Fault monitoring system of frequency converter

Abstract

The invention discloses a fault monitoring system of a frequency converter, and particularly relates to the technical field of frequency conversion equipment. The data acquisition module is used for collecting various necessary data in the operation process of the frequency converter; the data processing module is used for receiving and processing original data transmitted from the data acquisition module; the fault diagnosis module is used for diagnosing the operation state of the frequency converter according to the analysis result provided by the data processing module; and the user interface module is used for providing an interface interactive with the system for a user to display monitoring and diagnosis results and providing functions of alarming, report generation and the like. Through close cooperation of the modules, a complete process from data acquisition to fault diagnosis to user feedback is realized, and the stability and reliability of equipment operation are effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of frequency conversion equipment, and in particular to a fault monitoring system for a frequency converter. Background Art

[0002] The frequency converter is a key device used to adjust the motor speed and control the motor operation. It is widely used in industrial, commercial and household appliances. The frequency converter changes the power supply frequency of the motor to achieve precise control of the motor speed, thereby improving energy efficiency and reducing energy consumption. In modern industrial production, the use of frequency converters not only improves the flexibility and efficiency of production equipment, but also has important significance for energy conservation and emission reduction. However, since the frequency converter operates under high load and complex environment, faults are inevitable. An effective fault monitoring and diagnosis system plays an important role in ensuring the stable operation of the frequency converter and extending the life of the equipment.

[0003] The existing inverter fault monitoring system has some defects in actual use. First, the traditional fault monitoring system usually relies on simple sensor data and fixed threshold judgment methods, which is difficult to cope with complex and changeable operating environments, resulting in insufficient accuracy and timeliness of fault detection. Secondly, the existing system lacks comprehensive data processing and analysis capabilities, and is unable to effectively clean, normalize and extract features from a large amount of operating data, which affects the accuracy and efficiency of fault diagnosis. In addition, the traditional monitoring system also has shortcomings in fault prediction, and is unable to detect potential faults in advance through trend analysis and prediction models, resulting in the inability to take preventive measures in time after the fault occurs. Finally, the user interface module has weak interactivity and remote access capabilities, which limits the user's real-time monitoring and management of the inverter's operating status. Therefore, we provide a fault monitoring system for the inverter. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a fault monitoring system for a frequency converter.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A fault monitoring system for a frequency converter, comprising a data acquisition module for collecting various necessary data during the operation of the frequency converter, a data processing module for receiving and processing raw data transmitted from the data acquisition module, a fault diagnosis module for diagnosing the operating state of the frequency converter using the analysis results provided by the data processing module, and a user interface module for providing an interface for users to interact with the system and display monitoring and diagnosis results and providing functions such as alarm and report generation;

[0007] The data acquisition module includes a sensor interface module for connecting various sensors to collect data, a data filtering module for filtering the collected data, a data buffer module for temporarily storing the collected data, and a data transmission module for transmitting the collected data to the data processing module for further processing;

[0008] The data processing module includes a preprocessing module for cleaning and normalizing data, a feature extraction module for extracting key feature parameters from the preprocessed data, a data analysis module for analyzing feature parameters using an algorithm, and a model training module for training a fault diagnosis model based on historical data and fault cases;

[0009] The fault diagnosis module includes a rule diagnosis module for making fault judgments using predefined rules and thresholds, an intelligent diagnosis module for identifying complex or unknown faults based on machine learning or artificial intelligence algorithms, a fault prediction module for predicting possible faults in advance through trend analysis and prediction models, and a fault location module for determining the specific location and components where the fault occurs;

[0010] The user interface module includes a monitoring interface module for displaying the operating status and monitoring data of the inverter in real time, a fault alarm module for sending an alarm signal when a fault or abnormality is detected, a report generation module for automatically generating regular operation reports and fault analysis reports, and a remote access module for allowing users to remotely access and monitor the system through the network.

