Fault prediction system and electronic equipment

Abstract

The present application discloses a fault prediction system and electronic equipment, which relates to the field of data processing technology, including: a data acquisition module, a data fusion module and a fault prediction module; the data fusion module is connected with the data acquisition module and the fault prediction module; the data acquisition module is used to collect multi-dimensional data of boiler heating surface tubes, and the multi-dimensional data includes temperature data, pressure data, flow data, vibration data, audio sound wave data and wall thickness data; the data fusion module is used to fuse multi-modal data, and analyze the health status of boiler heating surface tubes through big data analysis and deep learning algorithms, and determine the risk information of boiler heating surface tubes based on the analysis results; the fault prediction module is used to predict the fault of boiler heating surface tubes based on risk information. Compared with the current existing technology, the present application can identify the potential fault or wear risk of equipment in advance and issue early warnings in time.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a fault prediction system and electronic equipment. Background Art

[0002] The boiler of a thermal power plant is one of the most core equipment in the thermal power generation process. Its main function is to convert heat energy into steam by burning fuels such as coal and natural gas to drive the steam turbine to generate electricity. In a thermal power plant, the heating surface tube of the boiler is responsible for carrying the heat exchange in the steam cycle process, withstanding the challenges of high temperature, high pressure and corrosive media, and is a key component in the operation of the boiler.

[0003] However, due to long-term exposure to high temperature, high pressure, high-speed flowing media and strong heat load changes, the boiler heating surface tubes are prone to wear, corrosion, cracks and explosions, which in turn affects the safe operation of the boiler and also leads to low operating efficiency, economic benefits and environmental protection compliance levels of the power plant. Summary of the invention

[0004] In view of this, the present application provides a fault prediction system and electronic equipment, the main purpose of which is to improve the current existing technology, which is prone to wear, corrosion, cracks, and explosion of boiler heating surface tubes due to long-term exposure to high temperature, high pressure, high-speed flowing media and strong heat load changes, thereby affecting the safe operation of the boiler and also resulting in low operating efficiency, economic benefits and environmental protection compliance levels of the power plant.

[0005] In a first aspect, the present application provides a fault prediction system, including: a data acquisition module, a data fusion module and a fault prediction module;

[0006] The data fusion module is connected with the data acquisition module and the fault prediction module;

[0007] The data acquisition and transmission module is used to collect multi-dimensional data of the boiler heating surface tube, and the multi-dimensional data includes temperature data, pressure data, flow data, vibration data, audio sound wave data and wall thickness data;

[0008] The data fusion module is used to fuse the multimodal data, analyze the health status of the boiler heating surface tubes through big data analysis and deep learning algorithms, and determine the risk information of the boiler heating surface tubes based on the analysis results;

[0009] The fault prediction module is used to predict faults of the boiler heating surface tubes based on the risk information.

[0010] Optionally, it also includes: a simulation module, a decision protection module, an intelligent control module and a record management module;

[0011] The simulation module is connected to the fault prediction module and the decision protection module, and the intelligent control module is connected to the decision protection module and the record management module;

[0012] The simulation module is used to construct a digital twin model of the boiler heating surface tube based on the multi-dimensional data, and synchronize the status of the physical equipment and the virtual environment in real time to perform virtual simulation and fault simulation;

[0013] The decision-making protection module is used to generate a personalized fault response plan according to the fault prediction result and the multi-dimensional data, and take protective measures by automatically adjusting the operating conditions of the boiler heating surface tubes;

[0014] The intelligent control module is used to adjust the operating parameters of the boiler heating surface tube according to the fault prediction result and the multi-dimensional data;

[0015] The record management module is used to record the maintenance, inspection and fault history of the boiler heating surface tubes in the blockchain.

[0016] Optionally, the data acquisition module includes: a temperature sensor unit, a pressure sensor unit, a flow sensor unit, a vibration sensor unit, an audio sensor unit and a thickness gauge unit;

[0017] The temperature sensor unit includes a high-precision temperature measurement sensor, which is used to be installed at multiple positions of the boiler heating surface tube to measure the temperature change of the boiler heating surface tube in real time;

[0018] The pressure sensor unit includes a plurality of high-pressure resistant pressure sensors for measuring the internal pressure of the boiler heating surface tube;

[0019] The flow sensor unit includes an ultrasonic flowmeter and an electromagnetic flowmeter, which are used to collect the flow of the medium in the boiler heating surface tube;

[0020] The vibration sensor unit includes an accelerometer mounted on the heated surface tube to monitor the vibration frequency and amplitude;

[0021] The audio sensor unit includes a high-sensitivity microphone and a shock wave sound detection sensor, which is used to collect the sound of the boiler in operation and the shock wave sound during high-pressure explosion;

[0022] The thickness gauge unit comprises an ultrasonic thickness gauge, which is used to detect the wall thickness of the boiler heating surface tube.

[0023] Optionally, the data fusion module includes: a data preprocessing unit, a multimodal data fusion unit and an analysis unit;

[0024] The data preprocessing unit includes a data cleaning and normalization processing layer for performing noise filtering, missing value filling and normalization processing on the data from multiple sensors;

[0025] The multimodal data fusion unit includes a data synchronization processor and a feature extraction layer, which is used to unify and fuse the processed multiple data types, and generate a feature set for analysis through feature extraction and time series alignment;

[0026] The analysis unit includes a convolutional neural network structure, a spectrum analysis layer and a feature classifier, which are used to analyze the wall thickness and audio data, and perform feature classification to identify the state of the boiler heating surface tube.

[0027] Optionally, the fault prediction module includes: a fault prediction unit, a fault classification unit and a warning generation unit;

[0028] The fault prediction unit includes a long short-term memory network and a recursive neural network layer, which is used to predict the future health status of the boiler heating surface tubes based on the collected time series data and identify potential faults;

[0029] The fault classification unit includes a classifier based on a support vector machine, which is used to classify the fault type and support the identification and labeling of multiple categories of fault modes;

[0030] The warning generation unit includes a multi-level threshold management layer and an alarm signal output device, which is used to generate warning signals of different levels, including slight wear, severe wear and emergency failure, and supports sound and light alarms and remote notifications.

