Chemical equipment state monitoring and fault diagnosis system

Abstract

The invention relates to the related technical field of chemical equipment, in particular to a chemical equipment state monitoring and fault diagnosis system which comprises a self-adaptive monitoring device, a multi-source data fusion module, an intelligent fault diagnosis system and a linkage feedback mechanism. According to the chemical equipment state monitoring and fault diagnosis system, the adaptive monitoring device continuously monitors and collects data, the multi-source data fusion module integrates the data and generates comprehensive feature data, and the intelligent fault diagnosis system uses the data to carry out deep learning analysis so as to locate faults. And a linkage feedback mechanism adjusts a monitoring strategy according to an analysis result, so that accurate monitoring and quick response to the equipment are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of chemical equipment, in particular to a chemical equipment state monitoring and fault diagnosis system. BACKGROUND

[0002] The current state monitoring system of chemical equipment mainly relies on independent sensor networks for data collection, but lacks effective adaptive data collection strategies. This means that the monitoring device cannot dynamically adjust the data collection frequency according to the actual operating conditions of the equipment, resulting in redundant or missing data due to excessive or insufficient data, which greatly reduces the effectiveness of state monitoring. In addition, the existing offline diagnosis system and monitoring device lack effective information interaction, and the diagnosis results cannot be fed back to the monitoring link in a timely manner, resulting in insufficient closed-loop control capability of the entire system, further affecting the timeliness and accuracy of fault diagnosis.

[0003] Although the existing monitoring system can collect a large amount of real-time data, these data mainly rely on a single data source for analysis, which not only increases the false positive rate - more than 35% of the data cannot accurately reflect the actual operating conditions of the equipment, but also fails to fully exploit valuable information hidden behind the data. This not only affects the accuracy and reliability of fault diagnosis, but also limits the improvement of system fault warning and prevention capabilities. SUMMARY

[0004] The present application solves the problems in the related art and proposes a chemical equipment state monitoring and fault diagnosis system to solve the technical problem that the current offline diagnosis system and monitoring device lack interaction, the diagnosis results cannot be fed back to the monitoring link in a timely manner, and it is difficult to form a closed-loop control.

[0005] To solve the above technical problems, the present application is realized by the following technical scheme: a chemical equipment state monitoring and fault diagnosis system, comprising an adaptive monitoring device, a multi-source data fusion module, an intelligent fault diagnosis system and a linkage feedback mechanism; The adaptive monitoring device dynamically adjusts the data collection frequency according to the operating state of the chemical equipment; The multi-source data fusion module integrates the collected multi-source data to generate comprehensive feature data; The intelligent fault diagnosis system analyzes the feature data based on a deep learning algorithm to locate the fault type and position; The linkage feedback mechanism realizes deep interaction between the fault diagnosis system and the monitoring device, and adjusts the monitoring strategy according to the diagnosis result. In the present application, the adaptive monitoring device continuously monitors and collects data, the multi-source data fusion module integrates these data and generates comprehensive feature data, the intelligent fault diagnosis system uses these data for deep learning analysis to locate the fault, and the linkage feedback mechanism adjusts the monitoring strategy according to the analysis result, so as to realize accurate monitoring and rapid response of the equipment.

[0006] As a preferred solution, the adaptive monitoring device comprises a plurality of sensor nodes and a central controller, the sensor nodes are connected with the central controller through wireless communication technology, and the central controller analyzes the running parameters of the equipment in real time according to the preset adaptive strategy, and automatically adjusts the collection frequency of the sensor nodes.

[0007] By adopting the above technical solution, each sensor node is responsible for real-time collection of running parameters of the equipment, such as temperature, pressure or vibration, and transmits the collected data to the central controller through wireless communication technology. The central controller analyzes the received sensor node data in real time according to the preset adaptive strategy, and automatically adjusts the data collection frequency of each sensor node, so as to ensure that data can be obtained in time and accurately when the running parameters of the equipment are abnormal, thereby improving the sensitivity and efficiency of monitoring. This design can dynamically adjust the monitoring strategy according to different working conditions, save energy, improve the accuracy and real-time performance of data collection, reduce maintenance cost, and ensure the safe operation of the equipment.

