Preventive inspection and maintenance method, system and equipment based on airport apron high-value special vehicle and unpowered support equipment

Abstract

The invention belongs to the field of flaw detection, and particularly relates to a preventive inspection and maintenance method, system and equipment based on airport apron high-value special vehicles and unpowered guarantee equipment. The objective of the invention is to solve the problems of poor detection accuracy in a severe environment, difficulty in signal receiving caused by strong noise interference, and influence on detection accuracy when a complex material structure is processed. The method comprises the following steps: manufacturing a flaw detection probe based on an included angle and a thickness between a piezoelectric wafer and the bottom surface of a wedge block, integrating a temperature sensor and a humidity sensor, detecting equipment to be detected by using the flaw detection probe, carrying out filtering processing on a signal, compensating an ultrasonic propagation speed and attenuation of an ultrasonic signal, and obtaining damage data. And extracting problem feature data, obtaining a damage classification result and a damage degree result, and maintaining the to-be-detected equipment. According to the invention, the ultrasonic detection precision is improved, the environmental adaptability is enhanced, the maintenance cost is reduced, the maintenance process is optimized, and the safety of personnel and aircrafts is guaranteed.

Description

Background Art

[0002] In the modern civil aviation industry, in order to ensure the efficient and safe operation of airports, a large number of special vehicles and non-powered support equipment are needed to support daily operations. These equipment include but are not limited to lift trucks, baggage transfer trucks and ladder trucks. They are used very frequently in large airports, and can be used to remove aircraft thousands of times a day. However, with the increase in the frequency of use, the aging problem of special vehicles and equipment has become increasingly prominent, especially the aging of the door switch of the lift truck, internal cracking and other problems may cause the door to pop out accidentally, which in turn may cause safety hazards such as collision with aircraft. Such accidents not only threaten the safety of personnel, but may also cause significant economic losses to airlines.

[0003] At present, the inspection and maintenance of special vehicles and equipment mainly rely on manual visual inspection and conventional instrument detection. Although these methods can detect surface damage and obvious faults of equipment to a certain extent, there is a large detection blind spot for subtle cracks and damage that are difficult to observe inside the equipment, and it is impossible to effectively predict and prevent early faults. This greatly limits the application effect of existing detection methods, especially under high-frequency use conditions, and it is difficult to fully guarantee the safety and reliability of special vehicles.

[0004] The test results of existing conventional testing instruments are extremely inaccurate under the following conditions:

[0005] Harsh environmental interference: The apron operating environment is complex and often faces extreme weather conditions such as high temperature (up to 50°C), low temperature (as low as -20°C), rain, snow and high humidity. In addition, pollutants emitted by aircraft engines at idle speed, such as kerosene molecules, may have an adverse effect on the detection process. Therefore, the detection system is required to have good environmental adaptability and anti-interference capabilities.

[0006] Strong noise: The high noise level on the apron (about 70 decibels) will challenge the signal reception of the ultrasonic flaw detector, reduce the signal-to-noise ratio, and increase the difficulty of defect identification and location. In such an environment, traditional detection methods often have difficulty in obtaining accurate results.

[0007] Handling complex material structures: Special vehicles and equipment are often made of a variety of materials and complex structures, such as composite materials and ultra-thin high-strength steel with a thickness of 1mm. These material and structural characteristics make traditional ultrasonic flaw detection technology encounter obstacles in signal transmission and reception, resulting in the accuracy of internal defect detection being affected. In addition, the complex geometry of the vehicle structure also increases the difficulty of detection.

[0008] Based on this, the present invention proposes a preventive inspection and maintenance method, system and equipment based on high-value special vehicles and non-powered support equipment on the apron. Summary of the invention

[0009] In order to solve the above-mentioned problems in the prior art, namely, it is difficult to detect internal fine cracks and damages by manual inspection and conventional instrument inspection, the detection accuracy is poor in harsh environments, strong noise interference leads to difficulty in signal reception, and the detection accuracy is affected when processing complex material structures, the present invention provides a preventive inspection and maintenance method, system and equipment based on apron high-value special vehicles and non-powered support equipment.

