A method and system for detecting gate faults

Abstract

The present invention discloses a gate fault detection method and system, which relates to the technical field of fault diagnosis and health monitoring. It includes collecting the vibration signal of the gate, extracting the main modal frequency, setting the sampling frequency and the passband range, designing a finite impulse response filter for filtering, and then using the particle swarm algorithm for optimization to obtain an enhanced signal; extracting and integrating the features in the enhanced signal to obtain a feature vector, using the rule threshold matching method to map the feature vector to a discrete state set, and obtaining the gate state; sampling historical data and constructing a state transition probability matrix, calculating the steady-state probability vector according to the state transition probability matrix, combining with the gate state to generate a state sequence trajectory, setting the influence factor and weight using the empirical rule, calculating the fault occurrence probability for risk detection; the present invention significantly improves the accuracy, efficiency and reliability of gate fault detection.

Description

Technical Field

[0001] The present invention relates to the technical field of fault diagnosis and health monitoring, and particularly to a gate fault detection method and system. Background Art

[0002] With the continuous development of industrial automation, gates, as important control devices, play a crucial role in multiple fields such as water conservancy, transportation, and energy. In recent years, with the development of sensor technology, signal processing technology, and artificial intelligence technology, the fault detection method based on vibration signals has gradually become a mainstream solution. By collecting vibration signals through high-precision sensors and using frequency-domain analysis, filtering technology, and pattern recognition methods, early faults of gates can be effectively detected.

[0003] However, the existing gate fault detection technologies have the following problems: First, feature extraction is mostly based on single-domain analysis, ignoring time-frequency joint features and the non-stationary characteristics of signals; Second, state classification algorithms generally use static thresholds or shallow models, lacking the ability to adaptively fuse multi-dimensional features, and the threshold setting depends on empirical data, which is prone to misjudgment due to changes in working conditions; Third, fault prediction models are mostly based on isolated state analysis, without considering the temporal correlation of historical data, making it difficult to construct a dynamic transition probability network. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a gate fault detection method and system, which solve the problems in the prior art that existing feature extraction is mostly based on single-domain analysis, ignoring time-frequency joint features and the non-stationary characteristics of signals, state classification algorithms generally use static thresholds or shallow models, lacking the ability to adaptively fuse multi-dimensional features, and the threshold setting depends on empirical data, which is prone to misjudgment due to changes in working conditions, and fault prediction models are mostly based on isolated state analysis, without considering the temporal correlation of historical data, making it difficult to construct a dynamic transition probability network.

[0006] To solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a gate fault detection method, which includes

[0008] Collecting the vibration signal of the gate, extracting the main modal frequency, setting the sampling frequency and passband range, designing a finite impulse response filter for filtering, and then optimizing it using the particle swarm algorithm to obtain an enhanced signal;

[0009] Extracting and integrating the features in the enhanced signal to obtain a feature vector, mapping the feature vector to a discrete state set using the regular threshold matching method, and obtaining the gate state;

[0010] The gate states include the normal state, the slight structural fatigue state, the abnormal structural deviation state, and the critical structural damage state;

[0011] Obtain historical data for sampling, construct a state transition probability matrix, calculate the steady-state probability vector according to the state transition probability matrix, combine with the gate state, generate a state sequence trajectory, set the influence factor and weight using the empirical rule, and calculate the probability of failure for risk detection;

[0012] Store the data through a database.

[0013] As a preferred solution of the gate fault detection method of the present invention, wherein: the steps of collecting the gate vibration signal, extracting the main modal frequency, setting the sampling frequency and passband range, designing a finite impulse response filter for filtering, and then using the particle swarm optimization algorithm to optimize to obtain the enhanced signal include the following:

[0014] Use a triaxial acceleration sensor to collect the original vibration signal of the gate;

[0015] Use the fast Fourier transform to perform frequency-domain analysis on the collected vibration signal, and use the absolute value to calculate the amplitude of the frequency-domain signal;

[0016] Use the frequency main peak extraction algorithm to select the frequency corresponding to the maximum amplitude as the main modal frequency ;

[0017] Use the Nyquist theorem to set the sampling frequency and the sampling duration ;

[0018] Use the main modal frequency to set the passband range, and use subtraction to extract the passband width;

[0019] Use the window function method to design a finite impulse response filter, and use the Hann window function to construct the finite impulse response filter coefficients;

[0020] Use the order empirical formula to calculate the order of the finite impulse response filter;

[0021] Use the constructed finite impulse response filter to perform a convolution operation on the vibration signal to obtain a filtered signal, obtain the mean and standard deviation of the filtered signal, and calculate the kurtosis value;