[0008] As a preferred solution, the multi-source data fusion module uses a weighted average algorithm to fuse different types of data, assigns weights according to the influence of each data on the running state of the chemical equipment, and generates comprehensive feature data.

[0009] By adopting the technical scheme, the multi-source data fusion module fuses different types of data by weighted average. Each data source is assigned a corresponding weight according to its different influence on the running state of the chemical equipment. For example, the data of the temperature sensor may have a higher weight due to its important influence on the safety of the equipment, while the data of the vibration sensor may have a higher weight due to its important influence on the smooth running of the equipment. These weighted data are integrated together to generate comprehensive feature data that can more comprehensively describe the current running state of the chemical equipment. This comprehensive feature data can more accurately reflect the overall health status of the equipment running, discover potential problems in advance and take timely measures, thereby ensuring the safe operation and efficiency improvement of the equipment. The working principle is as follows: first, identify and collect multi-source data from various sensors; second, assign a corresponding weight based on the importance of each data to the running state of the equipment; third, integrate these weighted data by weighted average algorithm; and finally, generate comprehensive feature data that can comprehensively reflect the current running state of the equipment, providing a basis for subsequent equipment health management.

[0010] As a preferred solution, the intelligent fault diagnosis system is constructed based on a deep learning algorithm, adopts one or both of convolutional neural network or recurrent neural network architecture, and performs feature matching on the input data through a pre-set fault mode library to quickly locate the fault type and position.

[0011] By adopting the technical scheme, the convolutional neural network is good at processing image and structure data, such as spatial dependence in sensor data, which helps to identify spatial pattern faults and is suitable for diagnosing surface defects or internal structure problems of equipment; while the recurrent neural network is good at processing sequence data, such as time series dependence in time series data, which is suitable for identifying the trend of fault evolution over time and is used for diagnosing dynamic changes in equipment operation. The system can quickly locate and identify different types of faults and their specific positions by performing feature matching with the pre-set fault mode library. The working principle of the system is to first collect various sensor data of the equipment, process them through the convolutional neural network or the recurrent neural network, extract key fault features, and then compare them with the features in the fault mode library to quickly determine the fault type and the specific position where the fault occurs, thereby achieving efficient fault diagnosis and positioning.

[0012] As a preferred solution, the linkage feedback mechanism connects the fault diagnosis system and the monitoring device through a high-speed communication link. The diagnosis system feeds back the diagnosis result to the central controller of the monitoring device, and the central controller adjusts the monitoring strategy according to the diagnosis result to form a closed-loop feedback.

[0013] By adopting the above technical solution, the fault diagnosis system is responsible for real-time monitoring of the running state of the equipment and fault analysis, and the diagnosis result is fed back to the central controller of the monitoring device through the high-speed communication link. After receiving the diagnosis result, the central controller of the monitoring device adjusts the monitoring strategy according to the feedback information, so as to realize accurate monitoring and early warning. The function of the fault diagnosis system is to provide accurate fault diagnosis information, and the central controller of the monitoring device optimizes the monitoring strategy according to these information to ensure the safety and stability of the equipment operation. Through this closed-loop feedback mechanism, the whole system can quickly respond to potential faults of the equipment, timely adjust the monitoring parameters, and improve the reliability and operation efficiency of the system.

[0014] As a preferred solution, the sensor node periodically sends a data collection status report to the central controller, and the central controller dynamically adjusts the collection strategy according to the report to ensure that the monitoring device is always in the best operating state.

[0015] By adopting the above technical solution, the main function of the sensor node is to periodically collect data and send a status report to the central controller. The central controller dynamically adjusts the data collection strategy according to the received report information to ensure that the monitoring device is always in the best operating state. Specifically, the sensor node is responsible for real-time monitoring of environmental changes and data collection, and the central controller judges the current monitoring state according to these data and adjusts the collection frequency, collection range and other parameters to optimize the monitoring effect. This dynamic adjustment strategy can timely respond to environmental changes to ensure the accuracy and real-time of the monitoring data, so as to ensure that the monitoring device always operates efficiently.

[0016] As a preferred solution, the multi-source data fusion module pre-processes the data before data fusion to eliminate noise and outliers in the data.

[0017] By adopting the above technical solution, by pre-eliminating noise and outliers in the data, the quality and accuracy of data fusion are improved, the reliability and consistency of the final output data are enhanced, and the performance and stability of the whole system are effectively improved.