[0031] Optionally, the simulation module includes: a digital twin modeling unit, a virtual simulation unit, a synchronization control unit and a fault simulation unit;

[0032] The digital twin modeling unit includes a modeler based on computer-aided design (CAD) data and real-time operation data, which is used to build a virtual model of the boiler heating surface tubes and simulate the physical characteristics and operating conditions of the heating surface tubes;

[0033] The virtual simulation unit includes a finite element simulation layer and a fluid dynamics simulation layer, which are used to simulate the operating state of the boiler under different working conditions and predict the stress distribution and wear position of the heating surface tube;

[0034] The synchronization control unit includes a real-time data synchronization device and a virtual model update module, which are used to synchronize the real-time data of the physical device with the digital twin model;

[0035] The fault simulation unit includes a fault scenario generator, which is used to simulate fault situations under different conditions in the digital twin model.

[0036] Optionally, the decision protection module includes: an expert system unit, a decision tree generation unit and an active protection unit;

[0037] The expert system unit includes a knowledge base and an inference engine for storing empirical rules and expert knowledge of boiler operation;

[0038] The decision tree generation unit includes a decision tree construction layer based on data mining, which is used to dynamically generate a decision tree structure;

[0039] The active protection unit includes a parameter adjuster and a control strategy management layer, which are used to automatically adjust the operating conditions of the boiler according to the decision results.

[0040] Optionally, the intelligent control module includes: an adaptive control unit, a reinforcement learning unit and a self-healing feedback unit;

[0041] The adaptive control unit includes a control layer based on a proportional-integral-derivative PID control algorithm, which is used to adjust the boiler operating parameters in real time;

[0042] The reinforcement learning unit includes a Q-learning algorithm layer for optimizing the control strategy during actual operation;

[0043] The self-healing feedback unit includes a fault recovery management layer for automatically adjusting operating parameters according to real-time monitoring data.

[0044] Optionally, the record management module includes: a blockchain architecture unit, a data encryption unit, a record storage unit and a traceability audit unit;

[0045] The blockchain architecture unit includes a distributed ledger based on the Hyperledger architecture for storing data related to boiler maintenance and inspection;

[0046] The data encryption unit includes an encryption layer based on SHA-256, which is used to encrypt the maintenance records;

[0047] The record storage unit includes an on-chain storage layer and a data index manager for storing and retrieving maintenance and repair records of equipment in the blockchain;

[0048] The traceability audit unit includes a data traceability tool and an audit record analysis layer, which is used to trace and audit the historical maintenance data.

[0049] In a second aspect, the present application provides an electronic device, comprising the fault prediction system described in the first aspect.

[0050] By means of the above technical scheme, the present application provides a fault prediction system and electronic equipment. Compared with the current existing technology, the present application accurately monitors the various working conditions of the boiler by real-time collection of multi-dimensional operating data (such as temperature, pressure, vibration, flow, audio sound waves and wall thickness, etc.) of the boiler heating surface tube. With the support of the fault prediction and early warning module, the potential failure or wear risk of the equipment is identified in advance, and an early warning is issued in time. Through this intelligent troubleshooting and early warning mechanism, the risk of sudden failures such as bursting and excessive wear of the boiler heating surface tube is significantly reduced, and the safety of the boiler is improved; by long-term monitoring of the health status of the boiler heating surface tube, the system can analyze the wear, corrosion, cracks and other conditions of the pipeline in real time, identify possible fault points in the early stage, and adjust the boiler operating conditions or take repair measures in time according to the prediction results. This early detection and intervention mechanism can effectively delay the aging process of boiler equipment, reduce the downtime caused by equipment failure, and thus extend the service life of the boiler heating surface tube. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0053] Figure 1 A schematic diagram of the structure of a fault prediction system provided by an embodiment of the present application is shown;

[0054] Figure 2 A schematic diagram showing an example provided by an embodiment of the present application;

[0055] Figure 3 A schematic diagram showing an example provided by an embodiment of the present application;

[0056] Figure 4 A schematic diagram showing an example provided by an embodiment of the present application;

[0057] Figure 5 A schematic diagram showing an example provided by an embodiment of the present application;

[0058] Figure 6 A schematic diagram showing an example provided by an embodiment of the present application;

[0059] Figure 7 A schematic diagram showing an example provided by an embodiment of the present application;

[0060] Figure 8 A schematic diagram showing an example provided by an embodiment of the present application;

[0061] Fig. 9 A schematic diagram showing an example provided by an embodiment of the present application;

[0062] Figure 1 middle:

[0063] 1-Data acquisition module; 2-Data fusion module; 3-Fault prediction module; 4-Simulation module; 5-Decision-making protection module; 6-Intelligent control module; 7-Record management module. DETAILED DESCRIPTION

[0064] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0065] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0066] In this application, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0067] The embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. It should be noted that the embodiments and features in the embodiments of the present disclosure can be combined with each other without conflict.

[0068] Combine the following Figure 1 A fault prediction system according to some embodiments of the present application is described.

[0069] The present application provides a fault prediction system, such as Figure 1 As shown, it includes: a data acquisition module 1, a data fusion module 2 and a fault prediction module 3; the data fusion module 2 is connected with the data acquisition module 1 and the fault prediction module 3; the data acquisition module 1 is used to collect multi-dimensional data of the boiler heating surface tube, and the multi-dimensional data includes temperature data, pressure data, flow data, vibration data, audio sound wave data and wall thickness data; the data fusion module 2 is used to fuse the multi-modal data, and analyze the health status of the boiler heating surface tube through big data analysis and deep learning algorithm, and determine the risk information of the boiler heating surface tube based on the analysis results; the fault prediction module 3 is used to predict the fault of the boiler heating surface tube based on the risk information.