[0011] The present invention is further configured as follows: the intelligent diagnosis module includes an abnormal data detection module for using advanced algorithms to detect and identify abnormal points in data in real time, a high-dimensional data dimensionality reduction module for converting high-dimensional data into low-dimensional data using PCA (principal component analysis) dimensionality reduction technology, a fault feature selection module for selecting the most representative feature from multiple fault features, an integrated learning algorithm module for applying an integrated learning algorithm to build a more powerful fault diagnosis model, a deep signal processing module for using a deep learning model to perform deep processing on signals, a model adaptive update module for realizing adaptive learning and updating of the model and continuously optimizing the fault diagnosis model according to new data and feedback, an efficient computing framework module for building an efficient computing framework to optimize the use of computing resources, an adaptive threshold determination module for dynamically adjusting the determination threshold of various types of faults based on real-time data and historical data analysis, and a fault impact assessment module for evaluating the impact of faults on the overall operation of the system, analyzing the severity of the faults and possible chain reactions.

[0012] The present invention is further configured as follows: the abnormal data detection module uses an advanced algorithm to detect and identify abnormal points in the data, and transmits the abnormal point data to the high-dimensional data dimension reduction module to perform dimension reduction processing on the data after abnormal detection; the high-dimensional data dimension reduction module performs dimension reduction processing on the data, and transmits the reduced data to the fault feature selection module for feature extraction; the fault feature selection module extracts and selects the most representative fault features, and transmits these feature data to the integrated learning algorithm module; the integrated learning algorithm module applies an integrated learning method to learn and diagnose the feature data, and transmits the preliminary diagnosis results to the deep signal processing module for further deep processing; the deep signal processing module uses a deep learning model to perform deep processing on the signal, And the processed fault modes and characteristics are fed back to the model adaptive update module; the model adaptive update module continuously optimizes the fault diagnosis model according to the new data and feedback, and transmits the optimized model to the high-efficiency computing framework module for efficient calculation and execution; the high-efficiency computing framework module provides optimized computing resource usage to support the real-time execution of the model, and transmits the diagnosis results to the adaptive threshold judgment module for dynamic threshold adjustment; the adaptive threshold judgment module dynamically adjusts the judgment threshold of various types of faults based on real-time data and historical data analysis, and transmits the judgment results to the fault impact assessment module; the fault impact assessment module evaluates the severity of the fault and possible chain reactions based on the adaptive threshold judgment results, and provides a detailed fault impact analysis report.

[0013] The present invention is further configured as follows: the sensor interface module connects various sensors to collect data, and transmits the collected data to the data filtering module for filtering processing; the data filtering module filters the collected data, and transmits the filtered data to the data buffer module for temporary storage; the data buffer module temporarily stores the filtered data, and transmits the data to the data transmission module for further transmission.

[0014] The present invention is further configured as follows: the data transmission module transmits the buffered data to the data processing module for further processing; the preprocessing module cleans and normalizes the data transmitted from the data acquisition module and transmits the preprocessed data to the feature extraction module for feature extraction; the feature extraction module extracts key feature parameters from the preprocessed data and transmits the extracted feature data to the data analysis module for analysis.

[0015] The present invention is further configured as follows: the data analysis module uses an algorithm to analyze the extracted characteristic parameters, and transmits the analysis results to the model training module for model training; the model training module trains a fault diagnosis model based on historical data and fault cases, and transmits the trained model to the fault diagnosis module for diagnosis; the rule diagnosis module uses predefined rules and thresholds to make fault judgments, and transmits the preliminary diagnosis results to the intelligent diagnosis module for further diagnosis; the intelligent diagnosis module identifies complex or unknown faults based on machine learning or artificial intelligence algorithms, and transmits the identification results to the fault prediction module for fault prediction; the fault prediction module predicts possible faults in advance through trend analysis and prediction models, and transmits the prediction results to the fault location module for fault location.

[0016] The present invention is further configured as follows: the fault location module determines the specific location and components where the fault occurs, and transmits the final diagnosis result to the user interface module for display and alarm; the monitoring interface module displays the operating status and monitoring data of the inverter in real time, and transmits the monitoring data to the fault alarm module for alarm detection; the fault alarm module sends an alarm signal when a fault or abnormality is detected, and transmits the alarm information to the report generation module for report generation; the report generation module automatically generates regular operation reports and fault analysis reports, and transmits the generated reports to the remote access module for remote viewing; the remote access module allows users to remotely access and monitor the system through the network, and provides remote viewing and management functions for monitoring data and diagnostic results.

