Pipeline coal conveying intelligent monitoring and fault diagnosis system and method

Abstract

The invention discloses an intelligent monitoring and fault diagnosis system for pipeline coal transportation, and belongs to the technical field of pipeline coal transportation. The system comprises a data acquisition module which is used for monitoring pipeline pressure, flow, coal components and pipeline wall thickness key parameters in real time; the data transmission module is used for performing arrangement and format conversion on the data acquired by the sensor; the big data analysis and machine learning model training module is used for receiving the converted data, learning and training historical fault data and normal operation data by using a machine learning algorithm, and constructing a fault diagnosis model; the fault diagnosis analysis module is used for applying the trained fault diagnosis model to actual monitoring; and the fault positioning and alarming module is used for determining the specific position of the fault according to the diagnosis result and triggering an alarming mechanism. According to the invention, the fault diagnosis model is trained through a machine learning algorithm, the pipeline coal conveying data collected in real time can be accurately and rapidly analyzed, and the type and position of a potential fault can be found and identified in time.

Description

Technical Field

[0001] This invention relates to the field of pipeline coal transportation technology, and more specifically, to an intelligent monitoring and fault diagnosis system and method for pipeline coal transportation. Background Technology

[0002] In the field of pipeline coal transportation, traditional monitoring and fault diagnosis methods mainly rely on manual inspections and simple sensor monitoring. Manual inspections have many drawbacks, such as low efficiency, environmental limitations, and difficulty in real-time monitoring. Simple sensor monitoring can only provide local and single parameter information, and cannot comprehensively reflect the complex operating status of the pipeline coal transportation system. Although existing automated monitoring systems can collect some data, they mostly operate independently with low data fusion, making it difficult to achieve accurate fault diagnosis and location.

[0003] These problems severely impact the operational efficiency and safety of pipeline coal conveying systems, increase maintenance costs and downtime, and limit the modernization of the pipeline coal conveying industry. Therefore, we propose an intelligent monitoring and fault diagnosis system for pipeline coal conveying. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent monitoring and fault diagnosis system and method for coal pipeline transportation, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: An intelligent monitoring and fault diagnosis system for pipeline coal transportation includes a data acquisition module. Multiple high-precision sensors are deployed at key locations in the pipeline coal transportation system to monitor key parameters such as pipeline pressure, flow rate, coal composition, and pipeline wall thickness in real time. Key locations include the pipeline's starting point, ending point, branch points, valve positions, and easily blocked areas. The data transmission module, connected to the data acquisition module, is used to organize and convert the data collected by the sensors. The big data analysis and machine learning model training module is connected to the data transmission module. It is used to receive the converted data, use machine learning algorithms to learn and train on historical fault data and normal operation data, and build a fault diagnosis model. The fault diagnosis and analysis module is connected to the big data analysis and machine learning model training module. It is used to apply the trained fault diagnosis model to actual monitoring, perform fault diagnosis and analysis on real-time data, and identify potential fault types and locations. The fault location and alarm module is connected to the fault diagnosis and analysis module. It is used to determine the specific location of the fault based on the diagnosis results and trigger the alarm mechanism.

[0006] Preferably, the various high-precision sensors include pressure sensors, flow sensors, coal composition sensors, and pipe wall thickness detection sensors.

[0007] Preferably, the big data analysis and machine learning model training module also includes a data preprocessing unit, which is used to clean historical data, remove noise points, outliers and missing values, and perform standardization processing using the Z-Score standardization method.

[0008] Preferably, the big data analysis and machine learning model training module further includes a feature extraction and selection unit, which is used to extract time-domain features, frequency-domain features, pressure gradient, and flow fluctuation cycle features from the preprocessed data, and to select fault-related features using correlation analysis and principal component analysis (PCA).

[0009] Preferably, in the big data analysis and machine learning model training module, a fault diagnosis model is constructed using a deep neural network (DNN), implemented through the TensorFlow or PyTorch framework. The model initialization adopts the He initialization method and includes a dropout layer.