[0022] Use the delay embedding technique to convert the filtered signal into embedding vectors at different time points, and use the Chebyshev distance to traverse all embedding vectors, calculate the distance between the current embedding vector and all embedding vectors, sort all the distances in ascending order, and select the largest distance to define the similarity;

[0023] Calculate the entropy value of the filtered signal by using the fuzzy entropy algorithm in combination with similarity;

[0024] Set the initial weight coefficient using the empirical rule, and calculate the weight coefficients of the kurtosis value and the entropy value respectively using the proportional formula;

[0025] Integrate the kurtosis value, the entropy value, and the weight coefficient as particles, and randomly generate the initial position and population for initialization;

[0026] Construct the objective function by using the kurtosis value, the entropy value, and the weight coefficient;

[0027] Calculate the objective function value, and use the objective function value as the fitness value of the particle. Select the maximum fitness value for position update. During the iteration process, when the iteration number reaches the maximum number, output the optimal weight coefficient;

[0028] Take the optimal weight coefficient as the finite impulse response filter parameter, and perform the deconvolution operation using the blind deconvolution technique to obtain the enhanced signal and the amplitude at each time point.

[0029] As a preferred solution of the gate fault detection method described in the present invention, wherein: the features in the enhanced signal are extracted and integrated to obtain a feature vector, and the feature vector is mapped to a discrete state set using the rule threshold matching method, and obtaining the gate state includes the following steps:

[0030] Calculate the energy index of the enhanced signal using the weighted sum of squares formula;

[0031] Divide the enhanced signal into overlapping segments using the Welch method, construct the periodogram of each overlapping segment, and average all the overlapping segments to obtain the power spectral density;

[0032] Calculate the bandwidth power ratio of the enhanced signal by using the frequency domain analysis method in combination with the power spectral density;

[0033] Further use the fuzzy entropy algorithm to calculate the entropy value of the enhanced signal;

[0034] Use the feature splicing technology to splice the energy index, the bandwidth power ratio, and the entropy value to obtain a feature vector for normalization operation;

[0035] Use the rule threshold matching method to map the feature vector to the discrete state set, and perform state conversion during the mapping process to obtain the current state of the gate;

[0036] The discrete state set includes the normal state 、the slight structural fatigue state 、the structural offset abnormal state and the critical structural damage state ;

[0037] Set the priorities of the states using the Delphi method to obtain ;

[0038] During the state transition process, when a state satisfies multiple states simultaneously, the maximum priority is determined according to the priority to be the preferred state.

[0039] As a preferred solution of the gate fault detection method described in the present invention, wherein: the steps of obtaining historical data for sampling, constructing a state transition probability matrix, calculating a steady-state probability vector according to the state transition probability matrix, combining the gate state, generating a state sequence trajectory, setting influence factors and weights using empirical rules, and calculating the fault occurrence probability include the following steps:

[0040] Collect historical fault vibration data of the gate;

[0041] Use the current state of the gate as the starting point, and use the Metropolis random walk algorithm to sample the state sequence from the historical operation data and construct a state change sequence;

[0042] After calculating the transition probabilities between the current state of the gate and all states in the state change sequence using the Markov chain model, construct a state transition probability matrix;

[0043] Use the power iteration method to iteratively solve the state transition probability vectors in each row of the state transition probability matrix to obtain a steady-state probability vector;

[0044] Randomly sample a candidate state from the transition probability matrix , and calculate the acceptance probability;

[0045] Use a pseudo-random number generator to generate a random number, and compare the acceptance probability with the random number. When the acceptance probability is greater than or equal to the random number, then use the candidate state as the new state, otherwise retain the current state;

[0046] Repeat sampling to generate a state sequence trajectory, and use the damage state corresponding to each state in the state sequence trajectory as a fault cause node;

[0047] Use empirical rules to set influence factors and weights respectively;

[0048] Use the weighted summation formula to calculate the influence score;

[0049] Use the Sigmoid function to map the influence score to obtain the influence probability;

[0050] Use DeMorgan's theorem combined with the probability product rule to calculate the fault occurrence probability.

[0051] As a preferred embodiment of the gate fault detection method of the present invention, where: the risk detection further refers to setting an evaluation threshold using empirical rules, comparing the probability of failure with the evaluation threshold. When the probability of failure is greater than the evaluation threshold, it indicates a high risk at present, triggering an alarm and reminding the staff to take emergency repair measures. Otherwise, continue the detection and generate a gate health report.

