Fault prediction type industrial flow instrument based on deep learning algorithm and diagnostic analysis system

Abstract

The invention discloses a fault prediction type industrial flow instrument based on a deep learning algorithm and a diagnostic analysis system, and relates to the field of industrial automation and intelligent monitoring. The system comprises the following components: a data acquisition module, a data processing module, a dynamic adaptive learning module based on reinforcement learning, a fault prediction module and a diagnostic analysis module. Through the dynamic adaptive learning module based on reinforcement learning, unique state definition, action adjustment, reward function design and a reinforcement learning algorithm execution mechanism, fault prediction model parameters can be optimized in real time according to working condition changes, a hybrid neural network architecture of the fault prediction module is combined with an attention mechanism, and fault prediction accuracy is improved. The method further enhances the capability of capturing fault features, all the modules are in close cooperation and collaborative optimization, and compared with a traditional fault prediction system, in a complex and changeable industrial environment, the fault prediction precision is greatly improved, the prediction efficiency is remarkably improved, and potential faults can be found in advance.

Description

Fault Prediction-Based Industrial Flow Meter and Diagnostic Analysis System Based on Deep Learning Algorithms Technical Field

[0001] This invention relates to the field of industrial automation and intelligent monitoring, specifically to a fault prediction type industrial flow meter and diagnostic analysis system based on deep learning algorithms. Background Technology

[0002] In modern industrial production systems, industrial flow meters, as key measurement and monitoring equipment, are widely used in many fields such as petrochemicals, power energy, and metallurgical manufacturing. The stability and accuracy of their operation directly affect the continuity of production processes, the reliability of product quality, and the effective utilization of energy resources. Accurate and timely fault prediction of industrial flow meters can help enterprises take maintenance measures in advance, reduce unplanned downtime, lower production costs, and improve production efficiency and economic benefits. With the continuous improvement of industrial automation and intelligence, the requirements for fault prediction technology of industrial flow meters are becoming increasingly stringent.

[0003] However, traditional industrial flow meter fault prediction technologies mostly rely on single sensor data acquisition and simple threshold judgment. Each functional module is independent and lacks a collaborative optimization mechanism. This approach cannot fully consider the complex and ever-changing operating conditions in industrial settings and is difficult to capture subtle abnormal changes during instrument operation, resulting in low fault prediction accuracy. In addition, traditional fault prediction models usually use fixed parameters and strategies, which cannot be adjusted and optimized in real time when faced with fluctuations in operating conditions or changes in data. This leads to low prediction efficiency and fails to meet the requirements of industrial production for timely and accurate fault prediction. Once an instrument fails, it often cannot provide early warning, which can easily lead to serious consequences such as production interruption and equipment damage, causing huge economic losses and safety risks to enterprises.

[0004] In summary, existing industrial flow meter fault prediction technologies have significant limitations and are no longer able to meet the growing demands of modern industrial production. Therefore, it is particularly important to develop a fault prediction-based industrial flow meter and diagnostic analysis system based on deep learning algorithms. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a fault prediction industrial flow meter and diagnostic analysis system based on deep learning algorithms. It can achieve accurate perception and dynamic analysis of the operating status of industrial flow meters by constructing a multi-module collaborative architecture and combining a dynamic adaptive adjustment mechanism based on reinforcement learning with a fault prediction model of hybrid neural networks. This will improve the accuracy and efficiency of fault prediction in complex industrial environments, thus meeting the high requirements of modern industrial production for instrument fault prediction.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a fault prediction-based industrial flow meter and diagnostic analysis system based on deep learning algorithms. This system comprises the following components: a data acquisition module, a data processing module, a reinforcement learning-based dynamic adaptive learning module, a fault prediction module, and a diagnostic analysis module. The data acquisition module collects various data during the operation of the industrial flow meter and transmits the pre-processed raw data to the data processing module. The data processing module performs data cleaning, feature extraction, and data normalization on the received data, converting it into a format suitable for input to the deep learning model, and then transmits it to the reinforcement learning-based dynamic adaptive learning module. The system comprises a learning module and a fault prediction module. The dynamic adaptive learning module based on reinforcement learning includes a state definition submodule, an action definition submodule, a reward function design submodule, and a reinforcement learning algorithm execution submodule, used to adjust the fault prediction model parameters according to changes in industrial site conditions and the state of the fault prediction model. The fault prediction module is built based on a deep learning algorithm, initially trained using traditional training methods, and during runtime receives the adjusted model parameters to perform fault prediction, transmitting the results to the diagnostic analysis module. The diagnostic analysis module analyzes and processes the received fault prediction results, combines historical fault data and expert knowledge to generate a fault diagnosis report, and displays it to the operators.