[0010] Preferably, the fault location and alarm module uses the pressure wave propagation time difference method for fault location. By measuring the time difference between the pressure waves received by adjacent pressure sensors and the propagation speed of the pressure waves in the coal slurry, the location of the fault point is calculated.

[0011] A method for intelligent monitoring and fault diagnosis of coal conveying pipelines, applied to the above system, includes the following steps: Data acquisition: Multiple high-precision sensors are deployed at the pipeline's starting point, ending point, branch points, valve locations, and easily clogged areas to monitor key parameters such as pipeline pressure, flow rate, coal composition, and pipeline wall thickness in real time; Data transmission: The data collected by the sensors is processed and converted in format by the data acquisition and transmission module, and then transmitted to the big data analysis and machine learning model training module. Model building: In the big data analysis and machine learning model training module, machine learning algorithms are used to learn and train on historical fault data and normal operation data to build a fault diagnosis model; Diagnostic analysis: The trained fault diagnosis model is applied to actual monitoring to perform fault diagnosis analysis on real-time data and identify potential fault types and locations; Fault Alarm: Based on the fault diagnosis results, determine the specific location of the fault and trigger the alarm mechanism.

[0012] Preferably, in the model building step, the Adam optimization algorithm is used to train the deep neural network model. The model performance is evaluated by dividing the training set, validation set and test set, using the cross-entropy loss function, and the model is evaluated by the accuracy, recall, F1 score and AUC-ROC index.

[0013] Preferably, in the fault alarm step, multi-level alarm thresholds are set according to the fault type. When the predicted probability of a blockage fault exceeds 80%, a level one alarm is triggered, and when the probability is between 50% and 80%, a level two alarm is triggered. The alarm methods include audible and visual alarms, SMS alarms, email alarms, and instant messaging software alarms.

[0014] Preferably, the method also includes an online model update step: using the mini-batch stochastic gradient descent method, the fault diagnosis model is periodically retrained and its parameters are updated using new data accumulated during system operation to adapt to dynamic changes in the system.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention utilizes a fault diagnosis model trained with machine learning algorithms to accurately and rapidly analyze real-time collected pipeline coal conveying data, promptly identifying and pinpointing potential fault types and locations. Compared to traditional diagnostic methods based on thresholds and simple logical judgments, this patent improves both diagnostic accuracy and speed, shortening fault location time and enabling maintenance personnel to quickly take targeted measures, effectively reducing downtime and maintenance costs caused by faults. It addresses the problems of low efficiency and environmental limitations of manual inspections, limited information from simple sensors, low data fusion, and insufficient intelligence in fault diagnosis in existing technologies, thereby improving the efficiency and safety of pipeline coal conveying systems. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a flowchart of the big data analysis and machine learning model training process of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] Example: Please see Figure 1-2An intelligent monitoring and fault diagnosis system for pipeline coal transportation is disclosed, comprising a data acquisition module. Multiple high-precision sensors are deployed at key locations within the pipeline coal transportation system to monitor in real time key parameters such as pipeline pressure, flow rate, coal composition, and pipeline wall thickness. Key locations include the pipeline's starting point, ending point, branch points, valve positions, and areas prone to blockage. Based on the layout and process flow of the pipeline coal transportation system, critical monitoring points are identified.

[0019] Specifically, various high-precision sensors include pressure sensors, flow sensors, coal composition sensors, and pipe wall thickness sensors. Pressure sensors: used to monitor pressure changes within the pipeline in real time, promptly detecting pressure anomalies, such as pressure fluctuations caused by blockages or leaks. Flow sensors: monitor the flow rate of coal slurry within the pipeline; abnormal flow rates may indicate pipeline blockage or equipment malfunction. Coal composition sensors: detect the composition of the coal slurry, such as ash content, moisture, and particle size, ensuring the coal quality meets production process requirements. Pipe wall thickness sensors: periodically or continuously monitor the thickness of the pipe wall, promptly detecting pipe wear or corrosion.

[0020] The data transmission module, connected to the data acquisition module, is used to organize and convert the data collected by the sensors.