[0052] As a preferred embodiment of the gate fault detection method of the present invention, where: collect the vibration signal and first perform preprocessing operations;

[0053] The preprocessing operations include interpolation, smoothing, and normalization.

[0054] As a preferred embodiment of the gate fault detection method of the present invention, where: the storage of data through the database means storing the detection results, feature vectors, and state sequence trajectories into the database respectively, and using the database to add timestamps to each data and then classify them.

[0055] In a second aspect, the present invention provides a gate fault detection system, including,

[0056] An acquisition and optimization module, configured to acquire the gate vibration signal, extract the main modal frequency, set the sampling frequency and passband range, design a finite impulse response filter for filtering, and then use the particle swarm algorithm for optimization to obtain an enhanced signal;

[0057] A detection module, configured to extract and integrate the features in the enhanced signal to obtain a feature vector, map the feature vector to a discrete state set using the rule threshold matching method, and obtain the gate state;

[0058] An adoption and generation module, configured to acquire historical data for sampling, construct a state transition probability matrix, calculate the steady-state probability vector according to the state transition probability matrix, combine with the gate state to generate a state sequence trajectory, use empirical rules to set the influence factor and weight, and calculate the probability of failure for risk detection;

[0059] A storage module, configured to store data through the database.

[0060] In a third aspect, the present invention provides a computer device, including a memory and a processor, where: the memory stores a computer program, and where: when the computer program is executed by the processor, any step of the gate fault detection method as described in the first aspect of the present invention is implemented.

[0061] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and where: when the computer program is executed by the processor, any step of the gate fault detection method as described in the first aspect of the present invention is implemented.

[0062] The beneficial effects of the present invention are as follows: By combining time-domain kurtosis analysis and fuzzy entropy calculation, and further integrating multi-dimensional features such as energy index and bandwidth power ratio, the present invention solves the limitation of traditional methods relying on single-domain analysis. And by splicing multiple features such as kurtosis and entropy value into a feature vector, and combining the fuzzy entropy algorithm and the rule threshold matching method for state mapping, the accuracy and robustness of state classification are greatly improved. Secondly, a time-series analysis method based on historical data is introduced, and the Metropolis random walk algorithm and the Markov chain model are used to construct a dynamic transition probability network, which can effectively capture the time-series relationship of historical data, and then realize the dynamic prediction of the occurrence of faults. Therefore, by comprehensively considering time-frequency joint features, optimizing state and classification, and introducing time-series correlation analysis, the present invention significantly improves the accuracy, efficiency and reliability of gate fault detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0064] Figure 1 It is a flowchart of the gate fault detection method in Embodiment 1.

[0065] Figure 2 It is a structural diagram of the gate fault detection system in Embodiment 1.

[0066] Figure 3 It is a flowchart of signal processing in Embodiment 1.

[0067] Figure 4 It is a flowchart of gate state judgment in Embodiment 1. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings of the specification.

[0069] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0070] Second, the "one embodiment" or "embodiment" referred to herein means a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0071] Embodiment 1, referring to Figures 1 to 4 , is the first embodiment of the present invention. This embodiment provides a method for detecting gate faults, including the following steps:

[0072] S1. Collect the vibration signal of the gate, extract the main modal frequency, set the sampling frequency and passband range, design a finite impulse response filter for filtering, and then use the particle swarm optimization algorithm for optimization to obtain an enhanced signal;

[0073] Specifically, collecting the vibration signal of the gate, extracting the main modal frequency, setting the sampling frequency and passband range, designing a finite impulse response filter for filtering, and then using the particle swarm optimization algorithm for optimization to obtain an enhanced signal includes the following steps:

[0074] Use a three-axis acceleration sensor to collect the original vibration signal of the gate;

[0075] Use the fast Fourier transform to perform frequency-domain analysis on the collected vibration signal, and use the absolute value to calculate the amplitude of the frequency-domain signal;

[0076] Use the frequency main peak extraction algorithm to select the frequency corresponding to the maximum amplitude as the main modal frequency ;

[0077] Use the Nyquist theorem to set the sampling frequency and the sampling duration ;

[0078] Use the main modal frequency to set the passband range, and use subtraction operation to extract the passband width;

[0079] The lower frequency of the passband range is: , and the upper frequency of the passband range is: ;

[0080] Use the window function method to design a finite impulse response filter, and use the Hanning window function to construct the finite impulse response filter coefficients;

[0081] Use the order empirical formula to calculate the order of the finite impulse response filter. The formula is:

[0082]

[0083] In the formula, denotes the order of the finite impulse response filter, denotes the sampling frequency, denotes the passband width, denotes rounding up;