[0010] The first aspect of the present invention proposes a preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment, the method comprising:

[0011] Based on the detection refraction angle required by the material of the device to be inspected, the angle between the piezoelectric chip and the bottom surface of the wedge block in the flaw detection probe is obtained; based on the sound velocity and working frequency of the piezoelectric chip, the thickness of the piezoelectric chip is calculated; based on the angle and the thickness, the flaw detection probe is manufactured;

[0012] A temperature sensor and a humidity sensor are integrated in the flaw detection probe, and the temperature and humidity in the current detection environment are obtained based on the temperature sensor and the humidity sensor;

[0013] The device to be inspected is inspected using a flaw detection probe, the signal is filtered during the inspection process, and the ultrasonic propagation speed is compensated according to the temperature, and the attenuation of the ultrasonic signal is compensated according to the humidity, so as to obtain the inspection data; the damage wave in the inspection data is screened out as the damage data;

[0014] The damage data is preprocessed and problem feature data is extracted. The problem feature data is input into a pre-built and trained classifier to obtain damage classification results and damage degree results, and the equipment to be inspected is maintained.

[0015] In some preferred embodiments, the signal is filtered during the detection process, and the method is as follows:

[0016]

[0017] Among them, y(n) is the filtered signal, x(n) is the input signal, and a i and b i are the filter coefficients, and N and M are the filter orders.

[0018] In some preferred embodiments, the ultrasonic wave propagation velocity is compensated according to temperature, and the method is as follows:

[0019] V(T)=V 0 (1+p(TT 0 ));

[0020] Where V(T) is the ultrasonic wave propagation velocity at temperature T, V 0 is the reference temperature T 0 The propagation velocity under the condition, p is the temperature coefficient.

[0021] In some preferred embodiments, the attenuation of the ultrasonic signal is compensated according to humidity, and the method is as follows: The attenuation of the ultrasonic signal is compensated according to humidity, and the method is as follows:

[0022] A(H)=A 0 exp(-βH);

[0023] Where A(H) is the signal attenuation at humidity H, A 0 is the attenuation at reference humidity, and β is the humidity coefficient.

[0024] In some preferred embodiments, the flaw detection probe is used to detect the device to be detected, and the method is as follows:

[0025] Move the ultrasonic flaw detector to the vicinity of the object to be inspected, and adjust the parameters of the oscilloscope device based on the manufactured flaw detection probe;

[0026] Add a set amount of coupling agent to the part to be tested on the device to be tested, place the flaw detection probe on the surface of the part to be tested, make the flaw detection probe close to the surface, pull the flaw detection probe to make it in close contact with the surface of the part to be tested and move in a zigzag shape.

[0027] In some preferred embodiments, the classification results include mild injury, moderate injury and severe injury.

[0028] In some preferred embodiments, the mild injury, moderate injury and severe injury are classified based on the percentage of the injury area to the whole area.

[0029] Another aspect of the present invention provides a preventive inspection and maintenance system based on apron high-value special vehicles and non-powered support equipment, and a preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment, the method comprising:

[0030] A flaw detection probe design module is configured to obtain the angle between the piezoelectric chip and the bottom surface of the wedge in the flaw detection probe based on the detection refraction angle required by the material of the device to be inspected; calculate the thickness of the piezoelectric chip according to the sound velocity and working frequency of the piezoelectric chip; and manufacture the flaw detection probe based on the angle and the thickness;

[0031] A parameter acquisition module, which is configured to integrate a temperature sensor and a humidity sensor in the flaw detection probe, and acquire the temperature and humidity in the current detection environment based on the temperature sensor and the humidity sensor;

[0032] A damage data acquisition module is configured to use a flaw detection probe to detect the device to be detected, filter the signal during the detection process, compensate for the ultrasonic propagation speed according to temperature, and compensate for the attenuation of the ultrasonic signal according to humidity to obtain the detection data; and filter out the damage wave in the detection data as the damage data;

[0033] The detection module is configured to pre-process the damage data and extract problem feature data, input the problem feature data into a pre-built and trained classifier, obtain damage classification results and damage degree results, and maintain the equipment to be detected.