[0021] Specifically, data acquisition involves connecting data acquisition devices (such as data acquisition cards and terminals) to sensors and collecting sensor data according to set time intervals or trigger conditions. The acquired data includes pressure values, flow rates, coal composition parameters, and pipe wall thickness measurements. Data processing and format conversion involves preliminary processing of the collected raw data, including noise removal, data standardization, and format unification, ensuring data integrity and consistency. Data transmission utilizes wired or wireless communication technologies (such as Industrial Ethernet, ZigBee, LoRa, etc.) to transmit the processed data to the big data analysis and machine learning model training module. This ensures the stability and security of data transmission, preventing data loss or corruption.

[0022] The big data analysis and machine learning model training module is connected to the data transmission module. It is used to receive the converted data, use machine learning algorithms to learn and train on historical fault data and normal operation data, and build a fault diagnosis model.

[0023] In this application, the big data analysis and machine learning model training module also includes a data preprocessing unit, which is used to clean historical data, remove noise points, outliers and missing values, and perform standardization processing using the Z-Score standardization method.

[0024] Specifically, 1. Historical Data Collection: A large amount of historical data from the pipeline coal conveying system during its past operation is collected, including sensor data during normal operation and various fault occurrences, along with information such as the fault type, occurrence time, and location. This data comes from sensors installed at multiple key locations, covering operating states under different conditions. 2. Data Cleaning and Processing: The collected historical data is cleaned to remove noise, outliers, and missing values, ensuring data quality and completeness. Then, different types of data are standardized or normalized to ensure they have the same units and numerical range, facilitating subsequent model training. Z-Score standardization is used as the method.

[0025] In this application, the big data analysis and machine learning model training module also includes a feature extraction and selection unit, which is used to extract time-domain features, frequency-domain features, pressure gradient, and flow fluctuation cycle features from the preprocessed data, and to select fault-related features using correlation analysis and principal component analysis (PCA).

[0026] Specifically, 1. Feature Extraction: Features reflecting the operating status and fault characteristics of the pipeline coal transportation system are extracted from the preprocessed data. In addition to basic time and frequency domain features, more targeted features such as pressure gradients and flow fluctuation cycles are extracted based on the physical characteristics of pipeline coal transportation. 2. Feature Selection: Correlation analysis and Principal Component Analysis (PCA) are used to select features highly correlated with fault type and location from the extracted features. Correlation analysis helps determine which features have significant linear or nonlinear correlations with fault labels, thus initially screening features with potential value for fault diagnosis. PCA further reduces the dimensionality of multiple correlated features to a few principal components, reducing data dimensionality while retaining most of the information, improving model training efficiency and generalization ability. Simultaneously, domain expert knowledge can be incorporated to select features crucial for fault diagnosis based on practical experience, ensuring the comprehensiveness and accuracy of feature selection.

[0027] In this application, the big data analysis and machine learning model training module uses a deep neural network (DNN) to build a fault diagnosis model, which is implemented using the TensorFlow or PyTorch framework. The model initialization adopts the He initialization method and includes a dropout layer.

[0028] Specifically, regarding model selection and initialization: based on the characteristics of the fault diagnosis problem and the data features, a deep neural network (DNN) was chosen as the basic model architecture. DNNs have a multi-layered nonlinear transformation structure, enabling them to automatically learn complex patterns and nonlinear relationships in the data, and they have advantages in processing multi-source, heterogeneous pipeline coal conveying sensor data. Specifically, a DNN model was constructed using deep learning frameworks such as TensorFlow or PyTorch. The model contains multiple hidden layers, each consisting of hundreds to thousands of neurons. The model weights were initialized using the He initialization method. The He initialization method can reasonably set initial weight values ​​according to the characteristics of the activation function, avoiding problems such as vanishing gradients or excessively large gradients, and accelerating the model training convergence speed. Furthermore, a dropout layer was added to the model to randomly discard the output of some neurons, preventing overfitting and improving the model's generalization ability.

[0029] The fault diagnosis and analysis module is connected to the big data analysis and machine learning model training module. It is used to apply the trained fault diagnosis model to actual monitoring, perform fault diagnosis and analysis on real-time data, and identify potential fault types and locations.

