frame, all the extracted amplitude data are integrated in an orderly manner, and finally the instantaneous amplitude sequence of the acoustic emission signal frame sequence that can reflect the real-time changes of the amplitude of each signal frame is obtained.

[0095] Energy normalization and accumulation processing is performed on the extracted instantaneous amplitude sequence. First, all amplitude data in the instantaneous amplitude sequence are adjusted according to a unified energy reference standard so that the amplitude data of different signal frames have a unified energy comparison dimension. Then, the normalized amplitude data is accumulated frame by frame to obtain the total energy value corresponding to each acoustic emission signal frame sequence, and finally the frame energy value of the acoustic emission signal frame sequence is obtained.

[0096] Frame energy values ​​come from the first The energy calculation results of the first acoustic emission signal frame sequence, the first The start time of the acoustic emission signal frame sequence comes from the starting time node of the segmented extraction of the filtered acoustic emission signal during the windowing and framing operation. The frame duration of the acoustic emission signal frame sequence comes from the preset signal window duration in the windowing and framing operation. The integration time variable comes from the iteration of the first... The sequence of acoustic emission signal frames includes all consecutive time points from the start time to the end time. The instantaneous amplitude of each acoustic emission signal frame sequence at the corresponding moment is derived from the signal amplitude data at that moment after windowing and framing the filtered acoustic emission signal.

[0097] This calculation is used to obtain the first... The frame energy value of the acoustic emission signal frame sequence is obtained by analyzing the frame energy value of the first acoustic emission signal frame sequence. After squaring the instantaneous amplitude values ​​at all time points within the acoustic emission signal frame sequence, the squared results over the entire frame duration are accumulated to quantify the energy intensity of the acoustic emission signal in that frame. This provides core data support for constructing the energy integral spectrum of subsequent frames and locking instantaneous energy jump points. When the instantaneous amplitude at each time point within the sequence of acoustic emission signal frames increases as a whole, the resulting frame energy value will also increase. As the frame duration of the acoustic emission signal frame sequence increases, the resulting frame energy value will also increase. When the instantaneous amplitude at each time point within the sequence of acoustic emission signal frames decreases overall, the resulting frame energy value will decrease accordingly. When the frame duration of a sequence of acoustic emission signal frames is shortened, the resulting frame energy value will decrease accordingly.

[0098] Using the calculated frame energy values ​​of each acoustic emission signal frame sequence as the core data, each frame energy value is precisely matched with its corresponding time node according to the time arrangement of the acoustic emission signal frame sequence. Based on this correspondence, a feature system that can reflect the integral change law of acoustic emission signal energy in the time dimension is built, and the time-domain integral feature of the acoustic emission signal frame sequence is constructed. Finally, the frame energy integral spectrum of the tire that can intuitively reflect the time-domain change of energy is obtained.

[0099] A global peak search is performed on the constructed frame energy integral spectrum. The frame energy values ​​corresponding to all time nodes in the spectrum are traversed. Each frame energy value is compared with the frame energy values ​​of its neighboring time nodes one by one. The characteristic positions where the frame energy values ​​in the spectrum suddenly increase are accurately located. These characteristic positions are clearly confirmed and marked, and finally the instantaneous energy jump point of the tire is obtained.

[0100] Based on the marked instantaneous energy jump points of the tire, the acoustic emission signal segments corresponding to each instantaneous energy jump point are accurately extracted from the acoustic emission signal frame sequence. These signal segments are then systematically integrated according to the chronological order of the instantaneous energy jump points to form a sequence of acoustic emission signal segments of the tire. The sequence of signal segments is then subjected to full-band instantaneous frequency analysis to identify the signal frequency information corresponding to each time point in the segment sequence. Specific frequency information that can accurately reflect the impact state during tire operation is then selected, and finally, the impact characteristic frequency points of the tire are obtained.

[0101] Spatial correlation analysis was conducted on the selected impact characteristic frequency points of the tires to sort out the correspondence between each impact characteristic frequency point and the actual spatial position of the tires, clarify the degree of spatial correlation between each impact characteristic frequency point, assign a corresponding weighting coefficient to each frequency point according to the degree of spatial correlation, and perform comprehensive calculation and integration of all impact characteristic frequency points and their corresponding weighting coefficients. Based on the final result of the calculation and integration, a synchronous triggering command for the tire that can accurately trigger other sensors to collect data was generated.

[0102] The beneficial effects of this implementation process are that it performs progressively refined processing on the original acoustic emission signals of the tire. By accurately removing background noise and power frequency interference, the purity of the basic signal is ensured, providing a reliable data foundation for all subsequent signal analysis. Windowing and framing allow the continuous filtered acoustic emission signals to form a regular frame sequence, making subsequent signal feature extraction more systematic and targeted. The extraction of the instantaneous amplitude sequence and the energy normalization and accumulation of the acoustic emission signal frame sequence can accurately quantify the energy characteristics of each signal frame. The frame energy integral spectrum constructed based on the frame energy value can intuitively reflect the time-domain variation law of the acoustic emission signal energy. Peak search can accurately capture key nodes of energy mutation during tire operation, i.e., instantaneous energy jump points. The instantaneous frequency analysis of the signal segment sequence corresponding to the node can accurately extract the characteristic frequency points reflecting the tire impact state. Combined with the spatial correlation weighting processing method, the generated synchronous trigger command can fit the actual operating state and spatial characteristics of the tire, realizing the accurate generation of synchronous trigger command. This provides a precise and effective trigger basis for the subsequent synchronous data acquisition of multiple sensors, ensuring the synchronicity and targeting of subsequent tire condition monitoring data acquisition.

