Online health monitoring and self-repairing system and method for vehicle composite material structural component

Abstract

According to the on-line health monitoring and self-repairing system and method for the vehicle composite material, the conductive sensors are arranged between layers of the mixed woven fiber cloth composite material, excitation currents are provided for the sensors in the health monitoring state, voltage signals at different positions of the sensors are collected, the damage condition of a vehicle structural part is reflected, and the self-repairing effect is improved. Estimating the residual life of the vehicle structural member when the vehicle structural member is damaged; further, through damage imaging, visualization, positioning and type judgment of the damage can be achieved, different types of damage in the vehicle structural part can be recognized and positioned, through damage visualization, a visual damage distribution diagram is provided for maintenance personnel, and the maintenance personnel can make reasonable maintenance decisions conveniently. When damage occurs, large self-repairing current can be provided for the sensor through the power supply module, a large amount of heat is generated at the damage position, the thermoplastic yarn is converted into a fluid solid state, and therefore self-repairing is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of vehicle safety and structural health monitoring, and relates to an on-line health monitoring and self-repair system and method for vehicle composite materials. Background Art

[0002] With the rapid development of the automotive industry, the requirements for vehicle performance are increasing day by day, especially the energy utilization efficiency and structural safety performance of automobiles. Automobile lightweighting has become an important way to achieve energy conservation, emission reduction and improve vehicle safety.

[0003] Fiber-reinforced resin-based composites have excellent characteristics such as high specific strength, high specific modulus and fatigue resistance. At the same time, they also have good designability and are an ideal choice for automobile lightweighting. However, visible damages such as perforation, cracking and corrosion and invisible damages such as delamination, microcracks and fiber fractures may occur during the service of composites. Secondly, there is a lack of efficient and convenient repair means and reasonable remaining life assessment methods for damages. Once damages occur, they can only be allowed to develop, and the remaining life of components cannot be defined until the components fail and are replaced. Moreover, these damages are not easy to cause changes in the shape of the composite structure before failure and are difficult to detect by visual inspection and tapping. For example, most automobile structural parts are inside the vehicle, resulting in the inability to observe the above damages in time, but the existence of these damages will seriously affect the safety and reliability of automobiles.

[0004] In traditional composite material health monitoring methods, for example, the embedding of fiber Bragg grating sensors will damage the integrity of composite material structural parts, resulting in a decline in structural performance; acoustic emission and fiber optic sensors are not robust and are easily interfered; the measurement cost and time cost of the ray detection method are relatively high and the dynamic monitoring ability has relatively high limitations; ultrasonic sensors use point measurement and the damage location needs to be predicted; for the health monitoring of structural parts, the space inside the vehicle is narrow and the layout is too compact; it is often limited by the real-time monitoring and the accuracy of damage quantification. These methods are often difficult to achieve comprehensive and continuous monitoring of damages to automobile structural parts, and it is difficult to accurately evaluate the degree and development trend of damages. After damages occur, only the damages can be monitored and no self-repair means can be provided. Summary of the Invention

[0005] In order to solve the problems of the above-mentioned prior art, the invention aims to provide an on-line health monitoring and self-repair system and method for vehicle composite materials to realize real-time monitoring, quantitative evaluation and self-repair of damages to vehicle structural parts, ensure the safety and reliability of the automobile structure, and provide and optimize maintenance strategies.

[0006] The invention is realized through the following technical solutions: In a first aspect, the present invention provides an on - vehicle composite material online health monitoring and self - repair system, comprising: an on - vehicle structural member, a power supply module, a data acquisition module, and a data processing module; the on - vehicle structural member includes multiple layers of hybrid woven fiber cloths molded together, and the hybrid woven fiber cloth is a fiber cloth woven from thermoplastic yarns and reinforcing fiber tows; sensors and multiple electrodes are arranged between the layers of the hybrid woven fiber cloth, and the electrodes are adhered to the periphery of the sensors with conductive silver glue; the sensor includes a hybrid woven fiber cloth and a conductive material coated on the hybrid woven fiber cloth; The power supply module, connected to the electrodes, is used to provide an excitation current for the sensors in the health monitoring state or a self - repair current for the sensors in the self - repair state; The data acquisition module, connected to the electrodes, is used to collect multiple voltage signals at different positions of the sensors and transmit them to the data processing module; The data processing module is used to convert the collected voltage signals into resistance values, compare the resistance values with the stored damage warning threshold and critical life value, judge whether the on - vehicle structural member is damaged or not according to the comparison result, estimate the remaining life when the on - vehicle structural member is damaged, and perform damage imaging based on multiple voltage signals, and classify the obtained damage imaging map to judge the damage type.