[0034] According to a third aspect of the present invention, an electronic device is provided, comprising:

[0035] at least one processor; and

[0036] a memory communicatively connected to at least one of the processors; wherein,

[0037] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment.

[0038] In a fourth aspect of the present invention, a computer-readable storage medium is proposed, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment.

[0039] Beneficial effects of the present invention:

[0040] Improve detection accuracy: Through customized flaw detection probe design, combined with temperature and humidity compensation algorithms, the accuracy of ultrasonic detection is significantly improved, which can accurately detect subtle cracks and damage inside the equipment and reduce the risk of misjudgment due to detection errors.

[0041] Enhanced environmental adaptability: The present invention takes into special consideration the harsh working environment of the apron, such as extreme temperature, humidity changes and high noise levels. Through built-in temperature sensors and humidity sensors, as well as corresponding compensation algorithms, the stability and reliability of the detection system are ensured and are not affected by external environmental factors.

[0042] Reduce maintenance costs: By discovering potential damage to equipment early, maintenance measures can be taken in time to prevent small problems from turning into major failures, reduce unplanned downtime and maintenance costs, and extend the service life of the equipment.

[0043] Optimize maintenance process: Use pre-built and trained classifiers to automatically analyze damage data and quickly determine the extent and type of damage, providing a scientific basis for subsequent maintenance decisions and improving the efficiency and pertinence of maintenance work.

[0044] Ensure the safety of personnel and aircraft: Through effective preventive inspection and maintenance, safety accidents caused by equipment failure are reduced, the personal safety of staff is protected, and potential damage to aircraft is avoided, ensuring the normal operation of flights.

[0045] Improve airport operation efficiency: Reduce delays or flight cancellations due to equipment failures, improve the overall operation efficiency and service quality of the airport, and enhance customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0047] Figure 1 It is a flow chart of a preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment of the present invention;

[0048] Figure 2 It is a structural diagram of a computer system of a server for implementing the method, system, and device embodiments of the present application. DETAILED DESCRIPTION

[0049] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the relevant invention are shown in the accompanying drawings.

[0050] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0051] The first embodiment of the present invention provides a preventive inspection and maintenance method based on high-value special vehicles and non-powered support equipment on the apron, the method comprising:

[0052] Based on the detection refraction angle required by the material of the device to be inspected, the angle between the piezoelectric chip and the bottom surface of the wedge block in the flaw detection probe is obtained; based on the sound velocity and working frequency of the piezoelectric chip, the thickness of the piezoelectric chip is calculated; based on the angle and the thickness, the flaw detection probe is manufactured;

[0053] A temperature sensor and a humidity sensor are integrated in the flaw detection probe, and the temperature and humidity in the current detection environment are obtained based on the temperature sensor and the humidity sensor;

[0054] The device to be inspected is inspected using a flaw detection probe, the signal is filtered during the inspection process, and the ultrasonic propagation speed is compensated according to the temperature, and the attenuation of the ultrasonic signal is compensated according to the humidity, so as to obtain the inspection data; the damage wave in the inspection data is screened out as the damage data;

[0055] The damage data is preprocessed and problem feature data is extracted. The problem feature data is input into a pre-built and trained classifier to obtain damage classification results and damage degree results, and the equipment to be inspected is maintained.

[0056] In order to more clearly explain the preventive inspection and maintenance method of the present invention based on the high-value special vehicles and non-powered support equipment of the apron, the following is combined with Figure 1 The embodiments of the present invention are described in detail as follows:

[0057] Based on the detection refraction angle required by the material of the device to be inspected, the angle between the piezoelectric chip and the bottom surface of the wedge block in the flaw detection probe is obtained; based on the sound velocity and working frequency of the piezoelectric chip, the thickness of the piezoelectric chip is calculated; based on the angle and the thickness, the flaw detection probe is manufactured;

[0058] The present invention selects a new cutting-edge piezoelectric material PMN-PT (lead magnesium niobate-lead titanate), which has a higher electromechanical coupling coefficient and piezoelectric constant than traditional PZT materials, and has a higher electromechanical coupling coefficient and stability.