[0103] P2. Based on the synchronous trigger command, synchronous data acquisition is performed on the acceleration sensor and air pressure sensor inside the tire to obtain the synchronous vibration waveform and synchronous air pressure data of the tire.

[0104] In this embodiment of the invention, the step of synchronously acquiring data from the acceleration sensor and air pressure sensor inside the tire based on the synchronous trigger command to obtain the synchronous vibration waveform and synchronous air pressure data of the tire includes:

[0105] The system receives the synchronization trigger command and, based on the rising edge of the synchronization trigger command, activates the high-speed sampling buffer of the acceleration sensor to obtain the original acceleration sampling stream of the tire.

[0106] Based on the rising edge of the synchronous trigger command, the pressure transient capture of the air pressure sensor is initiated to obtain the original transient air pressure value of the tire;

[0107] The original acceleration sample stream is timestamped and aligned to obtain the aligned acceleration waveform segment of the tire;

[0108] Pressure pulsation decoupling is performed on the original transient air pressure value to obtain the transient air pressure fluctuation sequence of the tire;

[0109] The aligned acceleration waveform segment and the transient air pressure fluctuation sequence are fused using multi-source data to obtain the synchronous vibration waveform and synchronous air pressure data of the tire.

[0110] The system receives a synchronization trigger command generated from the acoustic emission signal processing stage, identifies the rising edge of the signal level change from low to high in the synchronization trigger command, and immediately activates the high-speed sampling buffer function of the tire's internal acceleration sensor at that moment. The acceleration sensor continuously collects acceleration signals during tire operation at a preset sampling rate, and stores all collected acceleration signal segments in chronological order to obtain the tire's original acceleration sampling stream.

[0111] The system identifies the rising edge of the signal level change from low to high in the synchronous trigger command. At that moment, the pressure transient capture function of the tire pressure sensor is immediately activated, allowing the pressure sensor to accurately capture the tire pressure change signal at that moment and in the adjacent time period. The captured pressure signal segment is completely recorded to obtain the original tire pressure transient value.

[0112] Extract the acquisition time information corresponding to each sampling point in the original acceleration sampling stream, compare this time information with the rising edge of the synchronization trigger command one by one, and use the rising edge of the synchronization trigger command as a reference to timestamp all sampling points in the original acceleration sampling stream. Select acceleration sampling data that match the preset time period before and after the rising edge of the synchronization trigger command, and arrange the selected sampling data in chronological order to obtain the aligned acceleration waveform segment of the tire.

[0113] The original transient tire pressure values ​​are processed by signal decomposition to identify and separate the stable pressure baseline component and the dynamic pulsation component mixed in the original transient tire pressure values. After removing the stable pressure baseline component, the remaining dynamic pulsation component is arranged in chronological order to obtain the transient tire pressure fluctuation sequence that can reflect the instantaneous change law of tire pressure.

[0114] Using the rising edge of the synchronous trigger command as a unified time reference, the aligned acceleration waveform segment and the sampled data at the corresponding time point in the transient air pressure fluctuation sequence are matched and associated one by one to complete the precise splicing and integration of the two types of data in the time dimension. The acceleration data segment obtained after integration is the synchronous vibration waveform of the tire, and the air pressure data segment obtained after integration is the synchronous air pressure data of the tire.

[0115] The beneficial effects are that this implementation process relies on the rising edge of the synchronous trigger command to achieve synchronous start-up and acquisition of data from the accelerometer and the barometric pressure sensor, ensuring the time consistency of data acquisition from the two types of sensors. The timestamp alignment mark ensures that the acceleration sampling data corresponds precisely to the trigger time, improving the relevance and effectiveness of the vibration waveform. The pressure pulsation decoupling process accurately separates the stable barometric pressure baseline, highlighting the core characteristics of transient barometric pressure fluctuations. The multi-source data fusion achieves precise binding of vibration and barometric pressure data in the time dimension, providing synchronous and reliable multi-source data support for subsequent tire damage source identification and condition assessment, effectively improving the correlation and analytical value of the data.

[0116] P3. Perform time-domain analysis on the synchronous vibration waveform to extract the repetitive impact pulse sequence generated by the tire during the ground imprint period, and perform peak decoupling calibration on the repetitive impact pulse sequence to obtain the impact peak sequence of the tire.

[0117] In this embodiment of the invention, the step of performing time-domain analysis on the synchronous vibration waveform to extract the repetitive impact pulse sequence generated by the tire during the contact patch period, and performing peak decoupling calibration on the repetitive impact pulse sequence to obtain the impact peak sequence of the tire, includes:

[0118] The vibration envelope curve of the tire is obtained by outlining the time domain envelope of the synchronous vibration waveform.

[0119] The ground imprint window is used to delineate the vibration envelope curve to obtain the ground imprint vibration segment of the tire.

[0120] The impact transient positioning of the ground imprint vibration segment is performed to obtain the impact pulse train of the tire;

[0121] The peak amplitude of the impact pulse train is marked, and the original peak amplitude set obtained by marking is traced by path mapping to obtain the impact source path distribution of the tire.

[0122] Based on the impact source path distribution, the original peak amplitude set is corrected for energy attenuation to obtain the impact peak sequence of the tire.

[0123] The step of marking the peak amplitude of the impact pulse train and performing path mapping tracing on the original peak amplitude set obtained by marking to obtain the impact source path distribution of the tire includes:

[0124] The impact pulse train is time-anchored to obtain the impact pulse time-scale sequence of the tire;

[0125] Based on the impact pulse time-stamped sequence, the impact pulse train is time-delay matched to obtain the impact arrival time delay of the tire;

[0126] Spatial location inversion is performed on the impact arrival delay to obtain the spatial location point of the impact source of the tire;

[0127] The propagation path of the impact source is fitted by the spatial positioning point of the impact source to obtain the impact source path distribution of the tire.

