High-reliability AHF production device fault monitoring and early warning device
By designing a multi-modular high-reliability fault monitoring and early warning device, using multiple sensors and deep learning algorithms for data fusion and analysis, the shortcomings of traditional devices in fault monitoring and early warning are solved, and efficient and safe fault monitoring and early warning of AHF production devices are achieved.
Patent Information
- Application Number
- CN202411878851.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-19
- Publication Date
- 2025-04-29
AI Technical Summary
The fault monitoring and early warning devices of traditional AHF production devices have problems such as unreasonable layout, limited data processing, collection defects, insufficient diagnosis, imperfect warning mechanisms and lack of self-diagnosis, and it is difficult to meet the safety and reliability requirements of high standards.
A high-reliability AHF production device fault monitoring and early warning device is designed, including multiple types of sensors, multi-source data fusion modules, data acquisition modules, data analysis modules and early warning modules. Data fusion, adaptive data acquisition, intelligent data analysis through deep learning algorithms, and multi-channel redundant communication and hierarchical alarm mechanisms are adopted to achieve comprehensive, real-time and accurate fault monitoring and early warning of AHF production devices.
It realizes comprehensive, real-time and accurate fault monitoring and early warning of AHF production equipment, improves the reliability and safety of production equipment, reduces the risks and losses of faults, and provides strong guarantees for the stable operation of AHF production.
Smart Images
Figure CN119904959A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of AHF production, and in particular to a high-reliability AHF production device fault monitoring and early warning device. Background Art
[0002] In the production process of anhydrous hydrofluoric acid (AHF), traditional fault monitoring and early warning devices have many shortcomings and are difficult to meet high standards of safety and reliability.
[0003] Sensor Problems
[0004] Unreasonable layout: Lack of comprehensive monitoring, failure to cover key parts, affecting the timeliness of fault detection.
[0005] Data processing limitations
[0006] Insufficient data fusion: It is difficult to effectively combine spatial and temporal features, which affects state judgment.
[0007] Imperfect preprocessing: Insufficient outlier processing and data completion affect the accuracy of analysis.
[0008] Data collection flaws
[0009] Fixed sampling frequency: It cannot be adjusted adaptively according to actual conditions, resulting in data omission or redundancy.
[0010] Weak signal processing capability: signal amplification and filtering are not accurate enough, affecting data quality.
[0011] Insufficient fault analysis
[0012] Limited diagnostic and predictive capabilities: Traditional methods have difficulty accurately identifying fault types and predicting fault time, and preventive measures are not timely.
[0013] Early warning mechanism issues
[0014] Unreliable communication: A single communication method can easily lead to information transmission failure.
[0015] Lack of alarm classification: unable to respond effectively according to the urgency of the fault.
[0016] Self-diagnosis and storage issues
[0017] Lack of self-diagnosis: Unable to detect internal system faults in time.
[0018] Insecure data storage: Lack of encryption protection and data mining capabilities affects data utilization.
[0019] Therefore, this invention proposes a high-reliability AHF production device fault monitoring and early warning device to solve the above problems. Summary of the invention
[0020] In order to overcome the defects of the prior art, the object of the present invention is to provide a high-reliability AHF production device fault monitoring and early warning device.
[0021] To achieve the above object, the technical solution of the present invention is implemented as follows: a high-reliability AHF production device fault monitoring and early warning device, characterized in that it includes:
[0022] Sensor group, the sensor group includes various types of sensors, including temperature sensors, pressure sensors, flow sensors, liquid level sensors, gas concentration sensors and vibration sensors. The temperature sensors are set at the key parts of the reactor, heat exchanger and pipeline of the AHF production device to detect temperature. The pressure sensors are set at the reactor, storage tank and pipeline connection to detect pressure. The flow sensors are set at the raw material input pipeline and product output pipeline to detect material flow. The liquid level sensors are set in the reactor and storage tank to detect the liquid level height. The gas concentration sensor is set at the tail gas discharge port of the production device, near the possible leakage point and inside the reactor to detect the concentration of AHF and related gases. The vibration sensor is set at the supporting structure of the reactor, the base of the large pump body and the motor to detect the vibration of the equipment;
[0023] The multi-source data fusion module is connected to the sensor group and is used to receive various types of data collected by different sensors. It uses a multi-source data fusion algorithm based on deep learning to fuse the data. The algorithm can identify the implicit correlation between different sensor data through a pre-trained neural network model and output the fused data.