[0007] Furthermore, the data acquisition module employs a multi-sensor collaborative acquisition strategy. Specifically, it uses a time-difference ultrasonic flow sensor to collect flow data, a piezoresistive pressure sensor to acquire pressure data, a thermocouple temperature sensor to measure temperature data, and an accelerometer to collect vibration data. The sampling frequency of each sensor is not fixed but dynamically adjusted based on the actual operating characteristics of the instrument. This adjustment process involves analyzing historical operating data to determine an initial sampling frequency that balances the instrument's operating status with the burden of control data processing. Simultaneously, the system calculates fluctuations in flow, pressure, and temperature data in real time to assess the complexity of the current operating condition. As the complexity of the condition increases, the sampling frequency increases accordingly, and vice versa. In addition, the acquired data first passes through a hardware filtering circuit to remove high-frequency noise interference before being transmitted to the data processing module. This multi-sensor collaborative acquisition strategy can comprehensively acquire instrument operating information from multiple dimensions, providing a rich and accurate data foundation for subsequent fault prediction and effectively overcoming the limitations of single-sensor data and the tendency to miss faults.

[0008] Furthermore, during the feature extraction process, the data processing module employs an improved local feature analysis algorithm. For flow data, the system divides its time series into overlapping subsequences. For each subsequence, it calculates its local mean and local standard deviation to reflect the central tendency and dispersion of the subsequence data. Subsequently, by calculating the rate of change of the local mean and local standard deviation of adjacent subsequences, the dynamic change characteristics of the flow data are obtained. After completing the local feature extraction of various types of data, the features are fused to form a complete feature vector. Compared with traditional feature extraction methods, this improved local feature analysis algorithm can more accurately capture the change characteristics of data within a local range, providing more discriminative and representative input features for the fault prediction model, thereby effectively improving the accuracy of fault prediction and enabling the system to discover potential fault hazards of industrial flow meters more promptly and accurately.

[0009] Furthermore, in the state definition submodule of the reinforcement learning-based dynamic adaptive learning module, a hierarchical encoding strategy is adopted when encoding the parameter states of the fault prediction model. For the first state of the deep learning model, the hierarchical encoding strategy is applied. Layer, weight matrix Divided by row Each small piece Its singular value vector is obtained through singular value decomposition. The singular value vectors of all the small blocks are concatenated together, and then combined with the bias vector of that layer. The parameters are concatenated to form the parameter encoding vector of this layer. By sequentially concatenating the parameter encoding vectors of all layers, the parameter state encoding of the entire model is obtained. At the same time, principal component analysis is used to reduce the dimensionality of the feature vectors of the working condition data, retaining the main feature dimensions, and then concatenating them with the model parameter state encoding to form the final state vector. This hierarchical encoding strategy can effectively compress the representation dimension of the model parameters, while retaining key information and reasonably integrating the features of the working condition data, making the definition of the state space more compact and representative, which helps reinforcement learning algorithms to learn model parameter adjustment strategies more efficiently.

[0010] Furthermore, in the action definition submodule of the reinforcement learning-based dynamic adaptive learning module, the adjustment of model parameters by actions adopts a dynamic adjustment method based on gradient direction and magnitude, for the model's weight matrix. Calculate its gradient on the current training data. Define the range of motion adjustment ,in To adjust the global coefficients, experiments were conducted on the model's training data under different operating conditions. The goal was to enable the model to converge quickly while ensuring stability. A grid search method was used to determine the optimal values. The gradient of the model weight matrix on the current training data. The sign function of the gradient is used to determine the direction of adjustment. Let L be the 2-norm of the gradient. To adjust the power factor of the amplitude, an adaptive learning algorithm is used, based on the model's complexity and the characteristics of the training data. This algorithm continuously adjusts the power factor to optimize the model's prediction error and training time. For the adjustment of the learning rate and regularization parameters, a dynamic adjustment rule based on the model training state is adopted. This gradient-based dynamic action adjustment method can adjust the model parameters more reasonably according to the current training state of the model and the characteristics of the data. Compared with the traditional fixed adjustment method, it can significantly improve the training efficiency and fault prediction performance of the model.