[0128] The acquired synchronous vibration waveform is subjected to time-domain envelope delineation. The amplitude data corresponding to all time points in the synchronous vibration waveform are traversed, and the extreme values ​​of amplitude near each time point are recorded in turn. These extreme points are then smoothly connected in chronological order to form a curve that can completely encompass all amplitude changes of the synchronous vibration waveform, and finally the vibration envelope curve of the tire is obtained.

[0129] The obtained vibration envelope curve is subjected to ground imprint window delineation processing to identify the time intervals in the vibration envelope curve where the amplitude changes periodically and the amplitude level is significant. The vibration envelope curve segment corresponding to the time interval is truncated, and the truncated segment completely covers the complete time period of tire-ground contact, thus obtaining the tire ground imprint vibration segment.

[0130] The obtained ground imprint vibration segment is subjected to impact transient localization processing. The amplitude change rate of all time points in the ground imprint vibration segment is traversed, and the time points where the amplitude change rate rises sharply are identified. The vibration signal segments corresponding to these time points are extracted. The extracted signal segments are composed of multiple continuous impact transient signals, and finally the impact pulse train of the tire is obtained.

[0131] The obtained impact pulse train is processed by peak amplitude marking, the highest amplitude point in each impact pulse signal is identified, the amplitude value corresponding to the highest point is recorded, and the highest amplitude values ​​of all impact pulses are collected and organized in the order of the impact pulses to finally obtain the original peak amplitude set of the tire.

[0132] The obtained impact pulse train is time-stamped, and a corresponding acquisition time identifier is assigned to each independent impact pulse signal in the impact pulse train. These time identifiers are arranged in the order of appearance of the impact pulses to form a sequence that can reflect the time sequence of appearance of each impact pulse, and finally the impact pulse time-stamp sequence of the tire is obtained.

[0133] Based on the obtained impact pulse time stamp sequence, the impact pulse train is subjected to time delay matching processing. The time stamp of each impact pulse is compared with the preset reference time stamp one by one, and the time difference of each impact pulse relative to the reference time stamp is calculated. These time differences are arranged in the order of the impact pulses to finally obtain the impact arrival time delay of the tire.

[0134] The obtained impact arrival time delay is processed by spatial location inversion. Combined with the propagation law of the impact signal in the tire structure, each impact arrival time delay is converted into the corresponding spatial location coordinates of the impact source inside the tire. These coordinate points are arranged in the order of the impact pulses to finally obtain the spatial location point of the impact source of the tire.

[0135] The obtained spatial positioning points of the impact sources are subjected to propagation path fitting processing. The signal propagation path from each spatial positioning point of the impact source to the sensor acquisition position is sorted out. The length of each propagation path is statistically sorted according to the order of the impact sources to form a distribution result that can reflect the propagation distance law of each impact source signal. Finally, the impact source path distribution of the tire is obtained.

[0136] Based on the obtained impact source path distribution, the original peak amplitude set is subjected to energy attenuation correction processing. According to the path length corresponding to each impact source, the magnitude of the corresponding amplitude value in the original peak amplitude set is adjusted to eliminate the influence of energy attenuation on amplitude measurement during signal propagation. The corrected amplitude values ​​are arranged in the order of impact pulses to finally obtain the impact peak sequence of the tire.

[0137] The beneficial effects are that the implementation process clearly outlines the amplitude change contour of the synchronous vibration waveform through time-domain envelope delineation, providing an intuitive basis for the accurate delineation of the ground imprint period. The ground imprint window delineation effectively focuses on the core period of tire-ground contact, improving the targeting of impact signal extraction. Impact transient positioning accurately identifies all impact pulse signals within the ground imprint period. Peak amplitude marking fully records the amplitude characteristics of each impact pulse. Time-scale anchoring and time delay matching realize the accurate quantification of impact pulse time information. Spatial location inversion and propagation path fitting clarify the spatial distribution and path information of each impact source. Energy attenuation correction eliminates the interference of signal propagation attenuation on amplitude. The obtained impact peak sequence can more realistically reflect the original energy intensity of each impact source, providing accurate and reliable feature data for subsequent tire damage source identification and condition assessment.

[0138] P4. Perform periodic fluctuation deconstruction on the impact peak sequence, and perform feature fusion on the obtained fluctuation feature components to construct the vibration feature spectrum of the tire.

[0139] In this embodiment of the invention, the step of periodically deconstructing the impact peak sequence and fusing the deconstructed wave characteristic components to construct the vibration characteristic spectrum of the tire includes:

[0140] Cyclostationary demodulation of the impact peak sequence yields the amplitude modulation envelope of the tire.

[0141] The amplitude modulation envelope is unwrapped in phase to restore the phase distribution of the amplitude modulation envelope in the circumferential space of the tire, thereby obtaining the circumferential phase spectrum of the tire.

[0142] By projecting energy flow onto the circumferential phase spectrum, the nodal energy load distribution of the tire is obtained;

[0143] Based on the geometric connectivity of the tire, the energy load distribution of the nodes is topologically interpolated to obtain the vibration characteristic spectrum of the tire.