[0018] As a preferred solution, the intelligent fault diagnosis system has self-learning ability and can continuously optimize the diagnosis model according to new fault cases to improve the diagnosis accuracy.

[0019] By adopting the above technical solution, by continuously collecting equipment operation data, the self-learning module optimizes the diagnosis model using these data, the diagnosis module analyzes the equipment state in real time based on the optimized model, and the final feedback module feeds back the diagnosis result to the user in time to realize the fast and accurate identification and processing of equipment faults.

[0020] As a preferred solution, the linkage feedback mechanism can increase the monitoring frequency of potential fault locations according to the diagnostic results and adjust the collection parameters of sensor nodes to obtain more detailed fault feature data.

[0021] By adopting the above technical solution, potential fault locations are identified through diagnostic results, and the monitoring frequency of these locations is increased according to the diagnostic results, so that potential problems are discovered earlier. The sensor nodes adjust their collection parameters accordingly to obtain more detailed fault feature data, helping further analysis and diagnosis. Specifically, the diagnostic system evaluates the current situation, identifies devices or components that may have problems, and feeds this information back to the monitoring system. After receiving the feedback, the monitoring system increases the monitoring frequency of these locations to more accurately capture abnormal changes. At the same time, the sensor nodes adjust the collection parameters according to the new monitoring requirements, such as increasing the sampling frequency or changing the collection range, to obtain more accurate data. In this way, the entire system can more efficiently identify and diagnose faults, take preventive measures to avoid major accidents, extend the service life of equipment, and ensure stable operation of the system.

[0022] As a preferred solution, the system can reduce the false positive rate to less than 10%, significantly improving the accuracy of fault diagnosis, and improving the monitoring efficiency and adaptive ability of the system through adaptive monitoring devices and linkage feedback mechanisms.

[0023] By adopting the above technical solution, the monitoring device continuously monitors the operating state of the system, and the adaptive monitoring device dynamically adjusts according to the actual operating conditions to ensure the sensitivity and accuracy of the monitoring. The linkage feedback mechanism can quickly respond to the monitoring results and quickly eliminate faults, further improving the adaptive ability and monitoring efficiency of the system. The system combines these components to achieve an efficient fault diagnosis and monitoring mechanism, significantly improving the operating stability and reliability of the system.

[0024] Compared with the prior art, the present application has the following advantages: 1. Improve fault diagnosis accuracy: through multi-source data fusion and linkage feedback mechanism, effectively solve the false positive rate problem caused by single data source analysis, reduce the false positive rate to less than 10%, significantly improve the accuracy of fault diagnosis; 2. Improve monitoring efficiency: the adaptive monitoring device can dynamically adjust the data collection frequency according to the operating state of the chemical equipment, avoid data redundancy and omission, improve the monitoring efficiency, and reduce the data storage and transmission cost; 3. Realize closed-loop control: The linkage feedback mechanism makes the monitoring device and the fault diagnosis system form a closed-loop control system, and the diagnosis result can be fed back to the monitoring link in time. The monitoring device adjusts the monitoring strategy according to the diagnosis result, ensures the timeliness and accuracy of fault diagnosis, and improves the operation reliability of the chemical equipment.

[0025] 4. Enhance the adaptive ability of the system: The intelligent fault diagnosis system has self-learning ability, can continuously optimize the diagnosis model according to new fault cases, adapts to the fault diagnosis needs of the chemical equipment under different operating environments and working conditions, and enhances the adaptive ability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is the overall structure schematic diagram of a chemical equipment state monitoring and fault diagnosis system of the present application; Figure 2 is the connection schematic diagram of a sensor node and a central controller of the adaptive monitoring device of a chemical equipment state monitoring and fault diagnosis system of the present application; Figure 3 is the data processing flow chart of a multi-source data fusion module in a chemical equipment state monitoring and fault diagnosis system of the present application; Figure 4 is the deep learning algorithm architecture diagram of an intelligent fault diagnosis system in a chemical equipment state monitoring and fault diagnosis system of the present application; Figure 5 is the working flow chart of a linkage feedback mechanism in a chemical equipment state monitoring and fault diagnosis system of the present application, which shows the process of feeding back the diagnosis result to the monitoring device and adjusting the monitoring strategy. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The description of the at least one exemplary embodiment is actually only illustrative, but not as any limitation on the present application and its application or use. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0028] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form, unless the context clearly indicates otherwise, and it should also be understood that when the terms "comprise" and / or "include" are used in the specification, there is a feature, step, operation, device, component and / or combination thereof.