[0070] In the embodiment of the present application, the data acquisition module 1 can be used to collect multi-dimensional data of the boiler heating surface tube, including temperature, pressure, flow, vibration, audio sound wave and wall thickness data. By collecting data from multiple sensors, the health status of the boiler heating surface tube can be fully monitored, and the data can be transmitted to the central processing system wirelessly or wiredly.

[0071] In some examples, the data fusion module 2 can fuse the multimodal data collected by the data acquisition module 1, and analyze the health status of the boiler heating surface tubes through big data analysis and deep learning algorithms. This module integrates multiple data sources to establish a multi-dimensional health assessment model for the boiler to identify potential failure risks, such as pipe wear, cracks, overheating, etc.

[0072] For this embodiment, the fault prediction module 3 can perform fault prediction based on the analysis results of the data fusion module 2 and in combination with historical data. This module uses deep learning models such as long short-term memory networks (LSTM) to perform time series analysis on the state data of the boiler heating surface tubes, identify possible fault trends in advance, and generate early warning signals.

[0073] Optionally, the fault prediction system of the embodiment of the present application also includes: a simulation module 4, a decision protection module 5, an intelligent control module 6 and a record management module 7; the simulation module 4 is connected to the fault prediction module 3 and the decision protection module 5, and the intelligent control module 6 is connected to the decision protection module 5 and the record management module 7; the simulation module 4 is used to construct a digital twin model of the boiler heating surface pipe based on multi-dimensional data, and synchronize the status of the physical equipment and the virtual environment in real time to perform virtual simulation and fault simulation; the decision protection module 5 is used to generate personalized fault response plans based on the fault prediction results and multi-dimensional data, and take protective measures by automatically adjusting the operating conditions of the boiler heating surface pipes; the intelligent control module 6 is used to adjust the operating parameters of the boiler heating surface pipes according to the fault prediction results and multi-dimensional data; the record management module 7 is used to record the maintenance, inspection and fault history of the boiler heating surface pipes in the blockchain.

[0074] In some examples, the simulation module 4 can build a digital twin model of the boiler heating surface tube, and synchronize the state of the physical equipment with the state in the virtual environment in real time, and perform operating condition analysis and fault simulation through virtual simulation. This module can simulate the state of the boiler heating surface tube under different operating conditions, predict possible fault points, and provide intuitive visualization of the equipment status.

[0075] For example, the decision-making protection module 5 can generate personalized fault response plans based on the prediction results of the intelligent fault prediction and early warning module and real-time operation data, including suggestions for adjusting operating parameters, optimization of operating conditions, etc. This module automatically adjusts boiler operating parameters such as temperature, pressure, flow, etc., and takes protective measures in advance to ensure the safety and stability of equipment operation.

[0076] In the embodiment of the present application, the intelligent control module 6 can automatically adjust the operating parameters of the boiler to restore the normal state of the equipment when a potential fault or abnormal operation is detected. The module continuously optimizes the control strategy through a reinforcement learning algorithm and automatically adjusts the parameters to achieve self-repair of the boiler and reduce downtime caused by faults.

[0077] In this embodiment, the record management module 7 can store the maintenance, inspection and fault history of the boiler heating surface tube in the blockchain. The blockchain technology is used to realize transparent management of data and ensure the security, non-tamperability and traceability of equipment maintenance records.

[0078] Optional, such as Figure 2As shown, the data acquisition module 1 includes: a temperature sensor unit 11, a pressure sensor unit 12, a flow sensor unit 13, a vibration sensor unit 14, an audio sensor unit 15 and a thickness gauge unit 16; the temperature sensor unit 11 includes a high-precision temperature measurement sensor, which is installed at multiple positions of the boiler heating surface tube to measure the temperature change of the boiler heating surface tube in real time; the pressure sensor unit 12 includes multiple high-pressure resistant pressure sensors, which are used to measure the internal pressure of the boiler heating surface tube; the flow sensor unit 13 includes an ultrasonic flowmeter and an electromagnetic flowmeter, which are used to collect the flow of the medium in the boiler heating surface tube; the vibration sensor unit 14 includes an accelerometer installed on the heating surface tube, which is used to monitor the vibration frequency and amplitude; the audio sensor unit 15 includes a high-sensitivity microphone and a shock wave sound detection sensor, which are used to collect the sound during the operation of the boiler and the shock wave sound during high-pressure explosion; the thickness gauge unit 16 includes an ultrasonic thickness gauge, which is used to detect the wall thickness of the boiler heating surface tube.

[0079] In the embodiment of the present application, the temperature sensor unit 11 includes high-precision temperature measurement sensors, which are installed at multiple key positions of the boiler heating surface pipes and can monitor the temperature changes on the pipe surface in real time. The temperature data is used to identify local overheating or abnormal temperature of the pipe to ensure that the boiler operates within the set safe temperature range.

[0080] As an optional method, the pressure sensor unit 12 includes a plurality of high-pressure resistant pressure sensors installed in the heating surface pipe for real-time measurement of the pressure change of the internal medium. The pressure data is used to monitor the operating pressure of the heating surface pipe to prevent safety problems caused by overpressure.

[0081] In some examples, the flow sensor unit 13 includes an ultrasonic flowmeter and an electromagnetic flowmeter, which are used to collect the flow of the medium in the boiler heating surface tube in real time. The flow data is used to determine the flow of the medium inside the pipeline, especially under high temperature and high pressure conditions, to ensure flow balance and avoid local overheating or pipeline wear caused by uneven flow.

[0082] In the embodiment of the present application, the vibration sensor unit 14 includes an accelerometer installed on the heated surface pipe to monitor the vibration frequency and amplitude. The vibration data can be used to identify possible mechanical failures or abnormal conditions of the pipeline and prevent fatigue damage caused by vibration. The unit has multi-directional vibration capture capabilities and is equipped with an anti-interference processing mechanism to improve the accuracy of detection.