[0144] Cyclic stationary demodulation is performed on the impact peak sequence. All peak data points within the impact peak sequence are traversed, and the periodic fluctuation pattern of the data points with tire circumferential rotation is analyzed. The basic component with stationary characteristics and the amplitude modulation component reflecting fluctuation changes are separated from the sequence. All amplitude features of the amplitude modulation component are extracted and integrated according to the order of tire circumferential rotation to form a curve that can reflect the amplitude modulation law of tire circumferential rotation, thus obtaining the amplitude modulation envelope of the tire.

[0145] The amplitude modulation envelope is dewound in phase. The phase data corresponding to each tire circumferential position in the amplitude modulation envelope is traversed. The phase folding intervals in the phase data are identified and the phase data in the intervals are corrected for continuity. The folded phase data is linearly unfolded according to the direction of tire circumferential rotation so that the corrected phase data can continuously and completely reflect the phase change state in the tire circumferential space. In this way, the phase distribution of the amplitude modulation envelope in the tire circumferential space is restored, and the tire circumferential phase spectrum is obtained.

[0146] Energy flow projection processing is performed on the circumferential phase spectrum. The structural nodes of the tire itself are used as the reference points for energy projection. The phase energy characteristics corresponding to each circumferential position in the circumferential phase spectrum are accurately mapped to the corresponding tire structural nodes according to the energy conduction path inside the tire. The phase energy value borne by each structural node is counted one by one. The energy values ​​of all structural nodes are recorded and arranged in an orderly manner according to the actual spatial position of the nodes to obtain the node energy load distribution of the tire.

[0147] Based on the geometric connectivity of the tire, the energy load distribution of nodes is processed by topological adjacency interpolation. The geometric connection relationship between all structural nodes of the tire is comprehensively sorted out, and the adjacent structural nodes corresponding to each structural node and their spatial position relationship are clarified. Based on the existing energy load values ​​of each structural node, linear interpolation calculation is performed on the energy load change trend between adjacent nodes, and the transition values ​​of energy load between adjacent nodes are supplemented. The original energy load values ​​of structural nodes are integrated with the interpolated transition values, and the full-dimensional feature arrangement is completed according to the overall geometric structure and circumferential spatial position of the tire to obtain the vibration characteristic spectrum of the tire.

[0148] The beneficial effects are that this implementation process accurately deconstructs the periodic fluctuation characteristics of the impact peak sequence through cyclic smooth demodulation, effectively separating the fundamental component and the amplitude modulation component. This allows the amplitude modulation envelope to clearly reflect the amplitude modulation law in the tire circumferential direction. Phase unwinding eliminates the feature distortion problem caused by phase folding, completely restoring the true phase distribution in the tire circumferential space. The circumferential phase spectrum provides accurate phase feature basis for subsequent energy analysis. Energy flow projection combines the phase energy characteristics with the actual structural nodes of the tire, making the energy distribution characteristics more consistent with the tire's physical structure. Topological adjacency interpolation based on geometric connectivity supplements the energy transition characteristics between adjacent nodes, making the node energy load distribution more continuous and complete. The finally generated vibration feature spectrum transforms the sequence characteristics of the impact peak into a spatial feature spectrum that conforms to the overall tire structure, which can comprehensively and accurately reflect the overall vibration characteristics and energy distribution law of the tire. This provides systematic and accurate vibration feature support for the subsequent correlation and localization of tire damage sources, effectively improving the comprehensiveness and accuracy of subsequent damage source identification.

[0149] P5. The vibration feature spectrum is correlated with the original acoustic emission signal for localization, and the suspected damage points obtained by localization are eliminated as false points based on the synchronous air pressure data to obtain the effective damage source of the tire.

[0150] In this embodiment of the invention, the step of associating the vibration feature spectrum with the original acoustic emission signal for localization, and removing false damage points from the located suspected damage points based on the synchronized air pressure data to obtain the effective damage source of the tire, includes:

[0151] Spatial domain peak picking is performed on the vibration feature spectrum to obtain candidate damage nodes of the tire;

[0152] Based on the candidate damage nodes, the original acoustic emission signal is time-stamped to obtain the damage-related time of the tire;

[0153] Based on the damage correlation time, time-frequency atomic capture is performed on the original acoustic emission signal to obtain the damage acoustic emission fingerprint of the tire;

[0154] The synchronous air pressure data is subjected to pressure transient change screening to obtain the air pressure disturbance characteristic points of the tire;

[0155] The consistency of the damage acoustic emission fingerprint and the air pressure disturbance feature points is verified, and the spatiotemporal matching nodes obtained by the verification are targeted to remove false sources to obtain the effective damage source of the tire.

[0156] Spatial domain peak picking is performed on the vibration feature spectrum. The energy values ​​corresponding to all spatial locations in the vibration feature spectrum are traversed. The energy value of each location is compared with the energy values ​​of the surrounding adjacent spatial locations one by one. Spatial nodes with prominent energy values ​​are identified. These nodes are fully marked and collected in an orderly manner to obtain the candidate damage nodes of the tire.

[0157] Based on the obtained candidate damage nodes of the tire, time-stamped mapping processing is performed on the original acoustic emission signal. The spatial position information of the tire corresponding to each candidate damage node is retrieved. Combined with the rotation law of tire operation, the acquisition time of the original acoustic emission signal when the corresponding vibration feature is generated at the spatial position is matched. Each candidate damage node is bound to the corresponding acquisition time. All bound time information is extracted and sorted in the order of candidate damage nodes to obtain the damage-related time of the tire.

[0158] Based on the obtained tire damage correlation time, time-frequency atom capture processing is performed on the original acoustic emission signal. Taking each damage correlation time as the core time point, segments of the original acoustic emission signal within a preset time period before and after that time point are extracted. Time-frequency features are extracted in all dimensions for each signal segment, and time-frequency atom information that can characterize tire damage features in the segment is captured. The time-frequency atom information corresponding to all candidate damage nodes is integrated in node order to form a unique feature set, thus obtaining the tire damage acoustic emission fingerprint.