[0030] The fault location and alarm module is connected to the fault diagnosis and analysis module. It is used to determine the specific location of the fault based on the diagnosis results and trigger the alarm mechanism.

[0031] In this application, the fault location and alarm module uses the pressure wave propagation time difference method for fault location. By measuring the time difference between the pressure waves received by adjacent pressure sensors and the propagation speed of the pressure waves in the coal slurry, the location of the fault point is calculated.

[0032] A method for intelligent monitoring and fault diagnosis of coal conveying pipelines, applied to the above system, includes the following steps: Data acquisition: Multiple high-precision sensors are deployed at the pipeline's starting point, ending point, branch points, valve locations, and easily clogged areas to monitor key parameters such as pipeline pressure, flow rate, coal composition, and pipeline wall thickness in real time.

[0033] Data transmission: The data collected by the sensors is processed and converted in format by the data acquisition and transmission module before being transmitted to the big data analysis and machine learning model training module.

[0034] Model building: In the big data analysis and machine learning model training module, machine learning algorithms are used to learn and train on historical fault data and normal operation data to build a fault diagnosis model.

[0035] Diagnostic Analysis: The trained fault diagnosis model is applied to actual monitoring to perform fault diagnosis analysis on real-time data and identify potential fault types and locations.

[0036] Fault Alarm: Based on the fault diagnosis results, determine the specific location of the fault and trigger the alarm mechanism.

[0037] In the model building step, the Adam optimization algorithm is used to train the deep neural network model. The model performance is evaluated by dividing the training set, validation set and test set, using the cross-entropy loss function, and the model is evaluated by the accuracy, recall, F1 score and AUC-ROC index.

[0038] Specifically, 1. Model training: 1. Dataset Partitioning: The feature-extracted and selected dataset is divided into training, validation, and test sets. A stratified sampling method is used, dividing the dataset according to a certain ratio (e.g., 70%, 15%, 15%) to ensure that the proportion of different fault types and normal samples in each subset is similar to the overall dataset. This allows the model to fully learn the features of various sample types during training and evaluation. 2. Training Process: The model is trained using the training set, employing the Adam optimization algorithm. This algorithm combines momentum and adaptive learning rate, dynamically adjusting the learning rate based on historical parameter gradient information, accelerating model convergence and improving optimization performance. During training, data is fed in batches, each containing a certain number of samples (e.g., 32, 64, or 128). The model's predicted output is calculated through forward propagation, and then the loss function (cross-entropy loss function) is calculated against the actual fault labels to evaluate the accuracy of the model's predictions. Then, backpropagation is performed to calculate gradients and update the model parameters. Regularly validate the model using a validation set, monitor its performance metrics such as accuracy, recall, and F1 score, and adjust the model's hyperparameters, such as learning rate, dropout probability, and batch size, based on the validation results.

[0039] Model Evaluation and Optimization: 1. Evaluate model performance. After the model is trained, use the test set to perform a comprehensive performance evaluation of the model and calculate metrics such as accuracy, recall, F1 score, precision, and area under the ROC curve (AUC-ROC).

[0040] Accuracy reflects the proportion of faulty samples correctly classified by the model out of the total samples. The formula is: Accuracy = (Number of correctly predicted samples / Total number of samples) × 100%; Recall represents the proportion of positive samples (faulty samples) that the model correctly identifies out of all actual positive samples. The formula is: Recall = (Number of correctly predicted positive samples / Total number of actual positive samples) × 100%; The F1 score is the harmonic mean of precision and recall, and is calculated as follows: F1 score = 2 × (precision × recall) / (precision + recall).

[0041] These metrics provide a comprehensive understanding of the model's performance in fault diagnosis tasks and allow for an assessment of the model's diagnostic capabilities and reliability for different types of faults.

[0042] 2. Model optimization: Based on the evaluation results, the model is further optimized.

[0043] II. Model Deployment and Application: 1. Model Deployment The trained fault diagnosis model is deployed into a real-world pipeline coal transportation monitoring system and integrated with the real-time data acquisition and transmission module. Docker containerization technology is used to encapsulate the model and its dependencies, ensuring stable operation and rapid deployment across different hardware and operating systems. The model is deployed as a backend service, communicating with the frontend monitoring system via RESTful APIs or RPC interfaces to achieve real-time fault diagnosis of the pipeline coal transportation process.