[0059] The wedge design must ensure that the ultrasonic wave enters the material under test at a set angle. According to Snell's law, the relationship between the incident angle and the refraction angle is:

[0060]

[0061] Among them, θ i is the incident angle, θ t is the refraction angle, v i and v t They are the sound speed in the wedge material and the material to be tested, respectively. The material to be tested is steel, whose sound speed is 5600m / s, and the wedge material is plexiglass (the sound speed is 2700m / s).

[0062] In order to design a suitable wedge angle, the present invention realizes a refraction angle of 70° in the test material with complex geometric structures of some special vehicles, and the incident angle is calculated by Snell's law as follows:

[0063]

[0064] The wedge is designed to ensure that the angle between the piezoelectric chip and the bottom surface of the wedge is 29.57°

[0065] The geometric dimensions of the probe affect its resolution and sensitivity. The relationship between the thickness d of the piezoelectric chip and the operating frequency is:

[0066]

[0067] Among them, v p is the sound velocity of the piezoelectric material. For PMN-PT material, its sound velocity is about 4200m / s. If the operating frequency f is 5MHz, the thickness d of the piezoelectric chip is about 0.42mm.

[0068] The present invention adopts a multi-layer polymer matching layer design, using a combination of PVDF (polyvinylidene fluoride) and other high-impedance polymer layers. Assuming that the thickness of each polymer layer is designed to be a quarter wavelength:

[0069] PVDF layer (sound velocity v PVDF ≈2300m / s):

[0070]

[0071] High impedance polymer layer (acoustic velocity v poly ≈3000m / s):

[0072]

[0073] The device to be inspected is inspected using a flaw detection probe, the signal is filtered during the inspection process, and the ultrasonic propagation speed is compensated according to the temperature, and the attenuation of the ultrasonic signal is compensated according to the humidity, so as to obtain the inspection data; the damage wave in the inspection data is screened out as the damage data;

[0074] The present invention uses a flaw detection probe to detect the device to be detected, and the method is as follows:

[0075] Move the ultrasonic flaw detector to the vicinity of the object to be inspected, and adjust the parameters of the oscilloscope device based on the manufactured flaw detection probe;

[0076] Add a set amount of coupling agent to the part to be tested on the device to be tested, place the flaw detection probe on the surface of the part to be tested, make the flaw detection probe close to the surface, pull the flaw detection probe to make it in close contact with the surface of the part to be tested and move in a zigzag shape.

[0077] The present invention designs an adaptive filtering algorithm to dynamically adjust the filtering parameters according to the real-time detection environment to reduce the interference of high-noise environments on ultrasonic signals. The formula is as follows:

[0078]

[0079] y(n) is the filtered signal, x(n) is the input signal, and a i and b i are the filter coefficients, and N and M are the filter orders.

[0080] Temperature compensation:

[0081] A temperature sensor is integrated in the probe device to monitor the temperature changes of the probe and the material to be tested in real time. The ultrasonic propagation speed and attenuation parameters are adjusted through the temperature compensation algorithm. The formula is as follows:

[0082] V(T)=V 0 (1+a(TT 0 ));

[0083] V(T) is the ultrasonic wave propagation velocity at temperature T, V 0 is the reference temperature T 0 The propagation speed under the condition of , a is the temperature coefficient. According to the above algorithm, when the ambient temperature is 50℃, the sound speed compensation is +0.18m / s; when the ambient temperature is minus 20℃, the compensation is -0.24m / s.

[0084] Humidity compensation

[0085] A humidity sensor is integrated into the probe device to monitor the ambient humidity in real time. The attenuation and reflection parameters of the ultrasonic signal are adjusted through the humidity compensation algorithm. The formula is as follows:

[0086] A(H)=A 0 exp(-βH);

[0087] A(H) is the signal attenuation at humidity H, A 0 is the attenuation under the reference humidity, and β is the humidity coefficient. The application of PMN-PT material is combined to improve the coupling effect in high humidity environment. According to the above algorithm, for example, in an environment with a humidity of 70%, the attenuation of the sound wave is about 0.08706dB / m, and the speed of sound in the air is about 347.318m / s.