[0024] A data acquisition module is connected to the multi-source data fusion module and is used to acquire the fused data output by the multi-source data fusion module and convert it into a digital signal;
[0025] A data analysis module is connected to the data acquisition module. The data analysis module pre-stores a comprehensive state characteristic model based on multi-source data fusion when the AHF production device is operating normally. The data analysis module is used to receive the digital signal and compare and analyze it with the pre-stored comprehensive state characteristic model.
[0026] The early warning module is connected to the data analysis module. When the data analysis module determines that there is a significant difference between the collected data and the comprehensive state characteristic model, the early warning module sends out an early warning signal. The early warning signal includes sound alarm, light alarm and sending alarm information to the remote monitoring terminal. The remote monitoring terminal can view the fused multi-source data information, fault diagnosis results and early warning level in real time.
[0027] Preferably, the temperature sensor is a high-sensitivity thermocouple sensor with a micro-nano structure, whose measurement accuracy is within the range of ±0.1°C, and a special coating that resists AHF corrosion is coated on the surface of the sensor probe; the pressure sensor is a high-pressure and corrosion-resistant pressure sensor using a new type of ceramic sensitive element, and the measurement accuracy is within the range of ±0.005MPa; the flow sensor is a high-precision mass flow sensor based on the Coriolis force principle, with a measurement accuracy within ±0.5%, and a self-cleaning structure is provided inside to prevent AHF crystallization blockage; the liquid level sensor is a non-contact liquid level sensor using millimeter-wave radar technology, with a measurement accuracy within the range of ±1mm, and its transmitting antenna is made of special corrosion-resistant materials; the gas concentration sensor is an electrochemical gas concentration sensor based on quantum dot materials with ultra-high sensitivity and selectivity to AHF, with a detection lower limit of 0.05ppm and a response time of less than 5 seconds; the vibration sensor is a high-precision optical fiber vibration sensor that can detect tiny vibrations of 0.01μm and a frequency response range of 1-1000Hz.
[0028] The layout of the sensor group is optimized based on the process simulation and fault propagation path analysis of the AHF production unit. The spacing and installation angles between the sensors are calculated to ensure comprehensive and no-dead-angle monitoring of the production unit. The installation position of the sensors takes into account the physical and chemical properties of AHF and the distribution of flow field, temperature field and pressure field during the production process.
[0029] Preferably, the deep learning algorithm in the multi-source data fusion module adopts an architecture combining a convolutional neural network (CNN) and a long short-term memory network (LSTM), wherein CNN is used to extract spatial features from sensor data, and LSTM is used to process temporal features of data, and the operating status of the AHF production device is accurately characterized by fusing these two features;
[0030] The multi-source data fusion module has data preprocessing functions, including data normalization, outlier removal and data completion. Data normalization uses the minimum-maximum normalization method to map different types of sensor data to the [0,1] interval, outlier removal uses the 3σ principle combined with the local outlier factor (LOF) algorithm, and data completion uses a linear interpolation method based on historical data.
[0031] Preferably, the sampling frequency of the data acquisition module can be adaptively adjusted within the range of 0.5-200 Hz, and the adjustment is based on the real-time operating status of the production device and the change rate of different sensor data;
[0032] The data acquisition module has a high-precision signal amplification circuit, the amplification factor can be accurately adjusted within the range of 1-1000 times, and adopts an adaptive filtering function based on wavelet transform, which can automatically adjust the filtering parameters according to the frequency characteristics of the signal and effectively remove various types of noise interference in the collected signal, including white noise, narrowband noise and pulse noise.