[0044] 2. Online learning and updates As the system continues to operate and new data accumulates, the model is regularly updated and retrained. Employing an online learning algorithm (mini-batch stochastic gradient descent) allows the model to update its parameters in real-time or periodically using new data without affecting normal operation, thus adapting to the dynamic changes and evolution of failure modes in the pipeline coal transportation system.

[0045] In this application, during the fault alarm step, multiple alarm thresholds are set according to the fault type. When the predicted probability of a blockage fault exceeds 80%, a level one alarm is triggered, and when the probability is between 50% and 80%, a level two alarm is triggered. The alarm methods include audible and visual alarms, SMS alarms, email alarms, and instant messaging software alarms.

[0046] This application also includes an online model update step: using the mini-batch stochastic gradient descent method, the fault diagnosis model is periodically retrained and its parameters are updated using new data accumulated during system operation to adapt to dynamic changes in the system.

[0047] Fault location and alarm 1. Fault location Sensor-based fault location methods utilize data from multiple sensors installed on the pipeline, employing specific algorithms and models to determine the exact location of the fault. For pressure sensor data, the pressure wave propagation time difference method is used for fault location. When a blockage or leak occurs in the pipeline, pressure waves are generated at the fault point, propagating along the pipeline towards both ends. By measuring the time difference between the pressure waves received by adjacent pressure sensors and combining this with the propagation speed of the pressure waves in the coal slurry, the distance from the fault point to each sensor can be calculated, thus determining the precise location of the fault. The propagation speed can be predetermined through experiments or empirical formulas, while the time difference is accurately measured using a high-precision data acquisition system.

[0048] 2. Alarm Triggering and Notification Alarm threshold setting: Set reasonable alarm thresholds based on the severity and urgency of the fault. Different alarm thresholds should be set for different types of faults. For blockage faults, a Level 1 alarm is triggered when the model predicts a blockage probability exceeding 80% and the blockage location is determined; a Level 2 alarm is triggered when the blockage probability is between 50% and 80%, alerting operators to monitor the pipeline's operating status. Alarm threshold settings can be optimized through continuous practice and adjustments, taking into account actual production experience and fault handling requirements.

[0049] Alarm methods and notification mechanisms: When the probability of a fault occurring exceeds the set alarm threshold, the corresponding alarm method is triggered. Alarm methods may include audible and visual alarms, SMS alarms, email alarms, instant messaging software alarms, etc. The appropriate alarm method and notification recipients are selected based on the urgency of the fault and the division of responsibilities among relevant personnel.

[0050] 3. Alarm Information Recording and Analysis Alarm information storage: Detailed information for each alarm event is recorded and stored, including fault type, occurrence time, location, alarm level, personnel handling the incident, handling time, and handling result. An alarm information database is established to facilitate the querying, statistics, and analysis of historical alarm data. Through long-term accumulation of alarm information, valuable data support can be provided for fault analysis and prevention in pipeline coal conveying systems.

[0051] Alarm Information Statistics and Analysis: Alarm information is regularly collected and analyzed to generate various statistical reports and analytical charts. In-depth analysis of alarm information can identify potential problems and weaknesses in the pipeline coal conveying system, providing a scientific basis for optimizing system operation, strengthening equipment maintenance, and developing preventative maintenance plans.

[0052] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring and fault diagnosis system for coal pipeline transportation, characterized in that, include: The data acquisition module deploys a variety of high-precision sensors at key locations in the pipeline coal conveying system to monitor key parameters such as pipeline pressure, flow rate, coal composition, and pipeline wall thickness in real time. These key locations include the pipeline's starting point, ending point, branch points, valve locations, and areas prone to blockage. The data transmission module, connected to the data acquisition module, is used to organize and convert the data acquired by the sensor. The big data analysis and machine learning model training module is connected to the data transmission module and is used to receive the converted data, use machine learning algorithms to learn and train on historical fault data and normal operation data, and build a fault diagnosis model. The fault diagnosis and analysis module is connected to the big data analysis and machine learning model training module. It is used to apply the trained fault diagnosis model to actual monitoring, perform fault diagnosis and analysis on real-time data, and identify potential fault types and locations. The fault location and alarm module is connected to the fault diagnosis and analysis module and is used to determine the specific location of the fault based on the diagnosis results and trigger the alarm mechanism.