[0088] The damage data is preprocessed and problem feature data is extracted. The problem feature data is input into a pre-built and trained classifier to obtain damage classification results and damage degree results, and the equipment to be inspected is maintained.

[0089] The preprocessing method includes at least data cleaning, denoising and standardization.

[0090] The problem characteristic data includes the size, shape, material properties and damage condition of the equipment.

[0091] The classification results in this embodiment include mild damage, moderate damage and severe damage. The mild damage, moderate damage and severe damage are classified based on the percentage of the damaged area to the whole. Specifically, if the damaged part exceeds 5% or less of the part, the damaged part is mild damage, if the damaged part exceeds more than 5% and less than 10% of the part, the damaged part is moderate damage, and if the damaged part exceeds more than 10% of the part, the damaged part is severe damage.

[0092] If the damage to the workpiece is slight, the damaged area is marked as a repairable abnormality. If the damage is moderate or severe, the damaged area is marked as an unrepairable abnormality. For repairable abnormalities, the objects in the damaged area are repaired. For unrepairable abnormalities, the components in the damaged area are directly replaced.

[0093] In the present invention, the detection steps are repeated until no damage is detected in the repaired workpiece.

[0094] The classifier used in this embodiment can be constructed based on support vector machine SVM and SMO optimization algorithm, introduction of polynomial kernel function, logistic regression or decision tree.

[0095] When the classifier is constructed by a support vector machine, the specific steps are as follows: The goal of SVM is to find a hyperplane that can maximize the separation of data of different categories. Its basic formula is:

[0096] f(x) = w·x+b;

[0097] Among them, f(x) is the hyperplane function, w is the weight vector, and b is the bias term. By optimizing the weight vector w and the bias term b, f(x) can maximize the separation of data of different categories;

[0098] Introducing an innovative SMO (Sequential Minimal Optimization) algorithm improvement for support vector machine (SVM), which is an SMO algorithm based on adaptive learning rate and dynamic sub-problem selection. Its innovations are as follows:

[0099] Adaptive learning rate: Introduce an adaptive learning rate mechanism to dynamically adjust the update step size of each Lagrange multiplier to improve optimization efficiency and convergence speed.

[0100] Dynamic sub-problem selection: Improve the sub-problem selection strategy of traditional SMO by introducing a dynamic adjustment mechanism to make the selected sub-problems more flexible and efficient in each iteration process.

[0101] Detailed description:

[0102] Adaptive learning rate:

[0103] The traditional SMO algorithm uses a fixed step size when updating the Lagrange multiplier. In order to improve efficiency, an adaptive learning rate can be introduced so that the learning rate is dynamically adjusted according to the current optimization state at each iteration. The learning rate is defined as η i , at each iteration, adjust the learning rate according to the error size:

[0104]

[0105] Among them, E i Indicates the error of the current sample, and k is the adjustment parameter. In this way, a larger learning rate can be used when the error is large to converge quickly; a smaller learning rate can be used when the error is small to make fine adjustments.

[0106] Dynamic subproblem selection

[0107] The traditional SMO algorithm optimizes two Lagrange multipliers a each time i and a j , usually a heuristic strategy is used to select these two variables. A dynamic sub-problem selection mechanism can be introduced to make the selection process more efficient.

[0108] Define the scoring function for subproblem selection:

[0109] S(i, j)=|E i -E j |;

[0110] E i and E j is the error between sample i and sample j. At each iteration, the two variables with the largest score function value are selected for optimization:

[0111] (i * , j * ) = argmax i, jS ( i, j );

[0112] By dynamically selecting the subproblem with the largest score function value, the Lagrange multiplier can be optimized more effectively and the convergence speed of the algorithm can be improved.

[0113] Algorithm steps:

[0114] Step A1, initialization: Randomly initialize the Lagrange multiplier a and the bias term b.

[0115] Step A2, outer loop: traverse all Lagrange multipliers and find a that violates the KKT condition i .