[0083] As a preferred embodiment, the audio sensor unit 15 includes a high-sensitivity microphone and a shock wave sound detection sensor. The high-sensitivity microphone is used to collect various sounds generated during the operation of the boiler, including the sound during high-pressure operation; the shock wave sound detection sensor is used to detect the shock wave sound generated by explosion or crack expansion. The unit can perform spectrum analysis on the audio signal to identify abnormal sound signals, such as characteristic sounds generated by pipeline crack expansion, explosion and other faults.

[0084] For this embodiment, the thickness gauge unit 16 includes an ultrasonic thickness gauge, which is used to detect the wall thickness of the boiler heating surface tube to determine whether there is wear and thinning. The ultrasonic thickness gauge can perform non-destructive measurement of the heating surface tube without disassembling the equipment, help determine the wear state of the heating surface tube, and provide timely maintenance suggestions.

[0085] Optional, such as Figure 3 As shown, the data fusion module 2 includes: a data preprocessing unit 21, a multimodal data fusion unit 22 and an analysis unit 23; the data preprocessing unit 21 includes a data cleaning and normalization processing layer, which is used to perform noise filtering, missing value filling and standardization processing on data from multiple sensors; the multimodal data fusion unit 22 includes a data synchronization processor and a feature extraction layer, which is used to unify the processed multiple data types, and generate a feature set for analysis through feature extraction and time series alignment; the analysis unit 23 includes a convolutional neural network structure, a spectrum analysis layer and a feature classifier, which is used to analyze the wall thickness and audio data, and perform feature classification to identify the state of the boiler heating surface tube.

[0086] In the embodiment of the present application, the data preprocessing unit 21 includes a data cleaning and normalization processing layer. The data cleaning is used to filter out noise data and abnormal data, and the normalization processing layer is used to standardize the multi-dimensional sensor data to ensure that different types of data have the same dimension for subsequent analysis.

[0087] In some examples, the multimodal data fusion unit 22 includes a data synchronization processor and a feature extraction layer. The data synchronization processor is used to time-align the multimodal data of different sensors to ensure the consistency of various types of data; the feature extraction layer extracts key features from various types of data to form a unified feature set, so as to facilitate the analysis unit to make a comprehensive judgment on the health status of the pipeline.

[0088] As an optional method, the analysis unit 23 includes a convolutional neural network (CNN) structure, a spectrum analysis layer and a feature classifier. The convolutional neural network structure is used to process the thickness measurement data to identify visual features such as pipeline wear and cracks; the spectrum analysis layer is used to analyze the sound data collected by the audio sensor to identify abnormal sound waves caused by faults; the feature classifier is used to classify various types of data to identify the health status of the boiler heating surface tubes and provide input for the fault prediction and early warning module.

[0089] Optional, such as Figure 4 As shown, the fault prediction module 3 includes: a fault prediction unit 31, a fault classification unit 32 and an early warning generation unit 33; the fault prediction unit 31 includes a long short-term memory network and a recursive neural network layer, which is used to predict the future health status of the boiler heating surface tube based on the collected time series data and identify potential faults; the fault classification unit 32 includes a classifier based on a support vector machine, which is used to classify the fault type and support the identification and labeling of multiple categories of fault modes; the early warning generation unit 33 includes a multi-level threshold management layer and an alarm signal outputter, which is used to generate early warning signals of different levels, including slight wear, severe wear and emergency failure, and supports sound and light alarms and remote notifications.

[0090] In the embodiment of the present application, the fault prediction module 3 is responsible for predicting possible faults of the boiler pipeline based on historical data and real-time data, and generating an early warning. Among them, the fault prediction unit 31 performs time series analysis based on a machine learning algorithm (such as a long short-term memory network LSTM) combined with the historical operating data (temperature, pressure, vibration, etc.) of the boiler pipeline. Through training on historical data, the unit can predict the operating status of the equipment in the future and identify potential problems such as wear, cracks or leaks in advance.

[0091] In this embodiment, the fault classification unit 32 classifies the fault type based on the output of the prediction model using algorithms such as support vector machines (SVM). According to different modes of sensor data, it automatically determines the type of fault, such as wear, leakage, or pipe burst, and predicts its severity.

[0092] As a preferred method, the warning generation unit 33 can generate warnings of different levels according to the classification results. For example, slight wear can generate a green warning, more serious cracks can generate a yellow warning, and major faults such as burst pipes or leaks can generate a red warning. In addition, the system will automatically send alarm information to the operator according to the type and urgency of the fault.

[0093] Optional, such as Figure 5As shown, the simulation module 4 includes: a digital twin modeling unit 41, a virtual simulation unit 42, a synchronization control unit 43 and a fault simulation unit 44; the digital twin modeling unit 41 includes a modeler based on computer-aided design CAD data and real-time operation data, which is used to construct a virtual model of the boiler heating surface tube and simulate the physical characteristics and operating conditions of the heating surface tube; the virtual simulation unit 42 includes a finite element simulation layer and a fluid dynamics simulation layer, which are used to simulate the operating state of the boiler under different working conditions and predict the stress distribution and wear position of the heating surface tube; the synchronization control unit 43 includes a real-time data synchronization device and a virtual model update module, which are used to synchronize the real-time data of the physical equipment with the digital twin model; the fault simulation unit 44 includes a fault scenario generator, which is used to simulate fault conditions under different conditions in the digital twin model.

[0094] In the embodiment of the present application, the simulation module 4 simulates the operating state of the boiler and performs virtual simulation to predict possible risks by creating a digital twin model of the boiler heating surface tube. Among them, the digital twin modeling unit 41 builds a virtual model of the boiler by collecting the design data and real-time operation data of the boiler heating surface tube. The digital twin model will synchronize the status of the physical equipment (such as temperature, pressure, etc.) in real time and display the pipeline morphology, stress distribution, fluid flow and other information.