[0159] The synchronous air pressure data is subjected to pressure transient change screening processing. The air pressure values ​​corresponding to all time points in the synchronous air pressure data are traversed, and the change in air pressure value at each time point relative to the previous moment is calculated. The transient change points of air pressure that exceed the normal change range are identified, and the time information and air pressure change characteristics corresponding to these change points are fully recorded. These feature points are organized and clearly marked in an orderly manner to obtain the tire air pressure disturbance feature points.

[0160] Consistency verification is performed on the acoustic emission fingerprint of tire damage and the tire pressure disturbance feature points. The time information corresponding to the acoustic emission fingerprint of damage is compared with the time information of the pressure disturbance feature points one by one to verify the matching of the two in the time dimension. At the same time, combined with the spatial location information of candidate damage nodes, the correlation between the pressure disturbance feature points and candidate damage nodes in the spatial dimension is verified. Nodes that match in both the temporal and spatial dimensions are selected as spatiotemporal matching nodes. Deep feature verification is performed on the spatiotemporal matching nodes to identify pseudo-source nodes with only a single feature anomaly and no actual tire damage, and these are precisely targeted and eliminated. Nodes that have passed the dual verification of spatiotemporal features and have feature matching are retained to obtain the effective damage source of the tire.

[0161] The beneficial effects of this implementation process are as follows: It achieves precise screening of tire damage candidate nodes by picking spatial peak values ​​of the vibration feature spectrum, defining a clear range for subsequent damage source localization; it achieves precise temporal correlation between vibration features and original acoustic emission signals based on time-scale mapping of candidate damage nodes; time-frequency atomic capture extracts unique damage acoustic emission features, giving damage nodes corresponding acoustic emission fingerprints; it captures abnormal tire pressure disturbances by screening for pressure transient changes in synchronous air pressure data, providing a pressure-dimensional reference for damage verification; it achieves dual verification of multi-source features in the spatiotemporal dimensions by verifying the consistency of damage acoustic emission fingerprints and air pressure disturbance feature points; and it precisely removes false nodes without actual damage through targeted elimination of false sources. The resulting effective damage sources, after multi-dimensional and multi-source data layer-by-layer verification, significantly improve the accuracy and authenticity of tire damage source localization, effectively avoiding misjudgments caused by single signal analysis. This provides reliable and accurate core data support for subsequent tire fatigue expansion risk assessment, ensuring the scientific validity of subsequent tire abnormality warnings.

[0162] P6. Perform fatigue propagation risk assessment on the effective damage source to obtain an abnormal condition warning signal for the tire.

[0163] In this embodiment of the invention, the step of assessing the fatigue propagation risk of the effective damage source to obtain an abnormal tire condition warning signal includes:

[0164] Damage parameters are reconstructed from the effective damage sources to obtain the damage feature tensor of the tire;

[0165] Based on the damage feature tensor, manifold feature learning is performed on the effective damage source to obtain the damage evolution manifold of the tire;

[0166] Rate fitting analysis was performed on the damage evolution manifold to obtain the fatigue propagation rate of the tire;

[0167] Based on the fatigue propagation rate, the remaining life of the effective damage source is inverted to obtain the estimated remaining life of the tire.

[0168] The estimated remaining lifespan is mapped to a warning level to obtain an abnormal tire condition warning signal.

[0169] Damage parameters are reconstructed for effective damage sources. All relevant damage parameters, such as spatial location features, damage amplitude features, damage energy features, and damage time-frequency features, are extracted. The extracted damage parameters are classified and organized according to feature dimensions. Damage parameters of different dimensions are combined in a multi-dimensional structure to complete the integration and construction of multi-dimensional damage features, thus obtaining the tire damage feature tensor.

[0170] Based on the obtained tire damage feature tensor, manifold feature learning is performed on the effective damage sources. Using the multi-dimensional data in the damage feature tensor as a basis, the feature performance and change patterns of the effective damage sources under each feature dimension are sorted out. Feature dimensionality reduction and feature space mapping are performed on the multi-dimensional data in the damage feature tensor to deeply explore the evolution trend of the damage features of the effective damage sources with the tire operation process. The obtained evolution trend is fully constructed and visualized in the form of a manifold to restore the overall evolution process and spatial distribution of the damage features, thus obtaining the tire damage evolution manifold.

[0171] Rate fitting analysis was performed on the obtained tire damage evolution manifold. All evolution feature points representing the damage development state in the damage evolution manifold were traversed, and the change trajectory of each evolution feature point with the actual running time of the tire was sorted out. The change trajectory was continuously fitted to accurately capture the speed of change of damage features with tire running time. This law was transformed into a quantitative result that can directly represent the damage development speed, and the fatigue propagation rate of the tire was obtained.

[0172] Based on the obtained fatigue propagation rate of the tire, the remaining life of the effective damage source is inverted. Taking the fatigue propagation rate as the core reference, combined with the tire's own structural characteristics and the damage threshold of normal tire use, the actual tire running time required for the effective damage source to develop from the current actual damage state to reach the damage threshold is estimated. This running time is accurately quantified to obtain the estimated remaining life of the tire.

[0173] The estimated remaining life of the tire is mapped to a warning level. A multi-level warning level standard is pre-set, which corresponds one-to-one with the range of remaining life values ​​of the tire. The calculated estimated remaining life is precisely compared with the preset standard to determine the specific warning level corresponding to the estimated remaining life. A standardized signal containing core information such as the spatial location of the damage source, the remaining life value, and the warning level is generated according to the determined warning level to obtain the abnormal tire condition warning signal.