[0116] Step A3, inner loop:

[0117] Step A31, select two variables (i * , j * )

[0118] Step A32, calculate the adaptive learning rate η i

[0119] Step A33, update a i* and a j*

[0120] a i* =a i* +η i* Δa i* ;

[0121] a j* =a j* +η i* Δa j* ;

[0122] Step A34, update the weight vector w and the bias term b.

[0123] Step A4, check convergence: determine whether all a satisfy the KKT condition, if so, stop iteration, otherwise return to step A2.

[0124] The advantage of adding the SMO innovative optimization algorithm is that the convergence speed of the SMO algorithm can be significantly improved through adaptive learning rate and dynamic sub-problem selection mechanism. Dynamic selection of sub-problems makes each iteration more effective and avoids invalid update steps.

[0125] At the same time, in order to map data to a high-dimensional space and make nonlinearly separable data linearly separable in a high-dimensional space, the SVM algorithm introduces a polynomial kernel function. The multi-layer kernel function structure draws on the idea of ​​hierarchical feature extraction in deep learning. It constructs multiple kernel functions by extracting features layer by layer and combines these kernel functions. Each layer uses a different kernel function and is optimized layer by layer to capture the high-level features of the data. The hierarchical structure is as follows:

[0126] Assume there are L layers of kernel functions, and define the kernel function of each layer as K (l) (x i , x j ), the final kernel function is the combination of kernel functions of each layer:

[0127]

[0128] β l is the weight of the kernel function of each layer, which is dynamically optimized through training.

[0129] Algorithm steps:

[0130] Step B1, initial layer (l=1):

[0131] The initial layer uses basic kernel functions such as RBF kernel and polynomial kernel. Assume that the kernel function of the initial layer is:

[0132] K (l) (x i , x j )=exp(-γ‖x i -x j ‖ 2 );

[0133] γ is the parameter of the RBF kernel.

[0134] Step B2, extract features layer by layer:

[0135] Each layer extracts features from the data and uses these features to construct high-level kernel functions. Define the feature extraction function for layer l for:

[0136]

[0137] W (l) is the weight matrix of the lth layer, b (l) is the bias and σ is the activation function (ReLU).

[0138] Step B3, high-level kernel function:

[0139] Construct high-level kernel functions using the extracted features.

[0140]

[0141] Represents the feature extraction result of the lth layer.

[0142] Step B4, final kernel function:

[0143] Combine the kernel functions of each layer to get the final kernel function.

[0144] Through the above innovative multi-layer kernel function structure, the performance and generalization ability of SVM can be significantly improved, especially when processing high-dimensional and nonlinearly separable data.

[0145] When the classifier is constructed by logistic regression or decision tree, specifically:

[0146] P(y=1|x)=exp(w·x+b) / (1+exp(w·x+b));

[0147] Among them, P(y=1|x) represents the probability that sample y belongs to category 1 under the condition of given sample x; w is the weight vector, b is the bias term, and by optimizing the weight vector w and the bias term b, P(y=1|x) can accurately predict the category of sample y;

[0148] When the damage detection model is a classifier constructed by a decision tree, specifically:

[0149] Decision Tree is a classification algorithm based on a tree structure. Its basic steps are to divide nodes according to different values ​​of features and recursively divide nodes until the stopping condition is met. Its basic formula is:

[0150] f(x)=argmax{P(y=1|x)};

[0151] Among them, f(x) is the decision function used to predict the category of sample y, P(y=1|x) represents the probability that sample y belongs to category 1 given sample x. By recursively dividing the nodes and calculating the probabilities, f(x) can accurately predict the category of sample y.

[0152] The present invention is based on the flaw detection of the U-lock weld of the side compartment of the lifting platform vehicle - taking two lifting platform vehicles of model TLD929 as an example, the U-lock of one vehicle is welded at the crack, and the U-lock of the other vehicle is intact. The U-lock is used to reinforce the side compartment door of the lifting platform vehicle so that it is tightly closed with the vehicle body. This actual measurement uses the probe of the present invention to detect the weld of the U-lock, and the results are as follows:

[0153] According to the ultrasonic flaw detection waveform, it can be seen that the U-shaped lock on the side door of the lift truck was welded by welding during maintenance. After testing, it was found that there was an obvious weld waveform at the depth of 3.6 mm. The damage type was determined to be an unrepairable abnormality. In order to avoid the risk of opening the door due to the breakage of the lock head during vehicle production, welding should be avoided at the broken part of the workpiece during maintenance.