[0084] Perform a convolution operation on the vibration signal using the constructed finite impulse response filter to obtain a filtered signal, acquire the mean and standard deviation of the filtered signal, and calculate the kurtosis value. The formula is:

[0085]

[0086] In the formula, denotes the kurtosis value of the filtered signal , denotes the standard deviation of the filtered signal ; denotes the mean of the filtered signal ; denotes the total length of the filtered signal, denotes the th amplitude of the filtered signal;

[0087] Use the delay embedding technique to convert the filtered signal into embedding vectors at different time points, and use the Chebyshev distance to traverse all the embedding vectors, calculate the distances between the current embedding vector and all the embedding vectors, and after sorting all the distances in ascending order, select the maximum distance to be defined as the similarity;

[0088] Use the fuzzy entropy algorithm combined with the similarity to calculate the entropy value of the filtered signal;

[0089] Set the initial weight coefficients using empirical rules, and use the proportional formula to calculate the weight coefficients of the kurtosis value and the entropy value respectively. The formula is:

[0090]

[0091]

[0092] In the formula, denotes the weight coefficient of the kurtosis value, denotes the weight coefficient of the entropy value, and denote the initial weight coefficients of the kurtosis value and the entropy value respectively;

[0093] Integrate the kurtosis value, the entropy value, and the weight coefficients as particles, and randomly generate the initial positions and the population for initialization;

[0094] Use the kurtosis value, the entropy value, and the weight coefficients to construct an objective function. The formula is:

[0095]

[0096] In the formula, represents the objective function value of the filtered signal ; represents the entropy value of the filtered signal ; represents the natural logarithm;

[0097] Calculate the objective function value, use the objective function value as the fitness value of the particle, select the maximum fitness value for position update. During the iteration process, when the number of iterations reaches the maximum number, output the optimal weight coefficient;

[0098] Use the optimal weight coefficient as the finite impulse response filter parameter, and perform deconvolution operation using the blind deconvolution technique to obtain the enhanced signal and the amplitude at each time point.

[0099] By simultaneously collecting vibration signals in three directions through a triaxial acceleration sensor, the vibration characteristics of the gate can be comprehensively captured. Using FFT for frequency-domain analysis of the vibration signal and finding the main modal frequency through the main peak extraction algorithm can significantly improve the understanding of the frequency characteristics of the signal. By accurately obtaining the main modal frequency, the present invention can be optimized for specific frequencies, improving the sensitivity of the present invention to potential failure modes. Using the Nyquist theorem to set the sampling frequency can ensure that sufficient information is collected, avoiding signal distortion or aliasing caused by insufficient sampling. This ensures that the filtered signal is more real and reliable. And by designing the FIR filter using the window function method and optimizing the filter parameters in combination with the particle swarm optimization algorithm, the performance of the filter can be effectively improved, removing the noise components in the signal and retaining the useful vibration characteristics. The present invention not only improves the quality of the signal, but also improves the accuracy of signal analysis, especially having significant advantages in an environment with large noise interference. Secondly, by calculating the kurtosis and entropy value of the filtered signal and combining and optimizing them with the weight coefficient, the filtered signal can be made to better conform to the target optimization standard. Moreover, by optimizing the weight coefficient using the particle swarm algorithm, the present invention can adaptively adjust the parameters to enhance the signal in an optimal manner, further improving the adaptive ability of the system. And using the blind deconvolution technique to perform deconvolution operation on the filtered signal can extract a more accurate and clear enhanced signal from the processed signal. This process helps to restore the original characteristics of the vibration signal by removing system noise and distortion, providing more accurate data support for subsequent fault diagnosis and prediction. Therefore, the present invention not only improves the accuracy of signal acquisition and analysis, but also greatly improves the efficiency and effect in the filtering and enhancement processes. At the same time, using the combination of the fuzzy entropy algorithm and kurtosis calculation optimizes the signal feature extraction, enabling the present invention to more accurately capture the abnormal features in the vibration signal.