[0029] Unless otherwise specifically stated, the relative arrangement of the parts and steps, the numerical expressions and the numerical values ​​set forth in these embodiments do not limit the scope of the present invention. At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The techniques, methods and equipment known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods and equipment should be considered as part of the authorization specification. In all examples shown and discussed here, any specific values ​​should be interpreted as being merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values. It should be noted that similar numbers and letters represent similar items in the following figures, and therefore, once an item is defined in one figure, it does not need to be further discussed in subsequent figures.

[0030] In the description of the present invention, it should be understood that the directions or positional relationships indicated by directional words such as "front, back, up, down, left, right", "horizontal, vertical, perpendicular, horizontal" and "top, bottom" are usually based on the directions or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description. Unless otherwise specified, these directional words do not indicate or imply that the device or element referred to must have a specific direction or be constructed and operated in a specific direction. Therefore, they cannot be understood as limiting the scope of protection of the present invention; the directional words "inside and outside" refer to the inside and outside relative to the outline of each component itself.

[0031] For ease of description, spatially relative terms such as "above", "above", "on the upper surface of", "above", etc. may be used herein to describe the spatial positional relationship of a device or feature to other devices or features as shown in the figures. It should be understood that spatially relative terms are intended to include different orientations of the device in use or operation in addition to the orientation described in the figures. For example, if the device in the drawings is inverted, the device described as "above other devices or structures" or "above other devices or structures" will be positioned as "below other devices or structures" or "below other devices or structures". Thus, the exemplary term "above" can include both "above" and "below". The device can also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatially relative descriptions used here are interpreted accordingly.

[0032] In addition, it should be noted that the use of terms such as "first" and "second" to limit components is only for the convenience of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore cannot be understood as limiting the scope of protection of the present invention.

[0033] likeFigure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5 A chemical equipment state monitoring and fault diagnosis system, a chemical equipment state monitoring and fault diagnosis system, including adaptive monitoring device, multi-source data fusion module, intelligent fault diagnosis system and linkage feedback mechanism; The adaptive monitoring device dynamically adjusts the data collection frequency according to the running state of the chemical equipment; The multi-source data fusion module integrates the collected multi-source data to generate comprehensive feature data; The intelligent fault diagnosis system analyzes the feature data based on deep learning algorithm to locate the fault type and position; The linkage feedback mechanism realizes the deep interaction between the fault diagnosis system and the monitoring device, and adjusts the monitoring strategy according to the diagnosis result. In the present invention, the adaptive monitoring device continuously monitors and collects data, the multi-source data fusion module integrates these data and generates comprehensive feature data, the intelligent fault diagnosis system uses these data for deep learning analysis to locate faults, and the linkage feedback mechanism adjusts the monitoring strategy according to the analysis result, so as to realize accurate monitoring and rapid response of the equipment.

[0034] The adaptive monitoring device includes a plurality of sensor nodes and a central controller, the sensor nodes are connected with the central controller through wireless communication technology, the central controller analyzes the running parameters of the equipment in real time according to the preset adaptive strategy, and automatically adjusts the collection frequency of the sensor nodes. Each sensor node is responsible for real-time collection of the running parameters of the equipment, such as temperature, pressure or vibration, and transmits the collected data to the central controller through wireless communication technology. The central controller analyzes the received sensor node data in real time according to the preset adaptive strategy, and automatically adjusts the data collection frequency of each sensor node to ensure that data can be obtained in time and accurately when the equipment running parameters are abnormal, thereby improving the sensitivity and efficiency of monitoring. This design can dynamically adjust the monitoring strategy according to different working conditions, save energy, improve the accuracy and real-time performance of data collection, reduce maintenance cost, and ensure the safety of equipment operation.