[0007] Preferably, the data processing module includes a data storage and processing module and a remote data storage and processing library; The data storage and processing module is used to convert multiple collected voltage signals into multiple resistance values; compare the multiple resistance values with the damage warning threshold and the critical life value. If at least one resistance value is equal to or greater than the critical life value, the judgment result is that the on - vehicle structural member is damaged. If all resistance values are less than the damage warning threshold, the judgment result is that the on - vehicle structural member is not damaged. Otherwise, the judgment result is that the on - vehicle structural member is damaged; send the judgment result and multiple resistance values to the remote data storage and processing library; The remote data storage and processing library is used to perform damage imaging based on multiple voltage signals when the judgment result is that the on - vehicle structural member is damaged, classify the obtained damage imaging map to judge the damage type; and evaluate the remaining life of the on - vehicle structural member according to the resistance values equal to or greater than the damage warning threshold.

[0008] Further, the remote data storage and processing library performs damage imaging according to the electrical impedance tomography algorithm based on multiple voltage signals.

[0009] Further, the remote data storage and processing library classifies the damage imaging map using a trained convolutional neural network to judge the damage type.

[0010] Further, the remote data storage and processing library evaluates the remaining life of the vehicle structural member according to the resistance value equal to or greater than the damage warning threshold and the resistance change law of the vehicle structural member; the resistance change law is obtained through a machine learning algorithm.

[0011] Preferably, the reinforcing fiber tow is a carbon fiber tow, a glass fiber tow, a basalt fiber tow or a Kevlar fiber tow.

[0012] Preferably, the vehicle structural member is a cross beam, a longitudinal beam, an A-pillar, a B-pillar, a C-pillar, a bumper beam, a leaf spring, a battery lower box or a battery box cover plate.

[0013] Preferably, the conductive material is a carbon nanotube.

[0014] Preferably, the power supply module is a generator or a motor.

[0015] In a second aspect, the present invention provides a method for online health monitoring and self-repair of a vehicle composite material, based on the online health monitoring and self-repair system for a vehicle composite material, including: Using a power supply module to provide an excitation current for the sensor; Using a data acquisition module to collect multiple voltage signals at different positions of the sensor; Using a data processing module to convert the collected voltage signals into resistance values, and comparing the resistance values with the stored damage warning threshold and critical life value, judging whether the vehicle structural member is damaged or not according to the comparison result, estimating the remaining life when the vehicle structural member is damaged, and performing damage imaging based on multiple voltage signals, and classifying the obtained damage imaging map to judge the damage type; When the vehicle structural member is damaged, using the power supply module to provide a self-repair current for the sensor, so that the vehicle structural member performs self-repair.