[0011] Furthermore, the reward function designed by the reward function design submodule in the reinforcement learning-based dynamic adaptive learning module is... Taking multiple factors into consideration, the specific formula is as follows: ,in For fault prediction accuracy, The reciprocal of the model's convergence time, normalized to... interval, As a stability index for the prediction results, it is calculated by continuous The inverse of the variance of the prediction result is obtained, and it is also normalized to... interval, The weight coefficients are determined as follows: A multi-objective optimization model is established with the objectives of maximizing fault prediction accuracy, minimizing model convergence time, and maximizing prediction result stability. The weight coefficients are optimized using a genetic algorithm. Multiple experiments are conducted on different industrial field working conditions to select the combination of weight coefficients that achieves the best overall performance. This reward function can comprehensively evaluate the impact of actions on model performance, guide the reinforcement learning algorithm to learn better parameter adjustment strategies, and effectively balance the model's prediction accuracy, convergence speed, and stability.

[0012] Furthermore, the reinforcement learning algorithm execution submodule in the reinforcement learning-based dynamic adaptive learning module employs an improved dual-deep Q-network algorithm and introduces a priority sampling mechanism for the experience replay pool. For each experience tuple in the experience replay pool... Calculate its priority ,in To prevent the priority from being zero, a very small constant is used. During sampling, samples are taken according to the priority ratio, so that important empirical tuples have a higher probability of being selected for training. At the same time, the update method of the target network is improved by adopting a soft update strategy. ,in The value function of the target network, To evaluate the network's value function, The optimal value of the soft update coefficient is determined by simulation experiments on actual operating data from different industrial sites, aiming to reduce the overestimation problem and improve the stability of the algorithm. The improved DDQN algorithm can make more efficient use of empirical data, reduce the instability of the algorithm, and improve the learning efficiency and accuracy of reinforcement learning algorithm in parameter adjustment of industrial flow meter fault prediction model.

[0013] Furthermore, the fault prediction module employs a hybrid neural network architecture when building the model based on deep learning algorithms. This architecture consists of a convolutional neural network (CNN) and a long short-term memory (LSTM) network. The CNN is used to extract spatial features of traffic and pressure data, while the LSTM is used to handle long-term dependencies in time-series data. An attention mechanism is introduced into the network structure; attention weights are calculated for the feature maps output by the CNN and the hidden states output by the LSTM, and for each time step... Hidden state Calculate attention weights ,in , This is the weight matrix. For bias vectors, For input data, Given the time series length, the hidden states are weighted and summed according to the attention weights to obtain the output with the attention mechanism. This hybrid neural network architecture combines the advantages of CNN and LSTM, and enhances the network's ability to capture key information through the attention mechanism. Compared with a single neural network model, it can more accurately predict the faults of industrial flow meters.

[0014] Furthermore, when generating a fault diagnosis report, the diagnostic analysis module employs knowledge graph reasoning technology. First, it constructs an industrial flow meter fault knowledge graph, storing fault phenomena, causes, and handling measures from historical fault cases in the form of triples. For the prediction results output by the fault prediction module, it searches and reasons within the knowledge graph. When a certain type of fault is predicted, it uses relationship queries within the knowledge graph to find all possible causes and historical handling experiences related to that fault. These causes are then ranked probably based on current operating data. Simultaneously, the reasoning results are further verified and supplemented using an expert knowledge rule base. Finally, the fault type, possible causes, probability ranking, historical handling experience, and targeted maintenance suggestions based on current operating conditions are compiled into a detailed fault diagnosis report. This knowledge graph reasoning technology fully utilizes historical fault data and expert knowledge to provide operators with more comprehensive, accurate, and actionable fault diagnosis information, effectively assisting in fault handling decisions.