[0174] The beneficial effects of this implementation process are that it achieves the structured integration of multi-dimensional damage features through the reconstruction of damage parameters of effective damage sources. The damage feature tensor completely retains all the core feature information of the damage source, providing a comprehensive and systematic data foundation for subsequent fatigue propagation analysis. Based on the manifold feature learning of the damage feature tensor, the evolution law of damage features is deeply mined. The damage evolution manifold intuitively and completely presents the development process and distribution state of damage. The rate fitting analysis of the damage evolution manifold realizes the accurate quantification of tire fatigue propagation speed, giving the assessment of fatigue propagation a clear quantitative indicator. Based on the inversion of the remaining life based on the fatigue propagation rate, the remaining life can be accurately calculated. The remaining life estimate, calculated from the tire's current damage state to reaching the damage threshold, upgrades tire condition assessment from qualitative to quantitative analysis. Mapping the remaining life estimate to warning levels generates abnormal condition warning signals, achieving standardized and tiered warnings for tire anomalies. The entire process, through progressive quantitative analysis and feature mining, ensures a high degree of scientific rigor and accuracy in tire fatigue expansion risk assessment. The generated abnormal condition warning signals accurately reflect the actual abnormal state and risk level of the tire, providing clear and reliable decision-making basis for timely tire maintenance and safe operation, effectively improving the practicality and guidance of tire abnormal condition warnings.

[0175] like Figure 2 The diagram shown is a functional block diagram of a tire abnormality early warning system based on multi-sensor fusion provided in an embodiment of the present invention.

[0176] The tire abnormality early warning system 100 based on multi-sensor fusion described in this invention can be installed in an electronic device. Depending on the functions implemented, the tire abnormality early warning system 100 may include an acoustic emission threshold triggering module 101, a multi-sensor synchronous acquisition module 102, an impact pulse decoupling calibration module 103, a periodic fluctuation feature fusion module 104, a damage source correlation verification module 105, and a fatigue extension early warning module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0177] In this embodiment, the functions of each module / unit are as follows:

[0178] The acoustic emission threshold triggering module 101 is used to perform real-time threshold detection on the original acoustic emission signal of the tire, and generate a synchronous triggering command for the tire based on the detection result.

[0179] The multi-sensor synchronous acquisition module 102 is used to synchronously acquire data from the acceleration sensor and air pressure sensor inside the tire based on the synchronous trigger command, so as to obtain the synchronous vibration waveform and synchronous air pressure data of the tire.

[0180] The impact pulse decoupling calibration module 103 is used to perform time-domain analysis on the synchronous vibration waveform to extract the repetitive impact pulse sequence generated by the tire during the ground imprint period, and to perform peak decoupling calibration on the repetitive impact pulse sequence to obtain the impact peak sequence of the tire.

[0181] The periodic fluctuation feature fusion module 104 is used to perform periodic fluctuation deconstruction on the impact peak sequence, and to perform feature fusion on the obtained fluctuation feature components to obtain the vibration feature spectrum of the tire.

[0182] The damage source association verification module 105 is used to associate and locate the vibration feature spectrum with the original acoustic emission signal, and to remove false points from the suspected damage points located based on the synchronous air pressure data, so as to obtain the effective damage source of the tire.

[0183] The fatigue propagation early warning module 106 is used to assess the fatigue propagation risk of the effective damage source and obtain an abnormal condition warning signal for the tire.

[0184] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0185] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0186] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0187] Figure 3 The monitoring curves for a tire abnormality early warning method based on multi-sensor fusion, provided in an embodiment of the present invention, are shown with parameters configured as follows: sampling frequency 20000Hz, threshold coefficient 1.2, filtering window 8ms, decoupling factor 0.95, and calibration gain 1.02. The curves contain two types of data: the blue curve represents the acoustic emission signal frame energy value, exhibiting a bimodal shape of "rise—fall—trough—rise again—fall again," with two peak values ​​of approximately 15.6 and 15.5, and a trough of approximately 9.2, reflecting the change in structural vibration energy of the tire during two impact events; the orange curve represents the impact peak sequence, rising rapidly from 0 and then stabilizing between 4.8 and 5.0, representing the intensity of the external impact load, used to distinguish between external excitation and internal tire anomalies.

[0188] Figure 4 The monitoring curve of a tire abnormality early warning method based on multi-sensor fusion provided in an embodiment of the present invention is shown when the parameters are configured as follows: sampling frequency 20000Hz, threshold coefficient 1.3, filtering window 8ms, decoupling factor 0.96, and calibration gain 1.01. The two peaks of the blue acoustic emission energy curve are approximately 15.3 and 15.7, respectively, and the trough is approximately 9.7. Figure 3 In comparison, the valley energy is slightly higher, reflecting the enhanced sensitivity of the algorithm to identify weak anomalies after the threshold coefficient and decoupling factor are improved; the orange impact peak curve eventually stabilizes between 4.7 and 5.0, with the peak value slightly lower than... Figure 3 This reflects the optimization of the separation effect of the decoupling factor on the impact and acoustic emission signals.

[0189] Figure 5 The monitoring curves for a tire abnormality early warning method based on multi-sensor fusion, provided in an embodiment of the present invention, are shown with the following parameters configured: sampling frequency 25000Hz, threshold coefficient 1.2, filtering window 7ms, decoupling factor 0.94, and calibration gain 1.03. The blue acoustic emission energy curve shows two peaks of approximately 15.5 and 15.6, with a trough of approximately 9.4. The curve exhibits richer details and smaller trough fluctuations, reflecting the improvement in signal resolution and noise suppression achieved by a higher sampling rate and narrower filtering window. The orange impact peak curve eventually stabilizes between 4.7 and 5.2, with a peak value slightly higher than the previous two groups, reflecting the amplification effect of the increased calibration gain on the impact signal.