[0035] The multi-source data fusion module uses a weighted average algorithm to fuse different types of data, assigns weights according to the influence of each data on the running state of the chemical equipment, and generates comprehensive feature data. The multi-source data fusion module fuses different types of data by weighted average, each data source is assigned a corresponding weight according to its different influence on the running state of the chemical equipment, for example, the data of the temperature sensor may have a higher weight due to its important influence on the safety of the equipment, while the data of the vibration sensor may obtain a higher weight due to its important influence on the smooth running of the equipment. These weighted data are integrated together to generate comprehensive feature data that can more comprehensively describe the current running state of the chemical equipment. This comprehensive feature data can more accurately reflect the overall health status of the equipment running, discover potential problems in advance and take timely measures, so as to ensure the safe operation and efficiency improvement of the equipment. Its working principle is: first, identify and collect multi-source data from various sensors; second, assign corresponding weights based on the importance of each data on the running state of the equipment; then, integrate these weighted data through a weighted average algorithm; finally, generate comprehensive feature data that can comprehensively reflect the current running state of the equipment, providing a basis for subsequent equipment health management.

[0036] The intelligent fault diagnosis system is based on deep learning algorithm, adopts one or both of convolutional neural network or recurrent neural network architecture, and matches the input data with the pre-set fault mode library to quickly locate the fault type and position. Convolutional neural network is good at processing image and structure data, such as spatial dependence in sensor data, which helps to identify spatial pattern faults, and is suitable for diagnosing surface defects or internal structure problems of equipment; while recurrent neural network is good at processing sequence data, such as time series dependence in time series data, which is suitable for identifying the trend of fault evolution over time, and is used for diagnosing dynamic changes in equipment operation. The system can quickly locate and identify different types of faults and their specific positions by matching features with the pre-set fault mode library. The working principle of the system is to first collect various sensor data of the equipment, process them through convolutional neural network or recurrent neural network, extract key fault features, and then compare them with the features in the fault mode library to quickly determine the fault type and specific location, thereby achieving efficient fault diagnosis and positioning.

[0037] Among them, the architecture diagram of convolutional neural network is: Input layer: receives pre-processed data such as vibration signals or temperature signals; Convolutional layer: extracts local features by sliding convolution kernel on input data; Activation layer: usually uses ReLU activation function to increase the non-linear ability of the network; Pooling layer: reduces the dimensionality of feature maps by downsampling, reducing computational load. Fully connected layer: integrates features extracted by convolutional and pooling layers for classification or regression tasks. The final fully connected layer usually uses a Softmax activation function to output fault classification results.

[0038] Architecture diagram of recurrent neural network: Input layer: receives sequence data such as vibration signals. Recurrent unit: receives current input and previous time step hidden state at each time step, outputs new hidden state.

[0039] Output layer: outputs prediction results based on hidden state; Long short-term memory network: to solve the long-range dependence problem of recurrent neural network, long short-term memory network introduces gating mechanism, including input gate, forget gate and output gate; In this embodiment, the architecture structure of combining convolutional neural network and recurrent neural network is adopted, the convolutional neural network is used to extract the features of input data first, and then the feature sequence is input into the recurrent neural network for further processing.

[0040] Implementation: the convolutional neural network part is responsible for extracting local features, the recurrent neural network part is responsible for capturing the time dependence of features, and finally the fault diagnosis result is output through the fully connected layer; Through the above architecture and implementation method, the intelligent fault diagnosis system can effectively extract features from the multi-source data of chemical equipment, and use deep learning model for fault diagnosis, improving the accuracy and reliability of diagnosis.

[0041] Linkage feedback mechanism connects fault diagnosis system and monitoring device through high-speed communication link. The diagnosis system feeds back the diagnosis result to the central controller of the monitoring device, and the central controller adjusts the monitoring strategy according to the diagnosis result, forming a closed-loop feedback. The fault diagnosis system is responsible for real-time monitoring of the running state of the equipment and fault analysis, and feeds back the diagnosis result to the central controller of the monitoring device through the high-speed communication link. After receiving the diagnosis result, the central controller of the monitoring device adjusts the monitoring strategy according to the feedback information, so as to realize accurate monitoring and early warning. The function of the fault diagnosis system is to provide accurate fault diagnosis information, and the central controller of the monitoring device optimizes the monitoring strategy according to these information to ensure the safety and stability of the equipment operation. Through this closed-loop feedback mechanism, the whole system can quickly respond to potential faults of the equipment, adjust the monitoring parameters in time, and improve the reliability and operation efficiency of the system.