[0015] Compared with existing technologies, this fault prediction industrial flow meter and diagnostic analysis system based on deep learning algorithms has the following advantages: First, through a reinforcement learning-based dynamic adaptive learning module, and through unique state definition, action adjustment, reward function design, and reinforcement learning algorithm execution mechanism, this system can optimize fault prediction model parameters in real time according to changes in operating conditions. The hybrid neural network architecture of the fault prediction module, combined with an attention mechanism, further enhances the ability to capture fault features. All modules work closely together and optimize collaboratively. Compared with traditional fault prediction systems, in complex and ever-changing industrial environments, the fault prediction accuracy is greatly improved, and the prediction efficiency is significantly increased. It can detect potential faults in advance and effectively avoid production interruptions and economic losses caused by instrument failures.

[0016] Second, this system utilizes innovative algorithms such as dynamic parameter adjustment based on gradient direction and amplitude, and an improved dual-depth Q-network algorithm. These algorithms enable the system to dynamically adjust its strategies according to the model training state and industrial site conditions, effectively addressing complex fluctuations under different operating conditions and enhancing system adaptability. The hierarchical coding strategy and operating condition data fusion method in the state definition submodule, along with the reward function design submodule's comprehensive multi-factor reward function and optimized weight coefficient determination method, further improve the accuracy and rationality of the system's model parameter adjustments. Simultaneously, the diagnostic analysis module employs knowledge graph reasoning technology, combining historical fault data and expert knowledge to deeply analyze and verify fault prediction results, generating detailed and actionable fault diagnosis reports. The integrated application of these innovative technologies not only improves the reliability of system fault prediction and diagnosis but also provides strong support for the intelligent operation and maintenance of industrial flow meters, enhancing the intelligence level and safety of industrial production.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 is a flowchart of the functional implementation of the fault prediction industrial flow meter and diagnostic analysis system based on deep learning algorithm; Figure 2 is an overall flowchart of the fault prediction industrial flow meter and diagnostic analysis system based on deep learning algorithm. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] Example 1: In the raw material transportation pipeline system of a large chemical production enterprise, a fault prediction type industrial flow meter and diagnostic analysis system based on deep learning algorithm was applied. The system can monitor data such as raw material flow, pressure, temperature and instrument vibration in the pipeline in real time. By predicting instrument failures in advance and assisting in diagnosis, the risk of production interruption caused by instrument failures is effectively reduced, ensuring the continuity and safety of chemical production.

[0022] After system startup, the data acquisition module employs a multi-sensor collaborative acquisition strategy. It accurately acquires the flow rate data of the raw materials within the pipeline using a time-difference ultrasonic flow sensor, reliably obtains the pressure data using a piezoresistive pressure sensor, accurately measures the temperature data of the raw materials using a thermocouple temperature sensor, and sensitively acquires vibration data during instrument operation using an accelerometer. The sampling frequency of each sensor is not fixed but dynamically adjusted according to the characteristics of the raw material flow rate and pressure fluctuations during actual instrument operation. This ensures comprehensive data acquisition while avoiding unnecessary resource waste. For example, when the raw material flow rate suddenly increases, increasing the sampling frequency can capture data changes more precisely, providing richer raw information for subsequent analysis. Furthermore, the system calculates the fluctuations in flow rate, pressure, and temperature data in real time to assess the complexity of the current operating conditions. This operation provides a basis for subsequent module processing, allowing the entire system to better adapt to different production states. The acquired data first passes through a hardware filtering circuit to remove high-frequency noise interference. This step reduces the impact of noise on subsequent data processing and model prediction, ensuring high-quality data transmitted to the data processing module.

[0023] After receiving the data, the data processing module first performs data cleaning to remove outliers and missing values. This eliminates the interference of invalid data on the analysis results, laying a reliable data foundation for subsequent processing. Next, feature extraction is performed using an improved local feature analysis algorithm. For flow data, its time series is divided into overlapping subsequences, and the local mean and local standard deviation of each subsequence are calculated. Then, the dynamic change characteristics of the flow data are obtained by calculating the rate of change of the local mean and local standard deviation of adjacent subsequences. Similar local feature extraction is then performed on data such as pressure, temperature, and vibration, and all features are fused. This process extracts key features that reflect the instrument's operating status from the raw data, enhancing the representativeness and discriminative power of the data, enabling the model to more accurately identify fault modes. Finally, data normalization is performed, converting the data into a format suitable for deep learning model input and transmitting it to the reinforcement learning-based dynamic adaptive learning module and fault prediction module. This step unifies the data scale, avoiding adverse effects on model training and prediction due to differences in data volume, and improving the model's stability and convergence speed.