[0016] Compared with the prior art, the present invention has the following beneficial effects: In the system of the present invention, a conductive sensor is arranged between the layers of a hybrid woven fiber cloth composite material. During the health monitoring state, an excitation current is provided to the sensor, the voltage signals at different positions of the sensor are collected and converted into resistance values. When the vehicle structural component is damaged, the resistance value increases. Thus, the resistance value can reflect the damage condition of the vehicle structural component. Therefore, it is possible to judge whether the vehicle structural component is damaged or broken according to the comparison results of the resistance value with the damage warning threshold and the critical life value, and estimate the remaining life of the vehicle structural component when it is damaged; in terms of electrode layout, while not affecting the basic mechanical properties of the structural component, electrodes are arranged around the edge, which can not only achieve the qualitative determination of damage, but also further achieve damage imaging. Through damage imaging, the visualization, positioning, and type discrimination of damage can be realized, different types of damage in the vehicle structural component can be identified and located. Through damage visualization, an intuitive damage distribution map is provided for maintenance personnel, facilitating them to make reasonable maintenance decisions. At the same time, when damage occurs, a large self-repair current can be provided to the sensor through the power supply module. Since the resistance at the damaged part increases, according to Joule's law, a large amount of heat will be generated at the damaged part, and the thermoplastic yarn will turn into a flowing solid state, thus achieving self-repair. The system adopts a single-layer dual-functional integrated design, integrating the conductive sensor network and the thermoplastic self-repair material in the same fiber layer. The thermoplastic self-repair materials in different layers can also achieve the self-repair effect, further improving the integration of material functions, and supporting the self-verification of the repair effect through in-situ impedance measurement. The sensor of the present invention is integrally formed with the composite material, having non-invasiveness, ensuring the mechanical properties of the composite material; the voltage signal is used throughout the monitoring process and is inside the composite material, without the need to predict the damage position in advance, and without the need to remove the damaged component for monitoring, and the damage can be monitored in real time, having no radiation effect on the human body; the present invention can realize the damage monitoring and early warning of vehicle structural components and the assessment of the remaining life, improve the safety of the vehicle, avoid the vehicle structural component exceeding the service critical life, repair in time, extend the service life, and reduce the maintenance cost. The present invention can improve the intelligent level of the vehicle, contributing to the networking of the health monitoring information of vehicle structural components. The force resistance data of the vehicle body structure during an accident or normal driving can be obtained, providing data support for the safety and lightweight design of the vehicle body structure, and optimizing the vehicle body structure design. In short, the present invention can realize the online monitoring of the load and damage of vehicle structural components of the vehicle, contribute to the fault diagnosis and safety assessment of the vehicle body, provide safety guarantee for the operation of intelligent vehicles, provide data support for the lightweight structure digital twin modeling and collision accident reverse simulation, and form a complete technical closed-loop of monitoring - repair - prediction - optimization. Promote the intelligent development of the vehicle. Brief Description of the Drawings

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a block diagram of an on-line health monitoring and self-repair system for vehicle composite materials of the present invention.

[0019] Figure 2 It is a schematic diagram of the interlayer structure of the sensor of the present invention.

[0020] Figure 3 It is a schematic diagram of the damage of the vehicle structural member of the present invention.

[0021] Figure 4 It is a schematic diagram of the damage imaging of the vehicle structural member of the present invention.

[0022] Figure 5 It is a schematic diagram of the deep learning damage classification process of the vehicle structural member of the present invention.

[0023] Figure 6 It is a schematic diagram of the hybrid woven fiber cloth of the present invention.

[0024] The labels in the figure are explained as follows: 1 - vehicle structural member, 2 - power supply module, 3 - data acquisition module, 4 - data storage and processing module, 5 - remote data storage and processing library, 6 - driver, 7 - vehicle fault repair center, 8 - sensor, 9 - electrode, 10 - conductive silver paste, 11 - wire. Detailed implementation manners

[0025] The following specific examples illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0026] It should be noted that the process equipment or devices not specifically noted in the following embodiments all adopt conventional equipment or devices in the art.

[0027] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices. Moreover, unless otherwise specified, the numbering of each method step is only a convenient tool for identifying each method step, rather than a limitation on the arrangement order of each method step or the scope in which the present invention can be implemented. Changes or adjustments to their relative relationships, without substantial changes in the technical content, should also be regarded as the scope in which the present invention can be implemented.