[0095] Exemplarily, the virtual simulation unit 42 uses finite element analysis (FEA) and fluid dynamics simulation (CFD) technology to simulate the behavior of boiler heating surface tubes under different working conditions, such as thermal expansion, stress distribution and wear under high temperature and high pressure. This simulation can help predict the weak points and possible fault locations of the pipeline.

[0096] In some examples, the synchronization control unit 43 ensures data synchronization between the physical device and the virtual model. Through real-time data update, the virtual model will reflect the current working status of the physical device and ensure the accuracy of the simulation data.

[0097] In the embodiment of the present application, the fault simulation unit 44 simulates different types of fault scenarios (such as pipe burst, leakage, etc.) in the virtual model. Through these simulations, the impact of the fault on the overall operation of the boiler can be evaluated in advance, and decision support can be provided for actual operation.

[0098] Optional, such as Figure 6As shown, the decision protection module 5 includes: an expert system unit 51, a decision tree generation unit 52 and an active protection unit 53; the expert system unit 51 includes a knowledge base and an inference engine for storing empirical rules and expert knowledge of boiler operation; the decision tree generation unit 52 includes a decision tree construction layer based on data mining, which is used to dynamically generate a decision tree structure; the active protection unit 53 includes a parameter adjuster and a control strategy management layer, which is used to automatically adjust the operating conditions of the boiler according to the decision results.

[0099] In the embodiment of the present application, the decision protection module 5 is used to actively adjust the operating parameters of the boiler according to the analysis results and predictions to prevent failures and improve the stability of the equipment. Among them, the expert system unit 51 uses the inference engine based on the expert knowledge base to analyze the operating status of the boiler and propose an optimization plan. For example, if the temperature of a certain part of the pipeline is too high, the expert system will provide suggestions for adjusting the flow or pressure to reduce the burden on the equipment.

[0100] As an optional method, the decision tree generation unit 52 automatically generates a decision tree model by analyzing the sensor data. According to the operation status of the boiler, historical fault data and prediction results, the decision tree model can generate the best operation adjustment plan to avoid the occurrence of faults.

[0101] In some examples, the active protection unit 53 actively adjusts the working conditions of the boiler based on the output of the decision tree. For example, by changing the temperature, pressure or flow parameters of the boiler, the burden on the heating surface tubes is reduced and the risk of wear is reduced. The unit is directly connected to the boiler control system and can implement protection measures in real time.

[0102] Optional, such as Figure 7 As shown, the intelligent control module 6 includes: an adaptive control unit 61, a reinforcement learning unit 62 and a self-healing feedback unit 63; the adaptive control unit 61 includes a control layer based on a proportional-integral-derivative (PID) control algorithm, which is used to adjust the boiler operating parameters in real time; the reinforcement learning unit 62 includes a Q learning algorithm layer, which is used to optimize the control strategy during actual operation; the self-healing feedback unit 63 includes a fault recovery management layer, which is used to automatically adjust the operating parameters according to real-time monitoring data.

[0103] In the embodiment of the present application, the intelligent control module 6 can automatically adjust the operating state of the boiler to ensure the self-healing ability and long-term stability of the boiler. Among them, the adaptive control unit 61 automatically adjusts the control parameters of the boiler (such as load, flow, pressure, etc.) according to the real-time data of the boiler operation through the PID control algorithm or model predictive control (MPC). The unit can automatically adjust when the equipment is abnormal, reducing the burden on the boiler equipment.

[0104] Exemplarily, the reinforcement learning unit 62 gradually optimizes the control strategy through the reinforcement learning algorithm. In the actual operation process, the reinforcement learning unit can continuously learn from the historical data of the boiler, optimize the control strategy, and make the system more self-healing.

[0105] In some examples, the self-healing feedback unit 63 can automatically adjust the boiler operating parameters when monitoring pipeline wear or failure trends to reduce equipment burden and avoid failures. The self-healing feedback system can monitor the boiler operating status in real time and make dynamic adjustments.

[0106] Optional, such as Figure 8 As shown, the record management module 7 includes: a blockchain architecture unit 71, a data encryption unit 72, a record storage unit 73 and a traceability audit unit 74; the blockchain architecture unit 71 includes a distributed ledger based on the hyperledger architecture, which is used to store data related to boiler maintenance and inspection; the data encryption unit 72 includes an encryption layer based on SHA-256, which is used to encrypt maintenance records; the record storage unit 73 includes an on-chain storage layer and a data index manager, which are used to store and retrieve equipment maintenance and repair records in the blockchain; the traceability audit unit 74 includes a data traceability tool and an audit record analysis layer, which are used to trace and audit historical maintenance data.

[0107] In the embodiment of the present application, the core function of the record management module 7 is to ensure the security, transparency, non-tamperability and traceability of the maintenance records, inspection records and fault data of the boiler heating surface tubes. Through the application of blockchain technology, a distributed, encrypted and decentralized record management system is established. Among them, the blockchain architecture unit 71 is responsible for designing and implementing a distributed ledger system based on blockchain technology. Its main technical features include: decentralized storage, chain structure, node consensus mechanism, smart contract and system fault tolerance.

[0108] Specific:

[0109] Decentralized storage: Through blockchain distributed ledger technology, maintenance records no longer rely on a single server or database, but are distributed in multiple nodes. This ensures data security and redundant storage.

[0110] Chain structure: Maintenance data is stored in the form of blocks, each block includes recorded data within a certain period of time. Each block will establish a connection with the previous block (through hash link) to ensure that the data cannot be tampered with.

[0111] Node consensus mechanism: The blockchain uses consensus algorithms (such as PoW, PoS or PBFT) to ensure the consistency and legitimacy of data records by each node in the network. Whenever a new record is generated, the validity of the record is confirmed through the consensus mechanism.