2. The intelligent monitoring and fault diagnosis system for pipeline coal transportation according to claim 1, characterized in that: The various high-precision sensors include pressure sensors, flow sensors, coal composition sensors, and pipe wall thickness detection sensors.

3. The intelligent monitoring and fault diagnosis system for pipeline coal transportation according to claim 1, characterized in that: The big data analysis and machine learning model training module also includes a data preprocessing unit, which is used to clean historical data, remove noise points, outliers and missing values, and perform standardization processing using the Z-Score standardization method.

4. The intelligent monitoring and fault diagnosis system for pipeline coal transportation according to claim 1, characterized in that: The big data analysis and machine learning model training module also includes a feature extraction and selection unit, which is used to extract time-domain features, frequency-domain features, pressure gradient, and flow fluctuation cycle features from the preprocessed data, and to select fault-related features using correlation analysis and principal component analysis (PCA).

5. The intelligent monitoring and fault diagnosis system for pipeline coal transportation according to claim 1, characterized in that: In the big data analysis and machine learning model training module, a fault diagnosis model is constructed using a deep neural network (DNN) and implemented through the TensorFlow or PyTorch framework. The model initialization adopts the He initialization method and includes a dropout layer.

6. The intelligent monitoring and fault diagnosis system for pipeline coal transportation according to claim 1, characterized in that: The fault location and alarm module uses the pressure wave propagation time difference method for fault location. It calculates the location of the fault point by measuring the time difference between the pressure waves received by adjacent pressure sensors and the propagation speed of the pressure waves in the coal slurry.

7. A method for intelligent monitoring and fault diagnosis of coal pipeline transportation, applied to the system described in any one of claims 1-6, characterized in that, Includes the following steps: Data acquisition: Multiple high-precision sensors are deployed at the pipeline's starting point, ending point, branch points, valve locations, and easily clogged areas to monitor key parameters such as pipeline pressure, flow rate, coal composition, and pipeline wall thickness in real time; Data transmission: The data collected by the sensors is processed and converted in format by the data acquisition and transmission module, and then transmitted to the big data analysis and machine learning model training module. Model building: In the big data analysis and machine learning model training module, machine learning algorithms are used to learn and train on historical fault data and normal operation data to build a fault diagnosis model; Diagnostic analysis: The trained fault diagnosis model is applied to actual monitoring to perform fault diagnosis analysis on real-time data and identify potential fault types and locations; Fault Alarm: Based on the fault diagnosis results, determine the specific location of the fault and trigger the alarm mechanism.

8. The intelligent monitoring and fault diagnosis method for pipeline coal transportation according to claim 7, characterized in that, In the model building step, the Adam optimization algorithm is used to train the deep neural network model. The model performance is evaluated by dividing the training set, validation set and test set, using the cross-entropy loss function, and the model is evaluated by the accuracy, recall, F1 score and AUC-ROC index.

9. The intelligent monitoring and fault diagnosis method for coal pipeline transportation according to claim 7, characterized in that, In the fault alarm step, multi-level alarm thresholds are set according to the fault type. When the predicted probability of a blockage fault exceeds 80%, a level one alarm is triggered. When the probability is between 50% and 80%, a level two alarm is triggered. Alarm methods include audible and visual alarms, SMS alarms, email alarms, and instant messaging software alarms.

10. The intelligent monitoring and fault diagnosis method for coal pipeline transportation according to claim 7, characterized in that, It also includes an online model update step: using the mini-batch stochastic gradient descent method, the fault diagnosis model is periodically retrained and its parameters are updated using new data accumulated during system operation to adapt to dynamic changes in the system.

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