[0028] Reference Figure 1 , the in-line health monitoring and self-repair system for vehicle-use composite materials described in the present invention includes: a vehicle-use structural member 1, a power supply module 2, a data acquisition module 3, and a data processing module; the vehicle-use structural member 1 includes multiple layers of hybrid woven fiber cloths molded together, and the hybrid woven fiber cloth is a fiber cloth woven from thermoplastic yarns and reinforcing fiber tows (as Figure 6 shown); sensors 8 and multiple electrodes 9 are arranged between the layers of the hybrid woven fiber cloth, and the electrodes 9 are adhered to the periphery of the sensors 8 with conductive silver paste 10 ( Figure 2 ); the sensors 8 include hybrid woven fiber cloths and conductive materials coated on the hybrid woven fiber cloths; The power supply module 2 is connected to the electrodes 9 and is used to provide an excitation current for the sensors 8 in the health monitoring state or a self-repair current for the sensors 8 in the self-repair state; The data acquisition module 3 is connected to the electrodes 9 and is used to collect multiple voltage signals at different positions of the sensors 8 and transmit them to the data processing module; The data processing module is used to convert the collected voltage signals into resistance values, compare the resistance values with the stored damage warning thresholds and critical life values, judge whether the vehicle-use structural member 1 is damaged or not according to the comparison results, estimate the remaining life when the vehicle-use structural member 1 is damaged, and perform damage imaging based on multiple voltage signals, and classify the obtained damage imaging diagrams to judge the damage types.

[0029] The thermoplastic yarns include, but are not limited to, polyester (PET) thermoplastic yarns, nylon (PA) thermoplastic yarns, polylactic acid (PLA) yarns, polyethylene (PE) thermoplastic yarns, polyimide (PI) thermoplastic yarns, thermoplastic elastomer (TPE) yarns, polyurethane (PU) thermoplastic yarns.

[0030] Assume the number of electrodes is N , and they are numbered from one to Nnumber. The negative and positive electrodes of the power supply module 2 apply current to adjacent electrode pairs in sequence. That is, each time power is supplied, the negative and positive electrodes are respectively connected to the i-th electrode and the (i + 1)-th electrode, where i = 1, 2, …, N - 1. Specifically, for example, starting from the 1st electrode and the 2nd electrode, the negative and positive electrodes move backward in sequence. That is, after the power supply to the 1st electrode and the 2nd electrode is completed, the power supply is changed to the 2nd electrode and the 3rd electrode, and so on. When the power supply module 2 applies current to the i-th electrode and the (i + 1)-th electrode, the data acquisition module 3 measures the voltages between subsequent electrode pairs in sequence, that is, measures the voltages between the (i + 1)-th electrode and the (i + 2)-th electrode, the (i + 3)-th electrode and the (i + 4)-th electrode, …, the (N - 1)-th electrode and the N-th electrode. Through this method, a total of N ×( N- 3) boundary voltage values can be obtained to complete a full data acquisition. In the present invention, the power supply module 2 provides a small excitation current to the sensor 8, the data acquisition module 3 acquires the voltage signal of the sensor 8 and converts it into a resistance value, and determines whether the vehicle structural member 1 is damaged or impaired according to the magnitude relationship between the resistance value and the damage warning threshold and the critical life value, and estimates the remaining life when the vehicle structural member 1 is damaged. The mechanism lies in that the hybrid woven fiber cloth inside the vehicle structural member 1 and the sensor 8 are integrally formed by a hot molding process to form a self-sensing structure. When the vehicle structural member 1 is subjected to an external force, the hybrid woven fiber cloth will deform, and this deformation will be transmitted to the sensor 8 through the hot molding process, resulting in a change in the resistance value of the sensor 8. Specifically, when the hybrid woven fiber cloth is damaged, to a certain extent, the conductive path of the sensor 8 is cut off, thereby increasing the resistance. By monitoring the change in the resistance value, it can be determined whether the vehicle structural member 1 is damaged. Further, when the resistance value exceeds the damage warning threshold, it can be determined that the vehicle structural member 1 is damaged.

[0031] The self - repair function of the vehicle structural member 1 described in the present invention is intelligently regulated by the change of the resistance value. When the vehicle structural member 1 is damaged, the resistance value of the damaged area will increase significantly. When the resistance value exceeds the damage warning threshold, the warning system is triggered. The power supply module 2 provides a larger self - repair current according to the resistance value measured by the sensor 8. The specific ranges of the excitation current and the self - repair current need to be calibrated through material system experiments. Usually, the excitation current is set to 10 - 50 mA, which is used for the low - power consumption detection current to maintain the sensing function. The magnitude of the self - repair current is dynamically adjusted according to the phase - change temperature and damage scale of the thermoplastic yarn, and the typical range is 0.5 - 5 A. When the sensor 8 detects that it exceeds the critical life value, the system switches to the high - power self - repair mode and applies a current of 1 - 3 A. According to Joule's law, an instantaneous temperature rise of the phase - change temperature of the self - repair material will be generated in the damaged area, triggering the melting and flowing of the thermoplastic yarn. The PID algorithm can maintain a constant - temperature repair environment for the self - repair material to avoid overheating damage to the matrix. In electrical impedance tomography (EIT), the apparent expansion of the damaged area is a typical feature of the repair process. When the boundary of the expanded damaged area no longer expands within 3 consecutive heating - cooling cycles, the repair can be terminated.