[0033] Preferably, the data analysis module adopts a fault diagnosis method based on a hybrid intelligent algorithm, which combines a support vector machine (SVM) and a genetic algorithm (GA). First, the parameters of the SVM are optimized using the GA, and then the optimized SVM is used to compare and analyze the collected data with the pre-stored comprehensive state characteristic model, which can improve the accuracy and efficiency of fault diagnosis;
[0034] The data analysis module also has a fault prediction function. Based on the hidden Markov model (HMM), it analyzes historical data and current data to predict the possible fault types and fault occurrence times of the AHF production equipment, with the prediction time being up to 1-24 hours in advance.
[0035] Preferably, the communication between the early warning module and the remote monitoring terminal adopts a multi-channel redundant communication method, including wired industrial Ethernet and optical fiber communication and wireless Wi-Fi, ZigBee and 5G communication networks, to ensure reliable transmission of alarm information;
[0036] The early warning module issues graded alarms according to the severity and urgency of the fault, which are divided into minor fault warnings, moderate fault warnings and severe fault warnings. Different levels of warnings differ in the frequency and loudness of the sound alarm, the color and flashing frequency of the light alarm, and the level of detail of the alarm information sent to the remote monitoring terminal. Severe fault warning information can also automatically trigger relevant emergency operation plans.
[0037] Preferably, the device further comprises a self-diagnosis module, which is connected to the multi-source data fusion module, the data acquisition module, the data analysis module and the early warning module. The self-diagnosis module adopts a model-based diagnosis method to construct a normal operation model of each module, and regularly performs function tests on each module. When a module is detected to have a fault, the self-diagnosis module sends a self-diagnosis fault alarm signal through the early warning module, and can automatically switch to a standby module or adjust the operation mode of the device to maintain basic monitoring functions;
[0038] The device has an adaptive learning function, which can automatically update the model parameters of the deep learning algorithm in the multi-source data fusion module, the parameters of the fault diagnosis and prediction algorithm in the data analysis module, and the alarm thresholds and classification standards in the early warning module according to the long-term operation data, maintenance records and process adjustments of the AHF production device.
[0039] Preferably, the device further comprises a data storage module, which is connected to the data acquisition module and is used to store all collected sensor data, fused data, fault diagnosis results and warning records. The data storage module adopts a distributed storage architecture, including a local disk array and a cloud storage service, with a total storage capacity of not less than 5TB, and the data storage module adopts encrypted storage technology and uses the AES-256-bit encryption algorithm to encrypt the stored data to ensure the security and integrity of the data;
[0040] The data storage module has a data mining function, which can analyze the stored data based on data mining algorithms (such as association rule mining algorithm Apriori), and mine the potential relationship between different fault types and sensor data characteristics, providing more basis for fault diagnosis and prediction.
[0041] The beneficial effects of the present invention are embodied in:
[0042] This high-reliability AHF production unit fault monitoring and early warning device, through precise monitoring of the sensor group, multi-source data fusion, adaptive data acquisition, intelligent data analysis, reliable early warning mechanism, self-diagnosis function and secure data storage management, realizes comprehensive, real-time and accurate fault monitoring and early warning of AHF production units, improves the reliability and safety of production units, reduces the risk and loss of failures, and provides a strong guarantee for the stable operation of AHF production. At the same time, the device has strong versatility and scalability, and can be applied to other similar chemical production units, with broad application prospects and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS
[0043] In the attached picture:
[0044] Figure 1 It is a schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION
[0045] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only part of the embodiments of the invention, rather than all of the embodiments. In the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the invention.
[0046] Please refer to the instruction manual Figure 1 , the present invention provides a specific implementation of a high-reliability AHF production device fault monitoring and early warning device:
[0047] 1. Installation and layout of sensor group
[0048] Sensor selection basis
[0049] According to the characteristics of the AHF production device, sensors with specific performance are selected. For example, the temperature sensor uses a high-sensitivity thermocouple sensor with a micro-nano structure. Due to the high-precision requirements for temperature monitoring during the AHF production process, its measurement accuracy is within the range of ±0.1°C, and in order to resist the corrosiveness of AHF, a special coating that resists AHF corrosion is applied on the surface of the sensor probe.
[0050] The pressure sensor uses a high-pressure and corrosion-resistant pressure sensor with a new type of ceramic sensitive element. The measurement accuracy is within the range of ±0.005MPa to adapt to the precise measurement of different pressure environments in the production equipment.