[0100] S2. Extract the features in the enhanced signal for integration to obtain a feature vector, map the feature vector to a discrete state set using the rule threshold matching method, and obtain the gate state; the gate state includes a normal state, a slight structural fatigue state, a structural deviation abnormal state, and a critical structural damage state;

[0101] Specifically, extracting the features in the enhanced signal for integration to obtain a feature vector, mapping the feature vector to a discrete state set using the rule threshold matching method, and obtaining the gate state includes the following steps:

[0102] Calculate the energy index of the enhanced signal using the weighted sum of squares formula. The formula is:

[0103]

[0104] In the formula, represents the energy index, represents the amplitude of the enhanced signal at time point and represents the total number of time points;

[0105] Divide the enhanced signal into overlapping segments using the Welch method, construct the periodogram of each overlapping segment, and average all overlapping segments to obtain the power spectral density;

[0106] Calculate the bandwidth power ratio of the enhanced signal using the frequency domain analysis method combined with the power spectral density. The formula is:

[0107]

[0108] In the formula, represents the bandwidth power ratio, represents the power spectral density, represents the main modal frequency;

[0109] Further use the fuzzy entropy algorithm to calculate the entropy value of the enhanced signal;

[0110] Use the feature splicing technology to splice the energy index, bandwidth power ratio, and entropy value to obtain a feature vector for normalization operation;

[0111] Use the rule threshold matching method to map the feature vector to the discrete state set, and perform state conversion during the mapping process to obtain the current state of the gate;

[0112] The discrete state set includes a normal state 、a slight structural fatigue state 、a structural deviation abnormal state and a critical structural damage state ;

[0113] The priorities of the states are set using the Delphi method to obtain ;

[0114] During the state transition process, when a state satisfies multiple states simultaneously, the maximum priority is determined according to the priority as the preferred state;

[0115] The decision condition formula for state transition during the mapping process is:

[0116]

[0117] In the formula, and respectively represent the judgment thresholds for the slight structural fatigue state, represents the judgment threshold for the abnormal structural offset state, and represent the judgment thresholds for the critical structural damage state, represents the enhanced signal entropy value.

[0118] The energy index of the enhanced signal is calculated by the weighted sum of squares formula, enabling the present invention to quantify the overall energy of the signal, thus providing effective support for subsequent state classification. The enhanced signal is divided into overlapping segments by the Welch method, and the power spectral density of each segment is calculated. Through the calculation of the power spectral density, the frequency characteristics of the signal can be revealed, and valuable frequency information can be extracted therefrom, which is crucial for evaluating the vibration characteristics of the gate, especially in the early fault state identification of slight fatigue, abnormal offset, and critical damage states. As an important index for signal frequency domain analysis, the bandwidth power ratio can reflect the frequency concentration and bandwidth characteristics of the signal through its calculation. In the health monitoring of the gate, the change in the bandwidth power ratio can indicate potential structural problems or damage areas. Through this feature, the sensitivity of fault diagnosis can be improved, especially providing a reliable basis for identifying different types of fault states. By calculating the entropy value of the signal through the fuzzy entropy algorithm, it helps to reveal the complexity and uncertainty of the signal. The higher the entropy value, the more uncertain the change of the signal, which is usually related to potential structural anomalies or fault states. Combining the calculation of the entropy value can identify the abnormal state of the gate earlier, thereby enhancing the perception ability of the present invention for early faults. Secondly, the energy index, bandwidth power ratio, and entropy value are concatenated to obtain a comprehensive feature vector, and then normalized processing is performed, which can effectively remove the scale difference between data, enabling different features to have the same comparison weight. This operation not only improves the stability of the model but also optimizes the accuracy of state classification, ensuring that various features can equally affect the final state determination. And the rule threshold matching method maps the feature vector to a discrete state set through pre-set rules and performs precise classification during the state transition process. Through this method, the present invention can accurately divide between different states and determine the working state of the current gate according to the set rules, such as normal state, slight structural fatigue state, structural offset abnormal state, and critical structural damage state. The introduction of this step greatly improves the reliability of state determination, especially when multiple fault states coexist, which can efficiently distinguish and determine. At the same time, the Delphi method scientifically sets the priority of each state through expert opinions, ensuring that in the case of multiple states, the fault state most threatening to the gate can be preferentially identified.

[0119] S3. Obtain historical data for sampling, construct a state transition probability matrix, calculate the steady-state probability vector according to the state transition probability matrix, combine the gate state, generate a state sequence trajectory, set the influence factor and weight using empirical rules, and calculate the probability of fault occurrence for risk detection;

[0120] Specifically, the steps for obtaining historical data for sampling, constructing a state transition probability matrix, calculating a steady-state probability vector based on the state transition probability matrix, combining the gate state, generating a state sequence trajectory, setting influence factors and weights using empirical rules, and calculating the probability of failure are as follows:

[0121] Collect historical fault vibration data of the gate;

[0122] Taking the current state of the gate as the starting point, use the Metropolis random walk algorithm to sample the state sequence from the historical operation data and construct a state change sequence;

[0123] After calculating the transition probabilities between the current state of the gate and all states in the state change sequence using the Markov chain model, construct a state transition probability matrix;