[0032] The damage warning threshold and the critical life value described in the present invention are obtained and stored in the following way: Based on tests and numerical simulations, the corresponding relationship between the force characteristic curve and the resistance change rate when the vehicle structural member is damaged is obtained, that is, the resistance value corresponding to the end point of the elastic deformation section of the vehicle structural member is determined. , combined with the resistance change rate formula (1), substitute with , and the obtained resistance change rate is defined as the damage warning threshold of the corresponding vehicle structural member; then, based on the historical data, empirical formulas, experimental studies, industry practices, etc. of different vehicle structural members, the safety factor n of different vehicle structural members is determined. Combined with the allowable stress formula (2), the allowable stress of the corresponding vehicle structural member is obtained. , considering the stresses that different vehicle structural members will be subjected to during the vehicle service process, corresponding experiments and simulation simulations are carried out to obtain the resistance value of the vehicle structural member when it reaches the allowable stress. , combined with the resistance change rate formula (1), substitute with , and the obtained is defined as the life threshold of the corresponding vehicle structural member, called the critical life value. For the resistance change between the damage warning threshold and the critical life value, in the force - resistance change rate curve, the damage warning threshold and the critical life value are identified by the second - derivative method, a label data set is constructed, key features are extracted from the experimental curve, a remaining strength mapping model is constructed, 80% of the data set is used as the training data set, 20% is used as the verification data set, and the remaining strength coefficient formula (3) is defined to quantitatively specify the intermediate life.

[0033]

[0034] Among them, is the resistance change rate; is the difference between the initial resistance and the resistance after damage; is the initial resistance;

[0035] Among them, is the allowable stress; is the safety factor; is the ultimate tensile strength of the corresponding vehicle structural member.

[0036]

[0037] Among them, is the remaining strength coefficient; ; ;

[0038] The data processing module described in the present invention specifically includes a data storage and processing module 4 and a remote data storage and processing library 5; The data storage and processing module 4 is used to convert a plurality of collected voltage signals into a plurality of resistance values; compare the plurality of resistance values with a damage warning threshold and a critical life value. If at least one resistance value is equal to or greater than the critical life value, it is determined that the vehicle structural member 1 is damaged. If all resistance values are less than the damage warning threshold, it is determined that the vehicle structural member 1 is not damaged. Otherwise, it is determined that the vehicle structural member 1 is damaged; send the determination result and the plurality of resistance values to the remote data storage and processing library 5; The remote data storage and processing library 5 is used to perform damage imaging according to a plurality of voltage signals when the determination result is that the vehicle structural member 1 is damaged, classify the obtained damage imaging diagram to determine the damage type; and evaluate the remaining life of the vehicle structural member 1 according to the resistance value equal to or greater than the damage warning threshold.

[0039] The data storage and processing module 4 is used to store the voltage signal change data in the short term, as well as the damage warning threshold and the critical life value. When the resistance value corresponding to the voltage signal exceeds the damage warning threshold, it is determined that the vehicle structural member 1 is damaged, and the warning information is sent to the remote data storage and processing library 5 through the vehicle network, which notifies the driver 6, and the driver 6 decides whether to perform self-repair. When the resistance value is greater than or equal to the critical life value, it is determined that the vehicle structural member is completely damaged. At this time, the remote data storage and processing library 5 will notify both the vehicle fault repair center 7 and the driver 6. All the evaluation data of the vehicle structural member 1 is wirelessly transmitted to the remote data storage and processing library 5 through the vehicle network.