[0042] Sensor nodes regularly send data collection status reports to the central controller, which dynamically adjusts its collection strategy based on these reports to ensure the monitoring device is always operating optimally. The primary function of sensor nodes is to regularly collect data and send status reports to the central controller. Based on these reports, the central controller dynamically adjusts its data collection strategy to ensure the monitoring device is always operating optimally. Specifically, sensor nodes monitor environmental changes and collect data in real time. The central controller uses this data to determine the current monitoring status and adjust parameters such as the collection frequency and range to optimize monitoring effectiveness. This dynamic adjustment strategy enables timely response to environmental changes, ensuring the accuracy and real-time nature of monitoring data, and thus ensuring the efficient operation of the monitoring device.

[0043] The multi-source data fusion module pre-processes the data before fusion to eliminate noise and outliers. This pre-processing improves the quality and accuracy of data fusion, enhances the reliability and consistency of the final output data, and effectively improves the performance and stability of the entire system.

[0044] Specific steps and methods for multi-source data fusion: 1. Data Preprocessing Before data fusion, the collected raw data needs to be preprocessed to eliminate noise, outliers, and inconsistencies in the data. The preprocessing steps include: Data cleaning: Remove obviously erroneous or invalid data points. For example, if a sensor's reading is clearly outside its measurement range, it can be marked as invalid data.

[0045] Filtering: Use a low-pass filter to remove high-frequency noise and smooth the data curve. For example, for vibration data, a low-pass filter can be used to remove high-frequency interference.

[0046] Normalization: Convert data of different dimensions to the same dimension to reduce the differences between the data. For example, normalize temperature and pressure data to the range of 0 to 1.

[0047] Outlier detection and removal: Detect and remove obviously abnormal data points using statistical methods or machine learning algorithms. For example, use the Z-score or boxplot method to detect outliers.

[0048] 2. Data alignment Since different sensors may have different sampling frequencies and timestamps, the data needs to be time-aligned to ensure temporal consistency. Alignment methods include: Time interpolation: For data with a low sampling frequency, the sampling frequency can be increased to be consistent with the high-frequency data through interpolation methods.

[0049] Time window alignment: Divide data from different sensors into the same time window, for example, divide all sensor data into 1-second time windows, and take the average or median value in each window.

[0050] 3. Feature extraction Extract features from preprocessed data that can reflect the running state of the equipment. Feature extraction methods include: Statistical features: Calculate statistical quantities such as mean, variance, standard deviation, maximum value, minimum value, etc. For example, calculate the mean and standard deviation of vibration data, which can reflect the vibration intensity and stability of the equipment.

[0051] Time domain features: Extract time domain features of data such as period, frequency, amplitude, etc. For example, extract frequency features of vibration data through fast Fourier transform.

[0052] Frequency domain features: Perform frequency domain analysis on data and extract frequency domain features. For example, extract multi-scale features of signals through wavelet transform.

[0053] 4. Data fusion algorithm Fuse the extracted features to generate comprehensive feature data. Common data fusion algorithms include: Weighted average method: Assign weights according to the influence of each data on the running state of the equipment, then calculate the weighted average. For example, vibration data is more critical to mechanical fault diagnosis, so it can be given a higher weight; temperature data is more important to heat exchange equipment diagnosis, so it is assigned a weight accordingly.

[0054] Kalman filter: Suitable for data fusion of dynamic systems, through recursive estimation method to fuse multi-source data, reduce noise and uncertainty.

[0055] Bayesian fusion: Use Bayes' theorem to fuse multi-source data, calculate comprehensive feature data according to prior probability and posterior probability.

[0056] Machine learning method: such as support vector machine, neural network, etc., through training model to fuse multi-source data, learn the complex relationship between data.

[0057] 5. Verification of fusion results Verify the results of fusion to ensure their accuracy and reliability. Verification methods include: Compare with historical data: Compare the fusion results with historical fault data and normal operation data to verify their consistency.

[0058] Compare with expert experience: Invite experts in the field to evaluate the fusion results to ensure they meet the actual operating conditions.