[0154] Although the various steps in the above embodiment are described in the above-mentioned order, those skilled in the art can understand that in order to achieve the effect of this embodiment, different steps do not have to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple changes are within the scope of protection of the present invention.

[0155] A preventive inspection and maintenance system based on apron high-value special vehicles and non-powered support equipment according to a second embodiment of the present invention is based on a preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment according to the first embodiment, and the method comprises:

[0156] A flaw detection probe design module is configured to obtain the angle between the piezoelectric chip and the bottom surface of the wedge in the flaw detection probe based on the detection refraction angle required by the material of the device to be inspected; calculate the thickness of the piezoelectric chip according to the sound velocity and working frequency of the piezoelectric chip; and manufacture the flaw detection probe based on the angle and the thickness;

[0157] A parameter acquisition module, which is configured to integrate a temperature sensor and a humidity sensor in the flaw detection probe, and acquire the temperature and humidity in the current detection environment based on the temperature sensor and the humidity sensor;

[0158] A damage data acquisition module is configured to use a flaw detection probe to detect the device to be detected, filter the signal during the detection process, compensate for the ultrasonic propagation speed according to temperature, and compensate for the attenuation of the ultrasonic signal according to humidity to obtain the detection data; and filter out the damage wave in the detection data as the damage data;

[0159] The detection module is configured to pre-process the damage data, extract problem feature data, input the problem feature data into a pre-built and trained classifier, obtain damage classification results and damage degree results, and maintain the equipment to be detected. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the system described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0160] It should be noted that the above embodiment provides a preventive inspection and maintenance system based on high-value special vehicles and non-powered support equipment on the apron, and only uses the division of the above functional modules as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be decomposed or combined. For example, the modules in the above embodiments can be combined into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the modules or steps, and are not regarded as improper limitations on the present invention.

[0161] An electronic device according to a third embodiment of the present invention includes:

[0162] at least one processor; and

[0163] a memory communicatively connected to at least one of the processors; wherein,

[0164] The memory stores instructions that can be executed by the processor, and the instructions are used to be executed by the processor to implement the above-mentioned preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment.

[0165] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment.

[0166] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related instructions of the storage device and processing device described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0167] Those skilled in the art should be able to appreciate that the modules and method steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software or a combination of the two, and the programs corresponding to the software modules and method steps can be placed in random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the technical field. In order to clearly illustrate the interchangeability of electronic hardware and software, the composition and steps of each example have been generally described in the above description according to the function. Whether these functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0168] Reference below Figure 2 , which shows a schematic diagram of the structure of a computer system of a server for implementing the method, system, and device embodiments of the present application. Figure 2 The server shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0169] like Figure 2 As shown, the computer system includes a central processing unit (CPU) 201, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 202 or the program loaded from the storage part 208 to the random access memory (RAM) 203. Various programs and data required for system operation are also stored in the RAM 203. The CPU 201, ROM 202 and RAM 203 are connected to each other through a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0170] The following components are connected to the I / O interface 205: an input section 206 including a keyboard, a mouse, etc.; an output section 207 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 208 including a hard disk, etc.; and a communication section 209 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 209 performs communication processing via a network such as the Internet. A drive 210 is also connected to the I / O interface 205 as needed. A removable medium 211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 210 as needed so that a computer program read therefrom is installed into the storage section 208 as needed.

[0171] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the method of the present application are executed. It should be noted that the above-mentioned computer-readable medium of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections with one or more conductors, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device, or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0172] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0173] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0174] The terms "first", "second", etc. are used to distinguish similar objects rather than to describe or indicate a particular order or sequence.

[0175] The term "comprise" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus / device.