[0040] The remote data storage and processing library 5 is the data center at the back end, consisting of several computers and an expert group, and is used for long-term storage and processing of the health monitoring data of vehicle structural components. It transmits the comprehensive evaluation information of the obtained damage imaging map, damage type, and remaining life to the driver 6 and the vehicle fault repair center 7, providing data support for the driver 6 to understand the damage status and for vehicle damage repair or replacement.

[0041] Specifically, the remote data storage and processing library 5 performs damage imaging using the electrical impedance tomography algorithm based on multiple voltage signals, which can be referred to Figure 3 and Figure 4 as shown.

[0042] The electrical impedance tomography algorithm is the generalized vector pattern matching method, and its formula is:

[0043] where, represents the sensitivity matrix, represents the conductivity matrix, represents the voltage vector, and the above formula is normalized:

[0044] where (' ) represents normalization.

[0045] Calculate the initial value of the conductivity iteration :

[0046] Calculate the voltage vector obtained in the th iteration:

[0047] Normalize the voltage vector in the th iteration:

[0048] Calculate the and difference vector:

[0049] The conductivity distribution in the th iteration is:

[0050] Let , and continue iterating the voltage vector until the The iterative particle distribution of the objective function is as follows:

[0051] As the number of iterations increases, the voltage vector of the nth iteration and the initial voltage vector have an increasingly smaller included angle, and the objective function F will gradually approach 1. The initial voltage vector is the voltage signal collected by the data acquisition module 3.

[0052] As Figure 5 shown, the remote data storage and processing library 5 of the present invention uses a trained convolutional neural network to classify the damage imaging map to determine the damage type. First, a parametric finite element model is established in simulation software to simulate 3 types of typical damages, namely delamination, perforation, crack and their composite forms, covering different characteristic scales, such as crack length, delamination area, aperture size, to generate mapping data including voltage signal - EIT image - damage label. The convolutional neural network is trained with the mapping data, and the model is verified with the data obtained from experiments, and then the damage imaging map obtained from experiments is classified to determine the damage type.

[0053] The sensor 8 of the present invention is integrally formed by a molding technique between hybrid woven fiber cloths, and has non-invasiveness and will not damage the hybrid woven fiber cloth of the composite material.

[0054] The vehicle structural member 1 described in the present invention refers to a structural member that is damaged and deformed during the driving of the vehicle and will seriously endanger the safety of relevant personnel and the driving performance of the vehicle, including but not limited to cross beams, longitudinal beams, A-pillars, B-pillars, C-pillars, anti-collision beams, leaf springs, battery lower boxes, battery box covers, etc.

[0055] The reinforcing fiber bundles described in the present invention include but are not limited to carbon fiber bundles, glass fiber bundles, basalt fiber bundles, Kevlar fiber bundles, etc.

[0056] In some specific embodiments of the present invention, the sensor 8 is prepared by using carbon nanotubes as the conductive material and hybrid woven fiber cloth as the supporting material, in combination with an electrophoretic coating technique. The specific preparation method is as follows: fix the hybrid woven fiber cloth on the positive electrode plate, deposit the carboxyl group-bearing carbon nanotubes in the electrophoretic solution onto the hybrid woven fiber cloth on the positive electrode to endow the hybrid woven fiber cloth with conductivity, and then smear the carbon nanotubes on the hybrid woven fiber cloth evenly and dry at room temperature for 36 - 48 hours.

[0057] In some specific embodiments of the present invention, the power supply module 2 is an engine or a motor on the vehicle where the vehicle structural member 1 is located.

[0058] In the present invention, a data storage and processing module 4 is connected to a data acquisition module 3 through a CAN bus interface; a remote data storage and processing library 5 performs wireless communication and data transmission with the data storage and processing module 4 through the vehicle Internet of Things; a driver 6 and a vehicle fault repair center 7 obtain comprehensive evaluation information of a vehicle structural member 1 from the remote data storage and processing library 5 through a wireless communication network; the driver and the vehicle fault repair center perform self-repair and replacement on the vehicle structural member according to the comprehensive evaluation information provided by the remote data storage and processing library 5.