[0024] The dynamic adaptive learning module based on reinforcement learning, in its state definition submodule, employs a hierarchical encoding strategy when encoding the parameter states of the fault prediction model. For the first state of the deep learning model... Layer, weight matrix Divided by row Each small piece Its singular value vector is obtained through singular value decomposition. The singular value vectors of all the small blocks are concatenated together, and then combined with the bias vector of that layer. The parameters are concatenated to form the parameter encoding vector of this layer. The parameter encoding vectors of all layers are concatenated in sequence to obtain the parameter state encoding of the entire model. At the same time, principal component analysis is used to reduce the dimensionality of the feature vectors of the working condition data, and then concatenated with the model parameter state encoding to form the final state vector. The purpose of this is to more comprehensively and efficiently represent the model parameters and working condition information, and to provide a clear state basis for subsequent action decisions.

[0025] The action definition submodule adjusts the model parameters dynamically based on gradient direction and magnitude, including the model's weight matrix. Calculate its gradient on the current training data. Define the range of motion adjustment ,in This is the global adjustment coefficient. The gradient of the model weight matrix on the current training data. Let be the sign function of the gradient. Let L be the 2-norm of the gradient. The power factor for adjusting the amplitude is used to enable targeted optimization of model parameters based on actual conditions, thereby improving the model's adaptability to different working conditions.

[0026] The reward function design submodule calculates the reward based on a reward function that comprehensively considers fault prediction accuracy, the reciprocal of model convergence time, and the stability index of prediction results. The designed reward function... Taking multiple factors into consideration, the specific formula is as follows: ,in For fault prediction accuracy, The reciprocal of the model's convergence time. As a stability index for the prediction results, The weights are used as coefficients. This reward function guides the reinforcement learning process towards a better outcome, ensuring the model maintains prediction accuracy while balancing convergence speed and result stability. The reinforcement learning algorithm execution submodule employs an improved dual-deep Q-network algorithm, introducing a priority sampling mechanism for the experience replay pool. For each experience tuple in the experience replay pool... Calculate its priority ,in This is a very small constant, and the update method of the target network is improved by adopting a soft update strategy. ,in The value function of the target network, To evaluate the network's value function, The soft update coefficients are used, and a soft update strategy is adopted for the target network. The parameters of the fault prediction model are adjusted according to the changes in industrial site conditions and the state of the fault prediction model. The purpose of this process is to enable the model to dynamically adapt to the complex and ever-changing working conditions in chemical production, continuously optimize the model performance, and ensure the accuracy and timeliness of fault prediction.

[0027] The fault prediction module is built on deep learning algorithms, employing a hybrid neural network architecture consisting of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The CNNs extract spatial features from traffic and stress data, while the LSTM network handles long-term dependencies in time-series data. An attention mechanism is introduced into the network structure; attention weights are calculated for the feature maps output by the CNN and the hidden states output by the LSTM, and for each time step... Hidden state Calculate attention weights ,in , This is the weight matrix. For bias vectors, For input data, Given the time series length, the hidden states are weighted and summed according to attention weights to obtain an output with an attention mechanism. The purpose of this architecture is to fully explore the spatial and temporal features in the data, and at the same time, to highlight the influence of important information through the attention mechanism, thereby improving the model's ability to capture fault features. This module has been trained using traditional training methods in the initial stage. During runtime, it receives the model parameters adjusted by the dynamic adaptive learning module based on reinforcement learning, and predicts possible faults of the instrument. Its role is to discover potential fault risks in advance and provide prediction results for subsequent diagnostic analysis; and then transmit the results to the diagnostic analysis module.