[0190] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0191] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for early warning of abnormal tire conditions based on multi-sensor fusion, characterized in that, The method includes: P1. Perform real-time threshold detection on the original acoustic emission signal of the tire, and generate a synchronization trigger command for the tire based on the detection result, including: The original acoustic emission signal of the tire is obtained, and the background noise and power frequency interference of the original acoustic emission signal are removed to obtain the filtered acoustic emission signal of the tire. The filtered acoustic emission signal is windowed and framed to obtain the acoustic emission signal frame sequence of the tire; The acoustic emission signal frame sequence is subjected to energy integration mapping, and the peak points of the mapped frame energy integration spectrum are locked to obtain the instantaneous energy jump points of the tire, including: Extract the instantaneous amplitude sequence of the acoustic emission signal frame sequence; The instantaneous amplitude sequence is subjected to energy normalization and accumulation to obtain the frame energy value of the acoustic emission signal frame sequence, wherein the calculation formula of the frame energy value is as follows: ； in, Indicates the first The frame energy value of a sequence of acoustic emission signal frames. No. The start time of a sequence of acoustic emission signal frames. This indicates the frame duration of the acoustic emission signal frame sequence. Represents the integration time variable, with a range of values ​​from arrive consecutive time points, Indicates the first A sequence of acoustic emission signal frames at time... The instantaneous amplitude; Based on the frame energy value, the time-domain integral feature of the acoustic emission signal frame sequence is constructed to obtain the frame energy integral spectrum of the tire; Peak search is performed on the frame energy integral spectrum to obtain the instantaneous energy jump point of the tire; Instantaneous frequency analysis is performed on the acoustic emission signal segment sequence corresponding to the instantaneous energy jump point to obtain the impact characteristic frequency of the tire; Spatial correlation weighting is applied to the impact characteristic frequency points to generate the synchronous triggering command for the tire; P2. Based on the synchronous trigger command, synchronous data acquisition is performed on the acceleration sensor and air pressure sensor inside the tire to obtain the synchronous vibration waveform and synchronous air pressure data of the tire. P3. Perform time-domain analysis on the synchronous vibration waveform to extract the repetitive impact pulse sequence generated by the tire during the ground imprint period, and perform peak decoupling calibration on the repetitive impact pulse sequence to obtain the impact peak sequence of the tire. P4. Perform periodic fluctuation deconstruction on the impact peak sequence, and perform feature fusion on the obtained fluctuation feature components to construct the vibration feature spectrum of the tire. P5. The vibration feature spectrum is correlated with the original acoustic emission signal for localization, and the suspected damage points obtained by localization are eliminated as false points based on the synchronous air pressure data to obtain the effective damage source of the tire. P6. Perform fatigue propagation risk assessment on the effective damage source to obtain an abnormal condition warning signal for the tire.

2. The tire abnormality early warning method based on multi-sensor fusion as described in claim 1, characterized in that, Based on the synchronous trigger command, the acceleration sensor and air pressure sensor inside the tire are synchronously acquired to obtain the synchronous vibration waveform and synchronous air pressure data of the tire, including: The system receives the synchronization trigger command and, based on the rising edge of the synchronization trigger command, activates the high-speed sampling buffer of the acceleration sensor to obtain the original acceleration sampling stream of the tire. Based on the rising edge of the synchronous trigger command, the pressure transient capture of the air pressure sensor is initiated to obtain the original transient air pressure value of the tire; The original acceleration sample stream is timestamped and aligned to obtain the aligned acceleration waveform segment of the tire; Pressure pulsation decoupling is performed on the original transient air pressure value to obtain the transient air pressure fluctuation sequence of the tire; The aligned acceleration waveform segment and the transient air pressure fluctuation sequence are fused using multi-source data to obtain the synchronous vibration waveform and synchronous air pressure data of the tire.

3. The tire abnormality early warning method based on multi-sensor fusion as described in claim 1, characterized in that, The synchronous vibration waveform is analyzed in the time domain to extract the repetitive impact pulse sequence generated by the tire during the contact patch period, and the repetitive impact pulse sequence is decoupled and calibrated to obtain the impact peak sequence of the tire, including: The vibration envelope curve of the tire is obtained by outlining the time domain envelope of the synchronous vibration waveform. The ground imprint window is used to delineate the vibration envelope curve to obtain the ground imprint vibration segment of the tire. The impact transient positioning of the ground imprint vibration segment is performed to obtain the impact pulse train of the tire; The peak amplitude of the impact pulse train is marked, and the original peak amplitude set obtained by marking is traced by path mapping to obtain the impact source path distribution of the tire. Based on the impact source path distribution, the original peak amplitude set is corrected for energy attenuation to obtain the impact peak sequence of the tire.

4. The tire abnormality early warning method based on multi-sensor fusion as described in claim 3, characterized in that, The step of marking the peak amplitude of the impact pulse train and performing path mapping tracing on the original peak amplitude set obtained by marking to obtain the impact source path distribution of the tire includes: The impact pulse train is time-anchored to obtain the impact pulse time-scale sequence of the tire; Based on the impact pulse time-stamped sequence, the impact pulse train is time-delay matched to obtain the impact arrival time delay of the tire; Spatial location inversion is performed on the impact arrival delay to obtain the spatial location point of the impact source of the tire; The propagation path of the impact source is fitted by the spatial positioning point of the impact source to obtain the impact source path distribution of the tire.