[0059] The wire 11 described in the present invention is a commercially available product, and it has two uses: First, it is connected to and fixed to the electrodes 9 around the sensor 8 to transmit the voltage signal of the sensor 8 to the data acquisition module 3; Second, it is connected to the power supply module 2 to provide an excitation current for the sensor 8.

[0060] The present invention realizes the collaborative design of structure and function by embedding a conductive sensor network between the layers of a hybrid woven fiber cloth composite material. The system adopts a single-layer dual-functional integrated architecture, in which the conductive sensor network and the thermoplastic self-healing material are in-situ composite in the multi-layer hybrid woven fiber cloth. On the premise of ensuring the mechanical properties of the material, the voltage signal is collected in real time through the circumferentially distributed electrode network and the conductivity distribution is calculated. When the vehicle structural member is damaged, the resistance value increases significantly to trigger a damage warning: Based on the limitation of the damage warning threshold and the critical life value, the damage development stage can be determined and the remaining life can be predicted; at the same time, the damage is visually located and the type is discriminated through electrical tomography technology, and typical damage modes such as delamination, cracks, and perforations can be identified, and a damage imaging map is generated to facilitate the maintenance personnel to carry out maintenance and repair. The self-healing mechanism realizes closed-loop control through the Joule heat effect: When the damage resistance exceeds the critical life value, the system automatically switches to the self-healing current, so that the temperature in the damage area rises to the phase change point of the thermoplastic material in a short time without damaging the matrix material, and the molten material fills the damage area through capillary action and cools to repair. This design breaks through the limitations of traditional layered and multi-layer electrode systems. The electrodes are distributed around the edge, and the power supply module and the data acquisition module both play roles through the electrodes, reducing the number of electrode arrangements, optimizing the arrangement scheme, and realizing full-domain coverage detection. The repair effect can be verified in-situ. The system is deeply integrated into the automotive intelligent system. By obtaining real-time structure force-electricity coupling data, it not only provides a digital twin modeling basis for lightweight design, but also supports reverse simulation analysis of collision accidents, forming a closed-loop of full-life cycle management of monitoring-repair-prediction-optimization, which can reduce the maintenance cost of key structural members, extend the service life, and provide core technical guarantee for the safe operation of intelligent vehicles.

[0061] In summary, the present invention utilizes electrical impedance tomography technology and resin-based hybrid woven fiber cloth self-healing technology, which can predict the remaining strength and life of vehicle structural components, increase the service life of vehicle structural components, greatly improve the safety of vehicle structural components, reduce the use cost of vehicle structural components, and has the characteristics of non-invasiveness, no radiation, strong robustness, and real-time monitoring. Through data processing and imaging algorithms combined with deep learning technology, visualization, localization, type discrimination, and quantitative evaluation of damage can be achieved. The monitoring system of the present invention can identify and locate different types of damage in composites. By real-time monitoring of the damage development trend, the system can predict the remaining service life of the structure. Through damage visualization, it provides intuitive damage distribution maps and convenient and efficient self-healing means for maintenance personnel, helps to adopt reasonable maintenance strategies, and provides scientific decision-making and technical support for the maintenance and repair of vehicles. The present invention not only improves the safety and reliability of vehicles, but also provides strong technical support for vehicle lightweighting, performance improvement, and self-healing, and has important practical value and broad application prospects.

[0062] Although the present invention has been described above with reference to embodiments, various improvements can be made thereto and components thereof can be replaced with equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the disclosed embodiments of the present invention can be combined with each other in any manner. The reason for not exhaustively describing the situations of these combinations in this specification is only to save space and resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. An on-line health monitoring and self-repair system for vehicle composite materials, characterized in that, Including: A vehicle structural member (1), a power supply module (2), a data acquisition module (3), and a data processing module; The vehicle structural member (1) includes a multi-layer hybrid woven fiber cloth molded together. The hybrid woven fiber cloth is a fiber cloth woven from thermoplastic yarns and reinforcing fiber tows. Sensors (8) and a plurality of electrodes (9) are arranged between the layers of the hybrid woven fiber cloth. The electrodes (9) are adhered to the periphery of the sensors (8) with conductive silver paste (10). The sensors (8) include a hybrid woven fiber cloth and a conductive material coated on the hybrid woven fiber cloth; The power supply module (2), connected to the electrodes (9), is used to provide an excitation current for the sensors (8) in the health monitoring state or a self-repair current for the sensors (8) in the self-repair state; The data acquisition module (3), connected to the electrodes (9), is used to collect a plurality of voltage signals at different positions of the sensors (8) and transmit them to the data processing module; The data processing module is used to convert the collected voltage signals into resistance values, compare the resistance values with the stored damage warning threshold and critical life value, judge whether the vehicle structural member (1) is damaged or not according to the comparison result, estimate the remaining life when the vehicle structural member (1) is damaged, and perform damage imaging based on a plurality of voltage signals, and classify the obtained damage imaging map to judge the damage type.