[0028] After receiving the fault prediction results, the diagnostic analysis module uses knowledge graph reasoning technology for analysis and processing. This module has constructed an industrial flow meter fault knowledge graph, storing information such as fault phenomena, fault causes, and handling measures from historical fault cases in the form of triples. The role of the knowledge graph is to integrate historical fault knowledge and provide rich experience support for fault reasoning. The module searches and reasons about the prediction results in the knowledge graph, and at the same time, combines the expert knowledge rule base to verify and supplement the reasoning results. This step is to improve the accuracy and reliability of fault diagnosis and ensure the scientific nature of the diagnostic results. Finally, a fault diagnosis report is generated, which includes information such as fault type, possible causes, probability ranking, historical handling experience, and targeted maintenance suggestions based on the current operating conditions. This report is then displayed to the operators. The purpose of this report is to provide operators with clear and specific guidance to help them maintain the instruments in a timely and effective manner, avoid production interruptions caused by instrument failures, and ensure the smooth operation of chemical production.

[0029] Example 2 describes the deployment of a fault prediction-based industrial flow meter and diagnostic analysis system in the main pipeline flow monitoring of an urban water supply system. This system can track the flow rate, pressure, and temperature changes of tap water in the pipeline in real time, as well as the vibration of the meter itself. By predicting potential faults in the flow meter in advance and performing accurate diagnosis, it provides maintenance personnel with timely maintenance information, thereby ensuring the stable operation of the urban water supply system and avoiding problems such as inaccurate water metering and failure to detect pipeline abnormalities in a timely manner due to meter failure, thus ensuring normal water use for residents and businesses.

[0030] After system startup, the data acquisition module employs a multi-sensor collaborative acquisition strategy. It uses a time-difference ultrasonic flow sensor to collect flow data of tap water in the pipeline, a piezoresistive pressure sensor to acquire pressure data, and a thermocouple temperature sensor to measure tap water temperature. Simultaneously, an accelerometer collects vibration data from the instruments. The sampling frequency of each sensor is dynamically adjusted according to the actual characteristics of the instruments during different operating periods, such as peak and off-peak water supply periods. For example, during peak water supply periods, increasing the sampling frequency allows for more intensive capture of data changes. This dynamic adjustment reduces unnecessary energy consumption and data storage pressure while maintaining monitoring accuracy, ensuring that the collected data reflects the actual operating conditions without wasting resources. The system calculates fluctuations in flow, pressure, and temperature data in real time to assess the complexity of the current operating conditions. This allows the system to perceive changes in the water supply status, providing a foundational information for subsequent modules. The acquired data first passes through a hardware filtering circuit to remove high-frequency noise interference. This step filters out interference signals from the external environment (such as pipeline vibration and motor operation), ensuring cleaner data transmitted to the data processing module and improving the accuracy of subsequent processing.

[0031] The data processing module processes the received data. First, it cleans the data to remove invalid data. This operation eliminates outliers and missing values ​​caused by temporary sensor malfunctions, transmission interference, etc., preventing these poor data from affecting the reliability of subsequent analysis results. Next, it performs feature extraction using an improved local feature analysis algorithm. For flow data, the time series is divided into overlapping subsequences, and the local mean and local standard deviation of each subsequence are calculated. Then, the dynamic change characteristics are obtained by the rate of change of the local mean and local standard deviation of adjacent subsequences. Subsequently, local features are extracted and fused for pressure, temperature, and vibration data. This process extracts key features that reflect the instrument's operating status from massive amounts of raw data. These features can more intuitively reflect whether the instrument has any abnormalities, providing more valuable input for subsequent fault prediction. Finally, it performs data normalization, converting the data into a format suitable for deep learning model input and transmitting it to the reinforcement learning-based dynamic adaptive learning module and fault prediction module. This step unifies the magnitude and distribution of different types of data, avoiding the model's oversensitivity to certain parameters due to differences in data scale, thereby improving the stability of model training and the accuracy of prediction.