[0112] Smart Contracts: The unit can integrate smart contract functionality to automate operations such as verification, approval, and compliance checks of maintenance records. Smart contracts ensure automation of operations and avoid human error or intervention.

[0113] System fault tolerance: Since data is distributed across multiple nodes, even if some nodes fail, the data can still maintain integrity and consistency, enhancing the system's fault tolerance.

[0114] For example, the data encryption unit 72 can ensure that the maintenance records of the boiler will not be viewed or tampered by unauthorized personnel during storage and transmission. Its main technical features include: encryption algorithm, public and private key mechanism, digital signature and data access control. Specifically:

[0115] Encryption algorithm: Use strong encryption technology (such as SHA-256, AES-256, etc.) to encrypt maintenance records to ensure that the data stored on the blockchain is encrypted and can only be decrypted and viewed by authorized users.

[0116] Public-private key mechanism: Before each maintenance record is stored in the blockchain, it will be encrypted and stored using the recorder’s private key to ensure the authenticity and signature of the record. When users query or verify records, they use the public key for decryption verification.

[0117] Digital Signature: Maintenance records are authenticated by digital signatures to ensure that data has not been tampered with and to ensure the identity of the operator.

[0118] Data access control: The system uses a role-based access control (RBAC) mechanism to define different types of maintenance records that can be accessed by users with different permissions. Only authorized users or devices can access sensitive historical records or make modifications.

[0119] Exemplarily, the record storage unit 73 is responsible for storing all boiler maintenance, repair, inspection records and other data on the blockchain and providing efficient storage management. Its main technical features include: data storage, storage structure, data compression and indexing, and data traceability. Among them:

[0120] Data storage: Each maintenance record will be packaged into a "block" and added to the chain. Each block contains complete record information, such as maintenance time, operator, fault description, maintenance process, materials used, replaced parts and other detailed information.

[0121] Storage structure: Using a decentralized distributed storage method, each record will be stored by multiple nodes to ensure data redundancy and high availability. Records can also be identified by hash values ​​to ensure that they are unique and cannot be tampered with throughout the network.

[0122] Data compression and indexing: To improve storage efficiency and query speed, the system will appropriately compress the stored data and establish data indexes so that historical records can be efficiently retrieved and quickly located at a specific time point or a specific type of maintenance record.

[0123] Data traceability: Each record can be traced back to the entire process of its generation, approval, and execution. Through the timestamp and chain structure of blockchain technology, anyone can verify the source and authenticity of the record.

[0124] For example, the traceability audit unit 74 can ensure that all maintenance records of boiler heating surface tubes can be traced, audited and checked for compliance. Its main technical features include: audit function, transparency and compliance check, multi-party audit and audit report generation. Specifically:

[0125] Audit function: Through the immutability of blockchain, all records and operations can be permanently traced. Each maintenance record will automatically generate an audit log, including the time, operator, and content of the operation, to ensure the integrity and authenticity of the data.

[0126] Transparency and compliance checks: All maintenance records are open and transparent in the blockchain, and the system can automatically perform compliance checks through smart contracts to ensure that maintenance behaviors comply with operating standards, regulations and industry norms. If the records do not meet the standards, the system will automatically issue a warning or generate an audit report.

[0127] Multi-party audit: Through the distributed ledger of blockchain, the audit of maintenance records can be conducted simultaneously by multiple independent auditors, ensuring the independence and fairness of the audit process.

[0128] Audit report generation: When an audit is required, the system will automatically generate an audit report that lists detailed records of all maintenance operations, audit logs, and compliance check results. The audit report can be stored on the blockchain to ensure the authenticity and immutability of the report itself.

[0129] In some examples, the fault prediction system of the embodiment of the present application may include the following examples during the process of fault prediction: Fig. 9 As shown, but not limited to:

[0130] Step S1, collecting temperature, pressure, flow, vibration, audio and wall thickness data of the boiler heating surface tube, and transmitting them to the multimodal data fusion and analysis module.

[0131] Optionally, step S1 may specifically include: collecting temperature, pressure, flow, vibration, audio and wall thickness information through multiple sensors arranged on the heating surface tube of the boiler, the sensors including temperature sensors, pressure sensors, flow sensors, vibration sensors, high-sensitivity microphones, shock wave acoustic detection sensors and thickness gauges. The temperature sensor is used to collect real-time temperature data of the heating surface tube at multiple positions, the pressure sensor is used to measure the internal pressure of the heating surface tube, the flow sensor is used to monitor the flow of the medium in the heating surface tube, the vibration sensor is used to obtain the vibration frequency and amplitude of the pipeline, and the audio sensor is used to collect the operating sound of the boiler. The collected data is transmitted to the data processing platform through a data transmission unit, and the data transmission unit may include a wireless transmission device or an optical fiber transmission device to realize real-time transmission of data.

[0132] Step S2: perform multimodal fusion on the collected data, combine it with a deep learning model to perform data analysis, and identify the operating status of the heated surface tube.

[0133] Exemplarily, step S2 may specifically include: performing data preprocessing on the data collected in step S1, the data preprocessing includes noise filtering, missing value filling and normalization processing to improve the accuracy and consistency of the data. The preprocessed data is fused through multimodal data fusion, and the features of different data types are combined to generate a unified analysis feature set. Multimodal data includes multiple data types such as temperature, pressure, flow, vibration, audio and wall thickness. The fused data is analyzed using a deep learning analysis unit, which includes a convolutional neural network (CNN) and a spectrum analysis module, which is used to perform spectrum analysis on the audio data to identify the health status of the heated surface pipe.

[0134] Step S3: perform fault prediction based on the analysis results, identify potential faults, and generate early warning signals.