[0051] The flow sensor uses a high-precision mass flow sensor based on the Coriolis force principle, with a measurement accuracy within ±0.5%. Considering that AHF may crystallize and block the pipeline, a self-cleaning structure is provided inside.
[0052] The liquid level sensor adopts non-contact liquid level sensor with millimeter wave radar technology. The measurement accuracy is within ±1mm. The transmitting antenna is made of special corrosion-resistant material, which is suitable for accurate detection of liquid levels in reactors and storage tanks.
[0053] The gas concentration sensor uses an electrochemical gas concentration sensor based on quantum dot materials with ultra-high sensitivity and selectivity to AHF. The detection limit reaches 0.05ppm and the response time is less than 5 seconds. It is used to detect the concentration of AHF and related gases.
[0054] The vibration sensor uses a high-precision fiber optic vibration sensor that can detect tiny vibrations of 0.01μm and has a frequency response range of 1-1000Hz. It can effectively monitor the vibration of the reactor's supporting structure, large pump body and motor base.
[0055] Layout optimization
[0056] The layout of the sensor group is determined based on the process simulation and fault propagation path analysis of the AHF production device. For example, the installation location of the sensor is determined by simulating the material flow, energy transfer and other processes of the reactor, heat exchanger, pipeline and other equipment during the production process, as well as the propagation direction of possible faults.
[0057] Calculate the spacing and installation angles between sensors to ensure comprehensive and blind-angle monitoring of the production unit. Considering the physical and chemical properties of AHF, such as density and corrosiveness, as well as the distribution of flow field, temperature field and pressure field in the production process, temperature sensors are installed in the key parts of the reactor, heat exchanger and pipeline of the AHF production unit to detect temperature; pressure sensors are installed in the reactor, storage tank and pipeline connection to detect pressure; flow sensors are installed in the raw material input pipeline and product output pipeline to detect material flow; liquid level sensors are installed in the reactor and storage tank to detect liquid level height; gas concentration sensors are installed at the exhaust outlet of the production unit, near possible leakage points and inside the reactor to detect AHF and related gas concentrations; vibration sensors are installed in the supporting structure of the reactor, the base of the large pump body and the motor to detect the vibration of the equipment.
[0058] 2. Implementation of Multi-source Data Fusion Module
[0059] Deep learning algorithm architecture
[0060] The architecture combines convolutional neural network (CNN) and long short-term memory network (LSTM). In data processing, CNN is used to extract spatial features from sensor data. For example, for temperature sensor data at different locations of the reactor, CNN can identify the spatial distribution characteristics of temperature. LSTM is used to process the temporal characteristics of data, such as the trend of flow sensor data over time. By integrating these two features, the operating status of the AHF production unit can be accurately characterized.
[0061] Data preprocessing
[0062] Data normalization: Use the minimum-maximum normalization method to map different types of sensor data to the [0,1] interval. For example, the measurement values of the temperature sensor and the pressure sensor are normalized according to their respective maximum and minimum values to facilitate subsequent algorithm processing.
[0063] Outlier elimination: The 3σ principle combined with the local outlier factor (LOF) algorithm is used. First, the normal range of the data is determined according to the 3σ principle. For data outside this range, the LOF algorithm is used to further determine whether it is a true outlier. If so, it is eliminated.
[0064] Data completion: A linear interpolation method based on historical data is used. When some sensor data is missing, linear interpolation is used to complete the missing data according to the historical data trend of the sensor.
[0065] 3. Operation of Data Acquisition Module
[0066] Sampling frequency adjustment
[0067] The sampling frequency of the data acquisition module can be adaptively adjusted within the range of 0.5-200Hz. For example, during the startup phase of the production unit or when the operating status changes dramatically, such as when the temperature of the reactor rises rapidly, the sampling frequency is increased to 200Hz based on the faster rate of change of the temperature sensor data; while when the production unit is running stably, the sampling frequency can be reduced to 0.5Hz based on the real-time operating status of the production unit and the rate of change of different sensor data.