[0032] The reinforcement learning-based dynamic adaptive learning module begins operation. The state definition submodule employs a hierarchical encoding strategy to encode the state parameters of the fault prediction model. After dimensionality reduction through principal component analysis of the feature vectors of the operating data, it concatenates them with the encoded model parameter states to form a state vector. This transforms complex model parameters and operating information into a concise and comprehensive state representation, allowing the module to accurately grasp the overall situation of the current model and operating conditions, providing a clear basis for subsequent parameter adjustments. The action definition submodule adjusts the model parameters dynamically based on gradient direction and amplitude. Its purpose is to enable targeted optimization of model parameters according to actual prediction results and changes in operating conditions, ensuring the model maintains good performance under different water supply scenarios. The reward function design submodule... The module calculates rewards based on a reward function that combines the overall fault prediction accuracy, the reciprocal of the model convergence time, and the stability index of the prediction results. This reward function sets a clear optimization objective for the reinforcement learning process, guiding the module to improve fault prediction accuracy, accelerate model convergence, and ensure the stability of prediction results when adjusting parameters. The reinforcement learning algorithm execution submodule uses an improved dual-deep Q-network algorithm, introduces a priority sampling mechanism of the experience replay pool, and adopts a soft update strategy for the target network. It adjusts model parameters according to changes in the water supply system's operating conditions and the fault prediction model's state. This process enables the model to dynamically adapt to fluctuations in parameters such as flow and pressure in the urban water supply system, continuously optimize its performance, and ensure accurate fault prediction under different operating conditions.

[0033] The fault prediction module is built on deep learning algorithms, employing a hybrid neural network architecture consisting of convolutional neural networks and long short-term memory networks. The convolutional neural network extracts spatial features from flow and pressure data, while the long short-term memory network processes long-term dependencies in time-series data. An attention mechanism is also introduced. This architecture fully mines spatial correlation information and temporal variation patterns in the data, while the attention mechanism focuses on information more critical to fault prediction, significantly improving the model's ability to identify early fault characteristics. Initially trained using traditional methods, the module receives adjusted model parameters during runtime to perform fault prediction. Its function is to detect potential instrument faults in advance, giving maintenance personnel more time for repairs, and then transmits the results to the diagnostic analysis module.

[0034] After receiving the prediction results, the diagnostic analysis module uses knowledge graph reasoning technology to search and reason within the constructed industrial flow meter fault knowledge graph. This knowledge graph stores triple information such as fault phenomena, causes, and handling measures from historical fault cases, providing rich historical experience support for fault diagnosis and helping the module quickly locate possible fault causes. Simultaneously, it verifies and supplements the reasoning results using an expert knowledge rule base. This step leverages the professional experience of experts to further improve the accuracy and reliability of the diagnostic results. Finally, a fault diagnosis report is generated, containing information such as fault type, possible causes, probability ranking, historical handling experience, and maintenance suggestions for the current water supply conditions. This report provides clear and specific maintenance guidance to help maintenance personnel quickly take effective maintenance measures, promptly eliminate instrument faults, ensure the normal operation of the urban water supply system, and avoid impacting water use for residents and businesses due to instrument problems.

[0035] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A fault prediction-based industrial flow meter and diagnostic analysis system based on deep learning algorithms, characterized in that, The system comprises the following components: a data acquisition module, a data processing module, a reinforcement learning-based dynamic adaptive learning module, a fault prediction module, and a diagnostic analysis module. The data acquisition module collects various data during the operation of industrial flow meters and transmits the raw data to the data processing module after preliminary preprocessing. The data processing module cleans, extracts features, and normalizes the received data, converting it into a format suitable for deep learning model input, and then transmits it to the reinforcement learning-based dynamic adaptive learning module and the fault prediction module. The reinforcement learning-based dynamic adaptive learning module includes a state definition submodule, an action definition submodule, a reward function design submodule, and a reinforcement learning algorithm execution submodule, used to adjust the fault prediction model parameters according to changes in industrial site conditions and the fault prediction model's state. The fault prediction module is built based on a deep learning algorithm, initially trained using traditional training methods, and during runtime, receives the adjusted model parameters to perform fault prediction and transmits the results to the diagnostic analysis module. The diagnostic analysis module analyzes and processes the received fault prediction results, combines historical fault data and expert knowledge to generate a fault diagnosis report, and displays it to the operators.

2. The fault prediction type industrial flow meter and diagnostic analysis system based on deep learning algorithm according to claim 1, characterized in that, The data acquisition module adopts a multi-sensor collaborative acquisition strategy. Specifically, it uses a time-difference ultrasonic flow sensor to collect flow data, a piezoresistive pressure sensor to acquire pressure data, a thermocouple temperature sensor to measure temperature data, and an accelerometer to collect vibration data. The sampling frequency of each sensor is not fixed and is dynamically adjusted according to the actual operating characteristics of the instrument.