[0135] In some examples, step S3 may specifically include: using a fault prediction unit based on a long short-term memory network (LSTM) to perform time series prediction on the real-time data and historical data of the heated surface pipe to identify possible fault trends. The identified faults are classified by a fault classification unit, and the fault classification unit includes a classifier based on a support vector machine (SVM) for classifying and labeling possible fault types. Different levels of warning signals are generated by an early warning generation unit, and the early warning generation unit includes a multi-level threshold management module and an alarm signal outputter, which is used to generate slight wear, severe wear and emergency fault warning signals, and notify operators through sound and light alarms, text messages or remote notifications.

[0136] Step S4: construct a digital twin model to simulate and model the operating conditions of the boiler heating surface tubes.

[0137] As an optional method, step S4 may specifically include: based on the real-time data collected in step S1 and the physical parameters of the heating surface tube, a digital twin model of the heating surface tube is created using a digital twin modeling unit, and the digital twin model is based on the geometric structure and physical properties of the heating surface tube. The heating surface tube under different operating conditions is simulated using a virtual simulation unit, and the virtual simulation unit includes a finite element analysis module and a fluid dynamics simulation module, which are used to predict the stress distribution, temperature field and wear position of the heating surface tube. The real-time data of the physical equipment is synchronized with the digital twin model through a synchronous control unit to ensure the accuracy and real-time performance of the virtual model; the fault simulation unit is used to simulate the faults that may occur under different conditions, so as to facilitate the assessment of the potential risks of the equipment.

[0138] Step S5: Generate an optimization plan based on the fault prediction results and adjust the operating parameters of the boiler.

[0139] Optionally, step S5 may specifically include: using an expert system unit to analyze boiler operation data based on a knowledge base, the knowledge base storing the equipment's operating experience rules and expert knowledge. Combining current operating data and historical data, dynamically generating a decision tree through a decision tree generation unit to determine the best operation plan for the boiler. Using an active protection unit to automatically adjust the boiler's operating parameters based on the decision result, the active protection unit includes a parameter adjustment module and a control strategy management module, which are used to automatically adjust parameters such as temperature, pressure, and flow to reduce the wear and failure risks of the heating surface tubes.

[0140] Step S6: After identifying the risk, adaptively adjust the boiler operating parameters to reduce the risk of the heating surface tubes.

[0141] Exemplarily, step S6 may specifically include: using an adaptive control unit to adjust the boiler operating parameters in real time, the adaptive control unit includes a control module based on a PID control algorithm, which is used to respond to system disturbances and maintain the stability of boiler operation. The control strategy is continuously optimized by a reinforcement learning unit, and the reinforcement learning unit includes a Q learning algorithm module, which is used to optimize parameter settings during the operation of the boiler. The self-healing feedback unit is used to automatically adjust the operating conditions when a potential risk is detected in the heating surface tube, and the self-healing feedback unit includes a fault recovery management module, which is used to adjust the load, temperature and flow rate to reduce the wear of the heating surface tube and extend the service life of the equipment.

[0142] Step S7: Use blockchain to record maintenance, inspection and fault information.

[0143] For this embodiment, step S7 may specifically include: establishing a distributed ledger based on a blockchain architecture unit, the blockchain architecture unit including a Hyperledger architecture for storing maintenance and inspection records related to the heated surface pipes. Encrypting the records through a data encryption unit, the data encryption unit including an encryption module based on SHA-256 to ensure the security and tamper-proof nature of the data. Using a record storage unit to store all maintenance and inspection history records in the blockchain, and tracing and auditing the records through a traceability audit unit to ensure the authenticity and integrity of the historical data.

[0144] Compared with the current existing technology, this embodiment accurately monitors the various working conditions of the boiler by collecting multi-dimensional operating data (such as temperature, pressure, vibration, flow, audio sound waves and wall thickness, etc.) of the boiler heating surface tube in real time. With the support of the fault prediction and early warning module, the potential failure or wear risk of the equipment is identified in advance, and an early warning is issued in time. Through this intelligent troubleshooting and early warning mechanism, the risk of sudden failures such as bursting and excessive wear of the boiler heating surface tube is significantly reduced, and the safety of the boiler is improved; through long-term monitoring of the health status of the boiler heating surface tube, the system can analyze the wear, corrosion, cracks and other conditions of the pipeline in real time, identify possible fault points in early stage, and adjust the boiler operating conditions or take repair measures in time according to the prediction results. This early detection and intervention mechanism can effectively delay the aging process of boiler equipment, reduce the downtime caused by equipment failure, and thus extend the service life of the boiler heating surface tube; through the intelligent decision support and active protection module, it provides optimized operation suggestions and adjustment plans according to the boiler operating status, automatically adjusts the boiler operating conditions, and ensures that the boiler operates in the best working state. By optimizing the boiler load distribution and operating parameters, the system can improve the thermal efficiency of the boiler, reduce energy waste, and thus reduce fuel consumption, achieving the effect of energy saving and consumption reduction; using advanced artificial intelligence technology, combined with multimodal data analysis and deep learning algorithms, to achieve intelligent health management of boiler equipment. Through the self-healing intelligent control module, the system can automatically adjust the operating parameters and control strategies of the boiler according to real-time monitoring data and fault prediction results, reduce the burden on equipment, avoid equipment damage caused by improper operation, realize self-repair and adjustment of the system, and improve the automation level and stability of the system.

[0145] Based on the above Figure 1 The system shown in the embodiment of the present application also provides an electronic device, such as a smart phone, a tablet computer, a drone, an intelligent robot, a wearable smart terminal, etc., the device includes Figure 1 The system shown.

[0146] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0147] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0148] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features applied for herein.

Claims

1. A fault prediction system, characterized in that: include: Data acquisition module, data fusion module and fault prediction module; The data fusion module is connected with the data acquisition module and the fault prediction module; The data acquisition module is used to collect multi-dimensional data of the boiler heating surface tube, and the multi-dimensional data includes temperature data, pressure data, flow data, vibration data, audio sound wave data and wall thickness data; The data fusion module is used to fuse the multimodal data, analyze the health status of the boiler heating surface tubes through big data analysis and deep learning algorithms, and determine the risk information of the boiler heating surface tubes based on the analysis results; The fault prediction module is used to predict faults of the boiler heating surface tubes based on the risk information.