[0124] Use the power iteration method to iteratively solve the state transition probability vector of each row in the state transition probability matrix to obtain a steady-state probability vector;

[0125] Randomly sample a candidate state from the transition probability matrix , and calculate the acceptance probability. The formula is:

[0126]

[0127] In the formula, represents the acceptance probability, represents the current state of the gate, and respectively represent the steady-state probability vectors of the gate states and , represents the minimum operation;

[0128] Use a pseudo-random number generator to generate a random number and compare the acceptance probability with the random number. When the acceptance probability is greater than or equal to the random number, then use the candidate state as the new state, otherwise retain the current state;

[0129] Repeat sampling to generate a state sequence trajectory, and use the damage state corresponding to each state in the state sequence trajectory as a fault cause node;

[0130] Use empirical rules to set influence factors and weights respectively;

[0131] Use the weighted summation formula to calculate the influence score. The formula is:

[0132]

[0133] In the formula, represents the th fault cause node The impact score, represents the weight of the energy index, represents the weight of the power spectral density, represents the weight of the enhanced signal entropy value;

[0134] Use the Sigmoid function to map the impact score to obtain the impact probability. The formula is:

[0135]

[0136] In the formula, represents the th failure cause node of the mapping 's impact probability, represents the base of the natural logarithm;

[0137] Use DeMorgan's theorem combined with the probability product rule to calculate the failure occurrence probability. The formula is:

[0138]

[0139] In the formula, represents the failure occurrence probability under the given failure cause node and the suppression factor condition, represents the failure state, when it is , it means the failure occurs, represents the th suppression factor 's adjusted weight, represents the influence factor, represents the th failure cause node 's adjusted weight. The suppression factor and the adjusted weight as well as the adjusted weight can be set through expert experience and experimental data.

[0140] By collecting the historical fault vibration data of the gate and performing state sequence sampling, a state transition model of the system can be constructed based on the historical fault modes to predict future state changes. This not only improves the accuracy of fault prediction but also provides data support for the construction of the subsequent state transition probability matrix. The sampled state sequence provides a solid foundation for subsequent fault prediction, enabling the present invention to detect potential fault points in advance and avoid system fault failures. The Metropolis algorithm, through the idea of Markov chain, effectively avoids excessive unnecessary calculations and can generate a state sequence that conforms to the actual state change law according to historical data, providing an efficient sampling method. Moreover, by accepting or rejecting the sampling results, this algorithm helps to construct a state change trajectory that conforms to the actual operation law of the system, thus improving the accuracy and reliability of model prediction. The construction of the state transition probability matrix is a crucial step in the entire fault prediction process. By analyzing the transition probabilities between different states of the system, this step helps to identify which state transitions have a higher probability, so that the future state of the system can be judged more accurately during prediction. Secondly, by modeling the state transition through the Markov chain model, the probability of transitioning from the current state to the fault state can be effectively predicted, thereby providing decision-making support for fault prediction and prevention. The calculation of the steady-state probability vector can reveal the stable state distribution under long-term operation, which provides a clear criterion for fault prediction. For example, if the steady-state probability of a certain state is relatively high, it indicates that this state may be the most frequently occurring state during stable operation. If this state is close to the fault state, it indicates that there may be a relatively high fault risk. By efficiently calculating the steady-state probability vector through the power iteration method, the long-term stable state of the system can be quickly and accurately identified, and then the fault tendency of the system can be predicted. By combining the influencing factors and weights to perform weighted summation on the fault causes, the present invention can comprehensively consider the weights of various influencing factors and provide a more accurate fault prediction result. Moreover, using the Sigmoid function to map the influence score to the influence probability can more accurately estimate the influence of each fault cause node on the occurrence of the fault, thereby providing a more scientific fault prevention plan for decision-makers. And by combining DeMorgan's theorem and the probability product rule to calculate the fault occurrence probability, the present invention can comprehensively consider the combined influence of multiple factors (such as fault causes, suppression factors) on the occurrence of the fault, which improves the accuracy of fault prediction and enhances the robustness of the system. Through this probability calculation framework, the present invention can flexibly adjust the weights of various factors to adapt to different fault modes in different situations and provide guidance for maintenance and optimization.

[0141] Further, risk detection means further setting an evaluation threshold using empirical rules, comparing the fault occurrence probability with the evaluation threshold. When the fault occurrence probability is greater than the evaluation threshold, it indicates that the current situation is of high risk, triggering an alarm and reminding the staff to take emergency repair measures. Otherwise, continue the detection and generate a gate health report.