3. The fault prediction type industrial flow meter and diagnostic analysis system based on deep learning algorithm according to claim 1, characterized in that, During the feature extraction process, the data processing module employs an improved local feature analysis algorithm. For traffic data, the system divides its time series into overlapping subsequences. For each subsequence, it calculates its local mean and local standard deviation. Then, by calculating the rate of change of the local mean and local standard deviation of adjacent subsequences, the dynamic change characteristics of the traffic data are obtained. After completing the local feature extraction of various types of data, the features are fused.

4. The fault prediction type industrial flow meter and diagnostic analysis system based on deep learning algorithm according to claim 1, characterized in that, In the reinforcement learning-based dynamic adaptive learning module, the state definition submodule employs a hierarchical encoding strategy when encoding the parameter states of the fault prediction model. For the first state of the deep learning model... Layer, weight matrix Divided by row Each small piece Its singular value vector is obtained through singular value decomposition. The singular value vectors of all the small blocks are concatenated together, and then combined with the bias vector of that layer. The parameters are concatenated to form the parameter encoding vector of this layer. By concatenating the parameter encoding vectors of all layers in sequence, the parameter state encoding of the entire model is obtained.

5. The fault prediction type industrial flow meter and diagnostic analysis system based on deep learning algorithm according to claim 1, characterized in that, In the action definition submodule of the reinforcement learning-based dynamic adaptive learning module, the adjustment of model parameters by actions adopts a dynamic adjustment method based on gradient direction and magnitude. For the model's weight matrix... Calculate its gradient on the current training data. Define the range of motion adjustment ,in This is the global adjustment coefficient. The gradient of the model weight matrix on the current training data. Let be the sign function of the gradient. Let L be the 2-norm of the gradient. This is the power factor for adjusting the magnitude.

6. The fault prediction type industrial flow meter and diagnostic analysis system based on deep learning algorithm according to claim 1, characterized in that, The reward function designed by the reward function design submodule in the reinforcement learning-based dynamic adaptive learning module is... Taking multiple factors into consideration, the specific formula is as follows: ,in For fault prediction accuracy, The reciprocal of the model's convergence time. As a stability index for the prediction results, These are the weighting coefficients.

7. The fault prediction type industrial flow meter and diagnostic analysis system based on deep learning algorithm according to claim 1, characterized in that, The reinforcement learning algorithm execution submodule in the reinforcement learning-based dynamic adaptive learning module employs an improved dual-deep Q-network algorithm and introduces a priority sampling mechanism for the experience replay pool. For each experience tuple in the experience replay pool... Calculate its priority ,in This is a very small constant, and the update method of the target network is improved by adopting a soft update strategy. ,in The value function of the target network, To evaluate the network's value function, This is the soft update coefficient.

8. The fault prediction type industrial flow meter and diagnostic analysis system based on deep learning algorithm according to claim 1, characterized in that, The fault prediction module employs a hybrid neural network architecture when building the model based on deep learning algorithms. This architecture consists of a convolutional neural network (CNN) and a long short-term memory (LSTM) network. The CNN is used to extract spatial features of traffic and pressure data, while the LSTM is used to handle long-term dependencies in time-series data. An attention mechanism is introduced into the network structure; attention weights are calculated for the feature maps output by the CNN and the hidden states output by the LSTM, and for each time step... Hidden state Calculate attention weights ,in ， This is the weight matrix. For bias vectors, For input data, This represents the length of the time series.

9. The fault prediction type industrial flow meter and diagnostic analysis system based on deep learning algorithm according to claim 1, characterized in that, When generating a fault diagnosis report, the diagnostic analysis module employs knowledge graph reasoning technology. First, it constructs an industrial flow meter fault knowledge graph, storing fault phenomena, causes, and handling measures from historical fault cases in the form of triples. For the prediction results output by the fault prediction module, it searches and reasons within the knowledge graph. Simultaneously, it combines expert knowledge rule bases to further verify and supplement the reasoning results. Finally, it compiles the fault type, possible causes, probability ranking, historical handling experience, and targeted maintenance suggestions based on the current operating conditions into a detailed fault diagnosis report.

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