[0059] Cross-validation: The fusion algorithm is evaluated by cross-validation method to ensure its stability and accuracy on different datasets.

[0060] The intelligent fault diagnosis system has self-learning ability, can continuously optimize the diagnosis model according to new fault cases, improve the diagnosis precision, through continuously collecting equipment operation data, the self-learning module optimizes the diagnosis model using these data, the diagnosis module analyzes the equipment state in real time based on the optimized model, and the final feedback module feeds back the diagnosis result to the user in time, realizes the fast and accurate identification and processing of equipment fault.

[0061] The linkage feedback mechanism can improve the monitoring frequency of the fault hidden danger part according to the diagnosis result, and adjust the collection parameters of the sensor node, so as to obtain more detailed fault characteristic data. Through the diagnosis result, the potential fault parts are identified, and the monitoring frequency of these parts is improved according to the diagnosis result, so that the potential problems are found earlier. The sensor node will adjust its collection parameters accordingly to obtain more detailed fault characteristic data to help further analysis and diagnosis. Specifically, the diagnosis system will evaluate the current situation, identify the equipment or components that may have problems, and feed back this information to the monitoring system. After receiving the feedback, the monitoring system will increase the monitoring frequency of these parts to more accurately capture abnormal changes. At the same time, the sensor node will adjust the collection parameters according to the new monitoring requirements, such as increasing the sampling frequency or changing the collection range, to obtain more accurate data. In this way, the whole system can more efficiently identify and diagnose faults, take measures in advance to avoid major accidents, prolong the service life of equipment, and ensure the stable operation of the system.

[0062] The system can reduce the false positive rate to below 10%, significantly improve the accuracy of fault diagnosis, and improve the monitoring efficiency and self-adaptability of the system through adaptive monitoring devices and linkage feedback mechanism. The monitoring device continuously monitors the running state of the system, and the adaptive monitoring device dynamically adjusts according to the actual running situation to ensure the sensitivity and accuracy of the monitoring. The linkage feedback mechanism can quickly respond to the monitoring results, quickly eliminate faults, and further improve the self-adaptability and monitoring efficiency of the system. The system combines these components to achieve an efficient fault diagnosis and monitoring mechanism, greatly improving the running stability and reliability of the system.

[0063] Taking a reaction kettle of a certain chemical enterprise as an example, the chemical equipment state monitoring device and fault diagnosis system of the present application are implemented.

[0064] S1: Monitoring device installation and debugging: Temperature sensors, pressure sensors and vibration sensors are installed at key positions of the reaction kettle, and the sensors are connected with the central controller through a wireless communication module.

[0065] The central controller initializes and debugs the sensors according to the preset adaptive strategy, ensuring normal operation of the sensors.

[0066] S2: Data collection and fusion: During the operation of the reactor, the sensors collect real-time temperature, pressure, and vibration data and send them to the central controller through wireless communication.

[0067] The central controller dynamically adjusts the collection frequency according to the adaptive strategy and transmits the collected data to the multi-source data fusion module.

[0068] The multi-source data fusion module preprocesses and weights the data to generate comprehensive feature data.

[0069] S3: Fault diagnosis and feedback: The comprehensive feature data is transmitted to the intelligent fault diagnosis system, which analyzes the data through deep learning algorithms to quickly locate the fault type and position.

[0070] The diagnosis result is displayed in a graphical interface, showing that one of the reactor's stirring shafts has abnormal vibration, possibly indicating bearing wear and tear.

[0071] The fault diagnosis system feeds back the diagnosis result to the central controller of the monitoring device, which adjusts the collection frequency of the vibration sensor at the stirring shaft position and adjusts the sensitivity of the sensor based on the diagnosis result.

[0072] S4: Closed-loop feedback and optimization: The monitoring device collects new data according to the adjusted strategy and transmits the new data to the fault diagnosis system.

[0073] The fault diagnosis system further verifies the accuracy of the fault diagnosis result based on the new data and updates the new fault features to the fault mode library, optimizing the diagnosis model.

[0074] Through this closed-loop feedback mechanism, the system can timely and accurately diagnose the fault hidden danger of the reactor, providing reliable basis for subsequent maintenance and repair.