[0142] By introducing a risk detection mechanism, the present invention can predict in advance the possible failures of the device and issue early warnings in a timely manner. Such early warnings help to avoid sudden failures of the device and ensure the long-term stable operation of the device. When the probability of failure is higher than the evaluation threshold, an alarm will be automatically triggered and emergency repair measures will be recommended, reducing human intervention and delays, and improving the response speed and efficiency of fault handling. In addition, through the generation of health reports, maintenance personnel can have a more comprehensive understanding of the operating conditions of the device, thereby making more reasonable maintenance decisions.

[0143] S4. Store the data through a database;

[0144] Specifically, storing the data through a database means storing the detection results, feature vectors, and state sequence trajectories into the database respectively, and adding timestamps to each data using the database and then classifying them.

[0145] Storing the detection results, feature vectors, and state sequence trajectories through a database enables a large amount of data to be effectively managed in a structured manner. By introducing timestamps, each data item can be accurately calibrated with time, making the data storage process efficient and orderly.

[0146] This embodiment also provides a gate fault detection system, including:

[0147] An acquisition and optimization module, configured to acquire gate vibration signals, extract the main modal frequency, set the sampling frequency and passband range, design a finite impulse response filter for filtering, and then use the particle swarm algorithm for optimization to obtain an enhanced signal;

[0148] A detection module, configured to extract and integrate the features in the enhanced signal to obtain a feature vector, map the feature vector to a discrete state set using the rule threshold matching method, and obtain the gate state;

[0149] An adoption and generation module, configured to acquire historical data for sampling, construct a state transition probability matrix, calculate the steady-state probability vector according to the state transition probability matrix, combine with the gate state to generate a state sequence trajectory, set the influence factor and weight using empirical rules, and calculate the probability of failure for risk detection;

[0150] A storage module, configured to store the data through a database.

[0151] This embodiment also provides a computer device applicable to the gate fault detection method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the gate fault detection method as proposed in the above embodiment.

[0152] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0153] This embodiment also provides a storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the method for detecting gate faults proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc.