[0068] Signal Processing
[0069] Signal amplification: The data acquisition module has a high-precision signal amplification circuit, and the amplification factor can be accurately adjusted within the range of 1-1000 times. According to the weakness of the sensor output signal, such as the weak vibration signal output by the vibration sensor, the amplification factor can be appropriately adjusted to increase the signal strength for subsequent processing.
[0070] Adaptive filtering: Adopts adaptive filtering function based on wavelet transform. For the collected signals containing various types of noise interference (such as white noise, narrowband noise and impulse noise), the filtering parameters are automatically adjusted according to the frequency characteristics of the signal. For example, for the flow sensor signal containing high-frequency narrowband noise, the noise interference can be effectively removed by adaptively adjusting the parameters of wavelet transform.
[0071] 4. Functional Execution of Data Analysis Module
[0072] Troubleshooting
[0073] A fault diagnosis method based on a hybrid intelligent algorithm is adopted, combining support vector machine (SVM) and genetic algorithm (GA). First, GA is used to optimize the parameters of SVM, for example, the optimal kernel function parameters of SVM are found through GA's search ability. Then, the optimized SVM is used to compare and analyze the collected data with the pre-stored comprehensive state feature model. When there is a difference between the collected data and the pre-stored model, the existence of a fault and the type of fault can be judged based on the size and characteristics of the difference, which can improve the accuracy and efficiency of fault diagnosis.
[0074] Failure prediction
[0075] The historical data and current data are analyzed based on the Hidden Markov Model (HMM). For example, based on the sensor data of the reactor temperature, pressure, etc. in the past period of time and the current real-time data, the possible fault type and fault occurrence time of the AHF production unit are predicted, and the prediction time can be advanced by 1-24 hours.
[0076] 5. Alarm mechanism of early warning module
[0077] Communication
[0078] The communication between the early warning module and the remote monitoring terminal adopts a multi-channel redundant communication method, including wired industrial Ethernet and optical fiber communication as well as wireless Wi-Fi, ZigBee and 5G communication networks. For example, under normal circumstances, industrial Ethernet is used first for alarm information transmission; when industrial Ethernet fails, it automatically switches to optical fiber communication or wireless communication to ensure reliable transmission of alarm information.
[0079] Classification alarm
[0080] The early warning module provides graded alarms according to the severity and urgency of the fault, which are divided into minor fault warning, moderate fault warning and severe fault warning.
[0081] Minor fault warning: The sound alarm uses a lower frequency and lower loudness sound, and the light alarm uses a light yellow light with a slower flashing frequency, sending basic fault prompt information to the remote monitoring terminal.
[0082] Moderate fault warning: The frequency and volume of the sound alarm are appropriately increased, the light alarm uses orange light and the flashing frequency is accelerated, and an alarm message containing more fault details is sent to the remote monitoring terminal.
[0083] Serious fault warning: The sound alarm uses high-frequency, high-loudness sound, and the light alarm uses red light and flashes quickly, sending detailed fault information to the remote monitoring terminal. In addition, the serious fault warning information can also automatically trigger related emergency operation plans, such as shutting down certain key equipment.
[0084] 6. Operation of the self-diagnosis module
[0085] Module detection
[0086] The self-diagnosis module adopts a model-based diagnosis method to build a normal operation model for each module (multi-source data fusion module, data acquisition module, data analysis module and early warning module). The function of each module is regularly tested. For example, for the data acquisition module, check whether its sampling frequency is within the specified range and whether the signal amplification factor is accurate.
[0087] Troubleshooting
[0088] When a module fails, the self-diagnosis module sends a self-diagnosis fault alarm signal through the early warning module. If the data acquisition module fails and there is a spare module, it will automatically switch to the spare module; if there is no spare module, adjust the operation mode of the device, such as reducing the data acquisition frequency or using a simplified data analysis algorithm to maintain basic monitoring functions.
[0089] 7. Data Storage Module Management
[0090] Data storage
[0091] The data storage module adopts a distributed storage architecture, including local disk arrays and cloud storage services, with a total storage capacity of no less than 5TB. All collected sensor data, fused data, fault diagnosis results and warning records are stored in the data storage module.
[0092] The AES-256 bit encryption algorithm is used to encrypt the stored data to ensure the security and integrity of the data. For example, the historical data of the temperature sensor is encrypted before being stored in the local disk array or cloud storage service.