2. The on-line health monitoring and self-repair system for vehicle composite materials according to claim 1, wherein The data processing module includes a data storage and processing module (4) and a remote data storage and processing library (5); The data storage and processing module (4) is used to convert a plurality of collected voltage signals into a plurality of resistance values; compare the plurality of resistance values with the damage warning threshold and the critical life value. If at least one resistance value is equal to or greater than the critical life value, the judgment result is that the vehicle structural member (1) is damaged. If all resistance values are less than the damage warning threshold, the judgment result is that the vehicle structural member (1) is not damaged. Otherwise, the judgment result is that the vehicle structural member (1) is damaged; send the judgment result and the plurality of resistance values to the remote data storage and processing library (5); The remote data storage and processing library (5) is used to perform damage imaging based on a plurality of voltage signals when the judgment result is that the vehicle structural member (1) is damaged, classify the obtained damage imaging map to judge the damage type; and evaluate the remaining life of the vehicle structural member (1) according to the resistance values equal to or greater than the damage warning threshold.

3. The on-line health monitoring and self-repair system for vehicle composite materials according to claim 2, characterized in that, The remote data storage and processing library (5) performs damage imaging using the electrical impedance tomography algorithm based on a plurality of voltage signals.

4. The on-line health monitoring and self-repair system for vehicle composite materials according to claim 2, characterized in that, The remote data storage and processing library (5) uses a trained convolutional neural network to classify the damage imaging map to judge the damage type.

5. The on-line health monitoring and self-repair system for vehicle composite materials according to claim 2, wherein, The remote data storage and processing library (5) evaluates the remaining life of the vehicle structural member (1) according to the resistance values equal to or greater than the damage warning threshold and the resistance change law of the vehicle structural member (1); the resistance change law is obtained through a machine learning algorithm.

6. The on-line health monitoring and self-repair system for vehicle composite materials according to claim 1, characterized in that The reinforcing fiber tows are carbon fiber tows, glass fiber tows, basalt fiber tows or Kevlar fiber tows.

7. The on-line health monitoring and self-repair system for vehicle composite materials according to claim 1, characterized in that, The vehicle structural member (1) is a crossbeam, a longitudinal beam, an A-pillar, a B-pillar, a C-pillar, a crash beam, a leaf spring, a battery lower box or a battery box cover plate.

8. The on-line health monitoring and self-repair system for vehicle composite materials according to claim 1, characterized in that, The conductive material is a carbon nanotube.

9. The on-line health monitoring and self-repair system for vehicle composite materials according to claim 1, characterized in that The power supply module (2) is a generator or a motor.

10. An online health monitoring and self-repair method for vehicle-use composite materials, characterized in that, The in-line health monitoring and self-repair system for vehicle composite materials according to claim 1, comprising: Using the power supply module (2) to provide an excitation current for the sensor (8); Using the data acquisition module (3) to acquire multiple voltage signals at different positions of the sensor (8); Using the data processing module to convert the acquired voltage signals into resistance values, and comparing the resistance values with the stored damage warning threshold and critical life value, judging whether the vehicle structural member (1) is damaged or not according to the comparison result, predicting the remaining life when the vehicle structural member (1) is damaged, and performing damage imaging based on multiple voltage signals, and classifying the obtained damage imaging diagram to judge the damage type; When the vehicle structural member (1) is damaged, using the power supply module (2) to provide a self-repair current for the sensor (8), so that the vehicle structural member (1) performs self-repair.

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