5. The tire abnormality early warning method based on multi-sensor fusion as described in claim 1, characterized in that, The process of periodically deconstructing the impact peak sequence and fusing the obtained wave characteristic components to construct the vibration characteristic spectrum of the tire includes: Cyclostationary demodulation of the impact peak sequence yields the amplitude modulation envelope of the tire. The amplitude modulation envelope is unwrapped in phase to restore the phase distribution of the amplitude modulation envelope in the circumferential space of the tire, thereby obtaining the circumferential phase spectrum of the tire. By projecting energy flow onto the circumferential phase spectrum, the nodal energy load distribution of the tire is obtained; Based on the geometric connectivity of the tire, the energy load distribution of the nodes is topologically interpolated to obtain the vibration characteristic spectrum of the tire.

6. The tire abnormality early warning method based on multi-sensor fusion as described in claim 1, characterized in that, The process of correlating the vibration feature spectrum with the original acoustic emission signal for localization, and eliminating false damage points based on the synchronized air pressure data to obtain the effective damage source of the tire, includes: Spatial domain peak picking is performed on the vibration feature spectrum to obtain candidate damage nodes of the tire; Based on the candidate damage nodes, the original acoustic emission signal is time-stamped to obtain the damage-related time of the tire; Based on the damage correlation time, time-frequency atomic capture is performed on the original acoustic emission signal to obtain the damage acoustic emission fingerprint of the tire; The synchronous air pressure data is subjected to pressure transient change screening to obtain the air pressure disturbance characteristic points of the tire; The consistency of the damage acoustic emission fingerprint and the air pressure disturbance feature points is verified, and the spatiotemporal matching nodes obtained by the verification are targeted to remove false sources to obtain the effective damage source of the tire.

7. The tire abnormality early warning method based on multi-sensor fusion as described in claim 1, characterized in that, The fatigue propagation risk assessment of the effective damage source, to obtain an abnormal tire condition warning signal, includes: Damage parameters are reconstructed from the effective damage sources to obtain the damage feature tensor of the tire; Based on the damage feature tensor, manifold feature learning is performed on the effective damage source to obtain the damage evolution manifold of the tire; Rate fitting analysis was performed on the damage evolution manifold to obtain the fatigue propagation rate of the tire; Based on the fatigue propagation rate, the remaining life of the effective damage source is inverted to obtain the estimated remaining life of the tire. The estimated remaining lifespan is mapped to a warning level to obtain an abnormal tire condition warning signal.

8. A tire abnormality early warning system based on multi-sensor fusion, characterized in that, The system for implementing the tire abnormality warning method based on multi-sensor fusion as described in claim 1 includes: An acoustic emission threshold triggering module is used to perform real-time threshold detection on the tire's original acoustic emission signal, and generate a synchronization triggering command for the tire based on the detection result, including: The original acoustic emission signal of the tire is obtained, and the background noise and power frequency interference of the original acoustic emission signal are removed to obtain the filtered acoustic emission signal of the tire. The filtered acoustic emission signal is windowed and framed to obtain the acoustic emission signal frame sequence of the tire; The acoustic emission signal frame sequence is subjected to energy integration mapping, and the peak points of the mapped frame energy integration spectrum are locked to obtain the instantaneous energy jump points of the tire, including: Extract the instantaneous amplitude sequence of the acoustic emission signal frame sequence; The instantaneous amplitude sequence is subjected to energy normalization and accumulation to obtain the frame energy value of the acoustic emission signal frame sequence, wherein the calculation formula of the frame energy value is as follows: ； in, Indicates the first The frame energy value of a sequence of acoustic emission signal frames. No. The start time of a sequence of acoustic emission signal frames. This indicates the frame duration of the acoustic emission signal frame sequence. Represents the integration time variable, with a range of values ​​from arrive consecutive time points, Indicates the first A sequence of acoustic emission signal frames at time... The instantaneous amplitude; Based on the frame energy value, the time-domain integral feature of the acoustic emission signal frame sequence is constructed to obtain the frame energy integral spectrum of the tire; Peak search is performed on the frame energy integral spectrum to obtain the instantaneous energy jump point of the tire; Instantaneous frequency analysis is performed on the acoustic emission signal segment sequence corresponding to the instantaneous energy jump point to obtain the impact characteristic frequency of the tire; Spatial correlation weighting is applied to the impact characteristic frequency points to generate the synchronous triggering command for the tire; The multi-sensor synchronous acquisition module is used to synchronously acquire data from the acceleration sensor and air pressure sensor inside the tire based on the synchronous trigger command, so as to obtain the synchronous vibration waveform and synchronous air pressure data of the tire. The impact pulse decoupling calibration module is used to perform time-domain analysis on the synchronous vibration waveform to extract the repetitive impact pulse sequence generated by the tire during the ground imprint period, and to perform peak decoupling calibration on the repetitive impact pulse sequence to obtain the impact peak sequence of the tire. The periodic fluctuation feature fusion module is used to perform periodic fluctuation deconstruction on the impact peak sequence, and to perform feature fusion on the obtained fluctuation feature components to obtain the vibration feature spectrum of the tire. The damage source association and verification module is used to associate and locate the vibration feature spectrum with the original acoustic emission signal, and to remove false points of suspected damage points obtained by locating them based on the synchronous air pressure data, so as to obtain the effective damage source of the tire. The fatigue propagation early warning module is used to assess the fatigue propagation risk of the effective damage source and obtain an abnormal condition warning signal for the tire.

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