[0093] Data Mining
[0094] The data storage module has a data mining function and can analyze the stored data based on data mining algorithms (such as the association rule mining algorithm Apriori). For example, the potential relationship between the abnormal increase in reactor temperature and the subsequent decrease in pipeline flow can be found. This relationship may indicate a certain type of fault, providing more basis for fault diagnosis and prediction.
[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
Claims
1. A high-reliability AHF production device fault monitoring and early warning device, characterized in that: include: A sensor group, the sensor group includes multiple types of sensors, including temperature sensors, pressure sensors, flow sensors, liquid level sensors, gas concentration sensors and vibration sensors. The temperature sensors are arranged at key parts of the reactor, heat exchanger and pipeline of the AHF production device to detect temperature. The pressure sensors are arranged at the reactor, storage tank and pipeline connection to detect pressure. The flow sensors are arranged at the raw material input pipeline and product output pipeline to detect material flow. The liquid level sensors are arranged in the reactor and storage tank to detect liquid level height. The gas concentration sensors are arranged at the tail gas discharge port of the production device, near possible leakage points and inside the reactor to detect AHF and related gas concentrations. The vibration sensors are arranged at the supporting structure of the reactor, the large pump body and the base of the motor to detect the vibration of the equipment; A multi-source data fusion module is connected to the sensor group and is used to receive various types of data collected by different sensors, and fuse the data using a multi-source data fusion algorithm based on deep learning. The algorithm can identify the implicit correlation between different sensor data through a pre-trained neural network model and output the fused data; A data acquisition module, connected to the multi-source data fusion module, for acquiring the fused data output by the multi-source data fusion module and converting it into a digital signal; A data analysis module connected to the data acquisition module, wherein the data analysis module pre-stores a comprehensive state characteristic model based on multi-source data fusion when the AHF production device is operating normally, and the data analysis module is used to receive the digital signal and compare and analyze it with the pre-stored comprehensive state characteristic model; An early warning module is connected to the data analysis module. When the data analysis module determines that there is a significant difference between the collected data and the comprehensive state characteristic model, the early warning module sends out an early warning signal. The early warning signal includes a sound alarm, a light alarm, and sending an alarm information to a remote monitoring terminal, wherein the remote monitoring terminal can view the fused multi-source data information, fault diagnosis results, and early warning levels in real time.
2. A high reliability AHF production device fault monitoring and early warning device according to claim 1, characterized in that: The temperature sensor is a high-sensitivity thermocouple sensor with a micro-nano structure, with a measurement accuracy within the range of ±0.1°C, and a special coating that resists AHF corrosion is coated on the surface of the sensor probe; the pressure sensor is a high-pressure and corrosion-resistant pressure sensor using a new type of ceramic sensitive element, with a measurement accuracy within the range of ±0.005MPa; the flow sensor is a high-precision mass flow sensor based on the Coriolis force principle, with a measurement accuracy within ±0.5%, and a self-cleaning structure is provided inside to prevent AHF crystallization blockage; the liquid level sensor is a non-contact liquid level sensor using millimeter wave radar technology, with a measurement accuracy within the range of ±1mm, and its transmitting antenna is made of special corrosion-resistant materials; the gas concentration sensor is an electrochemical gas concentration sensor based on quantum dot materials with ultra-high sensitivity and selectivity to AHF, with a detection lower limit of 0.05ppm and a response time of less than 5 seconds; the vibration sensor is a high-precision optical fiber vibration sensor that can detect tiny vibrations of 0.01μm and a frequency response range of 1-1000Hz. The layout of the sensor group is optimized based on the process simulation and fault propagation path analysis of the AHF production device. The spacing and installation angles between the sensors are calculated to ensure comprehensive and no-dead-angle monitoring of the production device. The installation position of the sensors takes into account the physical and chemical properties of AHF and the flow field, temperature field and pressure field distribution during the production process.