[0176] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment, characterized in that: The method includes: Based on the detection refraction angle required by the material of the device to be inspected, the angle between the piezoelectric chip and the bottom surface of the wedge block in the flaw detection probe is obtained; based on the sound velocity and working frequency of the piezoelectric chip, the thickness of the piezoelectric chip is calculated; based on the angle and the thickness, the flaw detection probe is manufactured; A temperature sensor and a humidity sensor are integrated in the flaw detection probe, and the temperature and humidity in the current detection environment are obtained based on the temperature sensor and the humidity sensor; The device to be inspected is inspected using a flaw detection probe, the signal is filtered during the inspection process, and the ultrasonic propagation speed is compensated according to the temperature, and the attenuation of the ultrasonic signal is compensated according to the humidity, so as to obtain the inspection data; the damage wave in the inspection data is screened out as the damage data; The damage data is preprocessed and problem feature data is extracted. The problem feature data is input into a pre-built and trained classifier to obtain damage classification results and damage degree results, and the equipment to be inspected is maintained.

2. A preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment according to claim 1, characterized in that: During the detection process, the signal is filtered and processed as follows: Among them, y(n) is the filtered signal, x(n) is the input signal, and a i and b i are the filter coefficients, and N and M are the filter orders.

3. According to claim 1, a high-value special vehicle based on the apron and The preventive inspection and maintenance method of power support equipment is characterized in that: The method for compensating the ultrasonic propagation velocity according to temperature is: V(T)=V0(1+p(T-T0)); Wherein, V(T) is the ultrasonic wave propagation velocity at temperature T, V0 is the propagation velocity at reference temperature T0, and p is the temperature coefficient.

4. The preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment according to claim 1 is characterized in that: The attenuation of the ultrasonic signal is compensated according to the humidity, and the method is as follows: The attenuation of the ultrasonic signal is compensated according to the humidity, and the method is as follows: A(H) = A0exp(-βH); Where A(H) is the signal attenuation at humidity H, A0 is the attenuation at reference humidity, and β is the humidity coefficient.

5. The preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment according to claim 1 is characterized in that: Use the flaw detection probe to detect the equipment to be tested. The method is as follows: Move the ultrasonic flaw detector to the vicinity of the object to be inspected, and adjust the parameters of the oscilloscope device based on the manufactured flaw detection probe; Add a set amount of coupling agent to the part to be tested on the device to be tested, place the flaw detection probe on the surface of the part to be tested, make the flaw detection probe close to the surface, pull the flaw detection probe to make it in close contact with the surface of the part to be tested and move in a zigzag shape.

6. The preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment according to claim 1 is characterized in that: The classification results include mild injury, moderate injury and severe injury.

7. The preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment according to claim 1 is characterized in that: The mild injury, moderate injury and severe injury are classified based on the percentage of the injured area to the whole body.

8. A preventive inspection and maintenance system based on apron high-value special vehicles and non-powered support equipment, based on a preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment according to any one of claims 1 to 7, characterized in that: The method includes: A flaw detection probe design module is configured to obtain the angle between the piezoelectric chip and the bottom surface of the wedge in the flaw detection probe based on the detection refraction angle required by the material of the device to be inspected; calculate the thickness of the piezoelectric chip according to the sound velocity and working frequency of the piezoelectric chip; and manufacture the flaw detection probe based on the angle and the thickness; A parameter acquisition module, which is configured to integrate a temperature sensor and a humidity sensor in the flaw detection probe, and acquire the temperature and humidity in the current detection environment based on the temperature sensor and the humidity sensor; A damage data acquisition module is configured to use a flaw detection probe to detect the device to be detected, filter the signal during the detection process, compensate for the ultrasonic propagation speed according to temperature, and compensate for the attenuation of the ultrasonic signal according to humidity to obtain the detection data; and filter out the damage wave in the detection data as the damage data; The detection module is configured to pre-process the damage data and extract problem feature data, input the problem feature data into a pre-built and trained classifier, obtain damage classification results and damage degree results, and maintain the equipment to be detected.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to at least one of the processors; wherein, The memory stores instructions executable by the processor, and the instructions are used to be executed by the processor to implement a preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement a preventive inspection and maintenance method based on apron high-value special vehicles and non-powered support equipment as described in any one of claims 1-7.

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