2. The system according to claim 1, characterized in that Also includes: Simulation module, decision-making protection module, intelligent control module and record management module; The simulation module is connected to the fault prediction module and the decision protection module, and the intelligent control module is connected to the decision protection module and the record management module; The simulation module is used to construct a digital twin model of the boiler heating surface tube based on the multi-dimensional data, and synchronize the status of the physical equipment and the virtual environment in real time to perform virtual simulation and fault simulation; The decision-making protection module is used to generate a personalized fault response plan according to the fault prediction result and the multi-dimensional data, and take protective measures by automatically adjusting the operating conditions of the boiler heating surface tubes; The intelligent control module is used to adjust the operating parameters of the boiler heating surface tube according to the fault prediction result and the multi-dimensional data; The record management module is used to record the maintenance, inspection and fault history of the boiler heating surface tubes in the blockchain.

3. The system according to claim 2, characterized in that The data acquisition module includes: a temperature sensor unit, a pressure sensor unit, a flow sensor unit, a vibration sensor unit, an audio sensor unit and a thickness gauge unit; The temperature sensor unit includes a high-precision temperature measurement sensor, which is used to be installed at multiple positions of the boiler heating surface tube to measure the temperature change of the boiler heating surface tube in real time; The pressure sensor unit includes a plurality of high-pressure resistant pressure sensors for measuring the internal pressure of the boiler heating surface tube; The flow sensor unit includes an ultrasonic flowmeter and an electromagnetic flowmeter, which are used to collect the flow of the medium in the boiler heating surface tube; The vibration sensor unit includes an accelerometer mounted on the heated surface tube to monitor the vibration frequency and amplitude; The audio sensor unit includes a high-sensitivity microphone and a shock wave sound detection sensor, which is used to collect the sound of the boiler in operation and the shock wave sound during high-pressure explosion; The thickness gauge unit comprises an ultrasonic thickness gauge, which is used to detect the wall thickness of the boiler heating surface tube.

4. The system according to claim 3, characterized in that The data fusion module includes: a data preprocessing unit, a multimodal data fusion unit and an analysis unit; The data preprocessing unit includes a data cleaning and normalization processing layer for performing noise filtering, missing value filling and normalization processing on the data from multiple sensors; The multimodal data fusion unit includes a data synchronization processor and a feature extraction layer, which is used to unify and fuse the processed multiple data types, and generate a feature set for analysis through feature extraction and time series alignment; The analysis unit includes a convolutional neural network structure, a spectrum analysis layer and a feature classifier, which are used to analyze the wall thickness and audio data, and perform feature classification to identify the state of the boiler heating surface tube.

5. The system according to claim 4, characterized in that The fault prediction module includes: a fault prediction unit, a fault classification unit and a warning generation unit; The fault prediction unit includes a long short-term memory network and a recursive neural network layer, which is used to predict the future health status of the boiler heating surface tubes based on the collected time series data and identify potential faults; The fault classification unit includes a classifier based on a support vector machine, which is used to classify fault types and support the identification and labeling of multiple categories of fault modes; The warning generation unit includes a multi-level threshold management layer and an alarm signal output device, which is used to generate warning signals of different levels, including slight wear, severe wear and emergency failure, and supports sound and light alarms and remote notifications.

6. The system according to claim 5, characterized in that The simulation module includes: a digital twin modeling unit, a virtual simulation unit, a synchronization control unit and a fault simulation unit; The digital twin modeling unit includes a modeler based on computer-aided design (CAD) data and real-time operation data, which is used to build a virtual model of the boiler heating surface tubes and simulate the physical characteristics and operating conditions of the heating surface tubes; The virtual simulation unit includes a finite element simulation layer and a fluid dynamics simulation layer, which are used to simulate the operating state of the boiler under different working conditions and predict the stress distribution and wear position of the heating surface tube; The synchronization control unit includes a real-time data synchronization device and a virtual model update module, which is used to synchronize the real-time data of the physical device with the digital twin model; The fault simulation unit includes a fault scenario generator, which is used to simulate fault situations under different conditions in the digital twin model.

7. The system according to claim 6, characterized in that The decision protection module includes: an expert system unit, a decision tree generation unit and an active protection unit; The expert system unit includes a knowledge base and an inference engine for storing empirical rules and expert knowledge of boiler operation; The decision tree generation unit includes a decision tree construction layer based on data mining, which is used to dynamically generate a decision tree structure; The active protection unit includes a parameter adjuster and a control strategy management layer, which are used to automatically adjust the operating conditions of the boiler according to the decision results.

8. The system according to claim 7, characterized in that The intelligent control module includes: an adaptive control unit, a reinforcement learning unit and a self-healing feedback unit; The adaptive control unit includes a control layer based on a proportional-integral-derivative PID control algorithm, which is used to adjust the boiler operating parameters in real time; The reinforcement learning unit includes a Q-learning algorithm layer for optimizing the control strategy during actual operation; The self-healing feedback unit includes a fault recovery management layer for automatically adjusting operating parameters according to real-time monitoring data.

9. The system according to claim 8, characterized in that The record management module includes: a blockchain architecture unit, a data encryption unit, a record storage unit and a traceability audit unit; The blockchain architecture unit includes a distributed ledger based on the Hyperledger architecture for storing data related to boiler maintenance and inspection; The data encryption unit includes an encryption layer based on SHA-256, which is used to encrypt the maintenance records; The record storage unit includes an on-chain storage layer and a data index manager for storing and retrieving maintenance and repair records of equipment in the blockchain; The traceability audit unit includes a data traceability tool and an audit record analysis layer, which are used to trace and audit the historical maintenance data.

10. An electronic device, comprising the system according to any one of claims 1 to 9.

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