3. A high reliability AHF production device fault monitoring and early warning device according to claim 1, characterized in that: The deep learning algorithm in the multi-source data fusion module adopts a convolutional neural network (CNN) combined with a long short-term memory network (LSTM), wherein CNN is used to extract spatial features from sensor data, and LSTM is used to process the temporal features of data, and the operating status of the AHF production device is accurately characterized by fusing these two features; The multi-source data fusion module has data preprocessing functions, including data normalization, outlier removal and data completion, wherein data normalization uses the minimum-maximum normalization method to map different types of sensor data to the [0,1] interval, outlier removal uses the 3σ principle combined with the local outlier factor (LOF) algorithm, and data completion uses a linear interpolation method based on historical data.
4. A high reliability AHF production device fault monitoring and early warning device according to claim 1, characterized in that: The sampling frequency of the data acquisition module can be adaptively adjusted within the range of 0.5-200 Hz, and the adjustment is based on the real-time operating status of the production device and the change rate of different sensor data; The data acquisition module has a high-precision signal amplification circuit, the amplification factor can be accurately adjusted within the range of 1-1000 times, and adopts an adaptive filtering function based on wavelet transform, which can automatically adjust the filtering parameters according to the frequency characteristics of the signal and effectively remove various types of noise interference in the collected signal, including white noise, narrowband noise and pulse noise.
5. A high reliability AHF production device fault monitoring and early warning device according to claim 1, characterized in that: The data analysis module adopts a fault diagnosis method based on a hybrid intelligent algorithm, which combines a support vector machine (SVM) and a genetic algorithm (GA). First, the GA is used to optimize the parameters of the SVM, and then the optimized SVM is used to compare and analyze the collected data with the pre-stored comprehensive state characteristic model, which can improve the accuracy and efficiency of fault diagnosis; The data analysis module also has a fault prediction function. Based on the Hidden Markov Model (HMM), historical data and current data are analyzed to predict the possible fault types and fault occurrence time of the AHF production device, and the prediction time can be advanced by 1-24 hours.
6. A high reliability AHF production device fault monitoring and early warning device according to claim 1, characterized in that: The communication between the early warning module and the remote monitoring terminal adopts a multi-channel redundant communication method, including wired industrial Ethernet and optical fiber communication as well as wireless Wi-Fi, ZigBee and 5G communication networks to ensure reliable transmission of alarm information; The early warning module performs graded alarms according to the severity and urgency of the fault, which are divided into minor fault warnings, moderate fault warnings and severe fault warnings. Different levels of warnings differ in the frequency and loudness of the sound alarm, the color and flashing frequency of the light alarm, and the level of detail of the alarm information sent to the remote monitoring terminal. Severe fault warning information can also automatically trigger relevant emergency operation plans.
7. A high reliability AHF production device fault monitoring and early warning device according to claim 1, characterized in that: The device further comprises a self-diagnosis module, which is connected to the multi-source data fusion module, the data acquisition module, the data analysis module and the early warning module. The self-diagnosis module adopts a model-based diagnosis method to construct a normal operation model of each module, and regularly performs function detection on each module. When a module is detected to have a fault, the self-diagnosis module sends a self-diagnosis fault alarm signal through the early warning module, and can automatically switch to a standby module or adjust the operation mode of the device to maintain basic monitoring functions; The device has an adaptive learning function, and can automatically update the model parameters of the deep learning algorithm in the multi-source data fusion module, the parameters of the fault diagnosis and prediction algorithm in the data analysis module, and the alarm threshold and classification standard in the early warning module according to the long-term operation data, maintenance records and process adjustment of the AHF production device.
8. A high reliability AHF production device fault monitoring and early warning device according to claim 1, characterized in that: The device also includes a data storage module, which is connected to the data acquisition module and is used to store all collected sensor data, fused data, fault diagnosis results and warning records. The data storage module adopts a distributed storage architecture, including a local disk array and a cloud storage service, with a total storage capacity of not less than 5TB. The data storage module adopts encrypted storage technology and uses an AES-256-bit encryption algorithm to encrypt the stored data to ensure the security and integrity of the data. The data storage module has a data mining function, and can analyze the stored data based on a data mining algorithm (such as the association rule mining algorithm Apriori), and mine the potential relationship between different fault types and sensor data features, providing more basis for fault diagnosis and prediction.