[0075] Specifically, based on the above chemical equipment state monitoring device and fault diagnosis system: Taking a certain chemical reactor as an example, assuming that the normal operating temperature is 120°C, the pressure is 2 MPa, and the vibration frequency is 0.5 Hz. The following is a specific scenario for adjusting the collection frequency: Scenario 1: The temperature sensor detects a sudden temperature rise to 160°C, exceeding the preset threshold of 150°C. The central controller determines that the temperature is abnormal, immediately increases the collection frequency of the temperature sensor, and at the same time increases the collection frequency of other related sensors (such as pressure sensor and vibration sensor) to more comprehensively monitor the equipment state.

[0076] Scenario 2: The temperature sensor detects that the temperature rises from 120°C to 130°C in 10 minutes, with a change rate of 1°C per minute. Although the temperature is still within the normal range, the change rate is abnormal. The central controller will also increase the collection frequency to more closely monitor the temperature change trend.

[0077] Scenario 3: The temperature sensor detects a temperature of 125°C, the pressure sensor detects a pressure of 2.5 MPa, and the vibration sensor detects a vibration frequency of 0.6 Hz. Although each parameter is within the normal range, the comprehensive analysis shows that the temperature, pressure and vibration all show a slight upward trend at the same time. The central controller determines that the equipment may be in an unstable state and increases the collection frequency in advance to discover potential faults in advance.

[0078] The above is the preferred embodiment of the present application, and those skilled in the art can also make changes and modifications to the above embodiment, therefore, the present application is not limited to the specific embodiments described above, any obvious improvements, replacements or modifications made by those skilled in the art on the basis of the present application shall fall within the protection scope of the present application.

Claims

1. A chemical equipment status monitoring and fault diagnosis system, characterized by: It includes adaptive monitoring device, multi-source data fusion module, intelligent fault diagnosis system and linkage feedback mechanism; The adaptive monitoring device dynamically adjusts the data acquisition frequency according to the operating status of the chemical equipment; The multi-source data fusion module integrates the collected multi-source data to generate comprehensive feature data; The intelligent fault diagnosis system analyzes characteristic data based on a deep learning algorithm to locate the fault type and location; The linkage feedback mechanism enables deep interaction between the fault diagnosis system and the monitoring device, and adjusts the monitoring strategy according to the diagnosis results.

2. The zipper production equipment according to claim 1, characterized in that: The adaptive monitoring device includes several sensor nodes and a central controller. The sensor nodes are connected to the central controller via wireless communication technology. The central controller analyzes the operating parameters of the equipment in real time according to a preset adaptive strategy and automatically adjusts the sensor's acquisition frequency.

3. The zipper production equipment according to claim 2, characterized in that: The multi-source data fusion module adopts a weighted average algorithm to fuse different types of data, assigns weights according to the degree of influence of each data on the operating status of the chemical equipment, and generates comprehensive feature data.

4. The zipper production equipment according to claim 3, characterized in that: The intelligent fault diagnosis system is built based on a deep learning algorithm and adopts a convolutional neural network or a recurrent neural network architecture. It performs feature matching on input data through a preset fault pattern library to quickly locate the fault type and location.

5. The zipper production equipment according to claim 4, characterized in that: The linkage feedback mechanism connects the fault diagnosis system and the monitoring device through a high-speed communication link. The diagnosis system feeds back the diagnosis results to the central controller of the monitoring device. The central controller adjusts the monitoring strategy according to the diagnosis results to form a closed-loop feedback.

6. The zipper production equipment according to claim 5, characterized in that: The sensor nodes regularly send data collection status reports to the central controller, and the central controller dynamically adjusts the collection strategy according to the reports to ensure that the monitoring device is always in the best operating state.

7. The zipper production equipment according to claim 6, characterized in that: The multi-source data fusion module pre-processes the data before data fusion to eliminate noise and outliers in the data.

8. The zipper production equipment according to claim 7, characterized in that: The intelligent fault diagnosis system has self-learning capabilities and can continuously optimize the diagnosis model according to new fault cases to improve diagnostic accuracy.

9. The zipper production equipment according to claim 8, characterized in that: The linkage feedback mechanism can increase the monitoring frequency of potential fault locations according to the diagnosis results and adjust the acquisition parameters of the sensors to obtain more detailed fault characteristic data.

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