[0154] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting gate faults, characterized in that: including Collect the vibration signal of the gate, extract the main modal frequency, set the sampling frequency and passband range, design a finite impulse response filter for filtering, and then use the particle swarm optimization algorithm to optimize it to obtain an enhanced signal; Extract and integrate the features in the enhanced signal to obtain a feature vector, use the rule threshold matching method to map the feature vector to a discrete state set, and obtain the gate state; The gate state includes a normal state, a slight structural fatigue state, a structural deviation abnormal state, and a critical structural damage state; Obtain historical data for sampling, construct a state transition probability matrix, calculate the steady-state probability vector according to the state transition probability matrix, combine the gate state, generate a state sequence trajectory, use the empirical rule to set the influence factor and weight, and calculate the probability of failure for risk detection; Store the data through a database; The steps of collecting the vibration signal of the gate, extracting the main modal frequency, setting the sampling frequency and passband range, designing a finite impulse response filter for filtering, and then using the particle swarm optimization algorithm to optimize it to obtain an enhanced signal include the following: Use a triaxial acceleration sensor to collect the original vibration signal of the gate; Use the fast Fourier transform to perform frequency-domain analysis on the collected vibration signal, and use the absolute value to calculate the amplitude of the frequency-domain signal; The maximum amplitude corresponding frequency is selected as the main modal frequency by using the dominant frequency peak extraction algorithm ; Set the sampling frequency using the Nyquist theorem and the sampling duration ; Use the main modal frequency to set the passband range, and use subtraction operation to extract the passband width; Use the window function method to design a finite impulse response filter, and use the Hanning window function to construct the finite impulse response filter coefficients; Use the order empirical formula to calculate the order of the finite impulse response filter; Use the constructed finite impulse response filter to perform a convolution operation on the vibration signal to obtain a filtered signal, obtain the mean and standard deviation of the filtered signal, and calculate the kurtosis value; Use the delay embedding technique to convert the filtered signal into embedding vectors at different time points, and use the Chebyshev distance to traverse all embedding vectors, calculate the distance between the current embedding vector and all embedding vectors, sort all the distances in ascending order, and select the largest distance to be defined as the similarity; Use the fuzzy entropy algorithm combined with the similarity to calculate the entropy value of the filtered signal; Use the empirical rule to set the initial weight coefficient, and use the proportional formula to calculate the weight coefficients of the kurtosis value and the entropy value respectively; Integrate the kurtosis value, the entropy value and the weight coefficient as particles, and randomly generate the initial position and population for initialization; Use the kurtosis value, the entropy value and the weight coefficient to construct an objective function; Calculate the objective function value, and use the objective function value as the fitness value of the particle, select the maximum fitness value for position update, and in the iteration process, when the iteration times reach the maximum number of times, output the optimal weight coefficient; Use the optimal weight coefficient as the finite impulse response filter parameter, and use the blind deconvolution technique to perform deconvolution operation to obtain the enhanced signal and the amplitude at each time point; The steps of extracting and integrating the features in the enhanced signal to obtain a feature vector, using the rule threshold matching method to map the feature vector to a discrete state set, and obtaining the gate state include the following: Use the weighted sum of squares formula to calculate the energy index of the enhanced signal; The enhanced signal is divided into overlapping segments using Welch's method, the periodogram of each overlapping segment is constructed, and all overlapping segments are averaged to obtain the power spectral density; The bandwidth power ratio of the enhanced signal is calculated using a frequency-domain analysis method in combination with the power spectral density; The fuzzy entropy algorithm is further used to calculate the entropy value of the enhanced signal; The energy index, bandwidth power ratio, and entropy value are concatenated using feature concatenation technology to obtain a feature vector for normalization; The feature vector is mapped to a discrete state set using the rule threshold matching method, and state transitions are performed during the mapping process to obtain the current state of the gate; The set of discrete states includes a normal state , a slight structural fatigue state , a structural deviation abnormal state and a critical structural damage state ; Set the priority of the status using the Delphi method to obtain ; During the state transition process, when the state satisfies multiple states simultaneously, the maximum priority is determined as the priority state according to the priority; The steps of obtaining historical data for sampling, constructing a state transition probability matrix, calculating the steady-state probability vector according to the state transition probability matrix, combining the gate state, generating a state sequence trajectory, and using empirical rules to set the influence factor and weight to calculate the probability of failure are as follows: Collect historical fault vibration data of the gate; Using the current state of the gate as the starting point, the Metropolis random walk algorithm is used to sample the state sequence from the historical operation data, and a state change sequence is constructed; After calculating the transition probabilities between the current state of the gate and all states in the state change sequence using the Markov chain model, a state transition probability matrix is constructed; The power iteration method is used to iteratively solve each row of the state transition probability vectors in the state transition probability matrix to obtain the steady-state probability vector; Randomly sample a candidate state from the transition probability matrix , and calculate the acceptance probability; Generate a random number using a pseudo-random number generator, and compare the acceptance probability with the random number. When the acceptance probability is greater than or equal to the random number, the candidate state is taken as the new state; otherwise, the current state is retained. Sampling is repeated to generate a state sequence trajectory, and the damage state corresponding to each state in the state sequence trajectory is used as a fault cause node; Empirical rules are used to set the influence factor and weight respectively; The weighted sum formula is used to calculate the influence score; The Sigmoid function is used to map the influence score to obtain the influence probability; DeMorgan's theorem is used in combination with the probability product rule to calculate the probability of failure; The risk detection further refers to using empirical rules to set an evaluation threshold, comparing the probability of failure with the evaluation threshold. When the probability of failure is greater than the evaluation threshold, it indicates that the current situation is a high risk, triggering an alarm and reminding the staff to take emergency repair measures. Otherwise, continue the detection and generate a gate health report.

2. The gate fault detection method according to claim 1, characterized in that: Collect the vibration signal and perform preprocessing operations first; The preprocessing operations include interpolation, smoothing, and normalization.

3. The gate fault detection method according to claim 2, characterized in that: Storing the data through the database means storing the detection results, feature vectors, and state sequence trajectories into the database respectively, and adding timestamps to each data using the database and then classifying them.

4. A gate fault detection system, based on the gate fault detection method described in claim 1, characterized in that: including, An acquisition and optimization module for acquiring the gate vibration signal, extracting the main modal frequency, setting the sampling frequency and passband range, designing a finite impulse response filter for filtering, and then using the particle swarm algorithm for optimization to obtain the enhanced signal; A detection module for extracting and integrating the features in the enhanced signal to obtain a feature vector, mapping the feature vector to a discrete state set using the rule threshold matching method, and obtaining the gate state; An adoption and generation module is used to obtain historical data for sampling, construct a state transition probability matrix, calculate a steady-state probability vector according to the state transition probability matrix, combine the gate state, generate a state sequence trajectory, set influence factors and weights using empirical rules, and calculate the probability of a fault occurrence for risk detection; A storage module is used to store data through a database.

5. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, the steps of the gate fault detection method described in claim 1 are implemented.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the gate fault detection method described in claim 1 are implemented.

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