[0017] The beneficial effects of the present invention are:

[0018] The present invention includes a data acquisition module, a data processing module, a fault diagnosis module and a user interface module. The modules work together to realize comprehensive data collection, processing, analysis and feedback functions. First, the data acquisition module connects to various sensors through the sensor interface module to collect various necessary data during the operation of the inverter, and performs preliminary processing and optimization of the data through the data filtering module. The processed data is stored in the data buffer module and transmitted to the data processing module through the data transmission module. The data processing module is responsible for cleaning, normalizing and feature extraction of the transmitted data. The preprocessing module cleans and normalizes the data, and the feature extraction module extracts key feature parameters from it, and further analyzes it through the data analysis module using algorithms. In the fault diagnosis module, the system uses a combination of rule diagnosis module and intelligent diagnosis module to perform fault diagnosis. The rule diagnosis module performs fault diagnosis based on predefined rules. The system uses the machine learning or artificial intelligence algorithms to identify complex or unknown faults. In addition, the system also includes a fault prediction module, which uses trend analysis and prediction models to predict possible faults in advance so that preventive measures can be taken in time. Finally, the user interface module is the interactive interface of the system, which displays the operating status and monitoring data of the inverter in real time. The monitoring interface module transmits data to the fault alarm module, and sends out an alarm signal when a fault or abnormality is detected. The report generation module is responsible for regularly generating operation reports and fault analysis reports, and supports the remote access module of the system operation, allowing users to remotely monitor and manage through the network. In summary, the inverter fault monitoring system realizes the complete process from data collection to fault diagnosis to user feedback through the close collaboration of various modules, effectively ensuring the stability and reliability of equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the system in the present invention.

[0020] Figure 2 It is a partial system diagram of the intelligent diagnosis module in the present invention. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0022] Example 1

[0023] like Figure 1As shown, a fault monitoring system for a frequency converter includes a data acquisition module for collecting various necessary data during the operation of the frequency converter, a data processing module for receiving and processing raw data transmitted from the data acquisition module, a fault diagnosis module for diagnosing the operating state of the frequency converter using the analysis results provided by the data processing module, and a user interface module for providing an interface for users to interact with the system and display monitoring and diagnosis results and providing functions such as alarm and report generation;

[0024] The data acquisition module includes a sensor interface module for connecting various sensors to collect data, a data filtering module for filtering the collected data, a data buffer module for temporarily storing the collected data, and a data transmission module for transmitting the collected data to the data processing module for further processing;

[0025] The data processing module includes a preprocessing module for cleaning and normalizing data, a feature extraction module for extracting key feature parameters from the preprocessed data, a data analysis module for analyzing feature parameters using an algorithm, and a model training module for training a fault diagnosis model based on historical data and fault cases;

[0026] The fault diagnosis module includes a rule diagnosis module for making fault judgments using predefined rules and thresholds, an intelligent diagnosis module for identifying complex or unknown faults based on machine learning or artificial intelligence algorithms, a fault prediction module for predicting possible faults in advance through trend analysis and prediction models, and a fault location module for determining the specific location and components where the fault occurs;

[0027] The user interface module includes a monitoring interface module for displaying the operating status and monitoring data of the inverter in real time, a fault alarm module for issuing an alarm signal when a fault or abnormality is detected, a report generation module for automatically generating regular operation reports and fault analysis reports, and a remote access module for allowing the user to remotely access and monitor the system through the network;

[0028] The sensor interface module connects various sensors to collect data, and transmits the collected data to the data filtering module for filtering; the data filtering module filters the collected data, and transmits the filtered data to the data buffer module for temporary storage; the data buffer module temporarily stores the filtered data, and transmits the data to the data transmission module for further transmission;

[0029] The data transmission module transmits the buffered data to the data processing module for further processing; the preprocessing module cleans and normalizes the data transmitted from the data acquisition module and transmits the preprocessed data to the feature extraction module for feature extraction; the feature extraction module extracts key feature parameters from the preprocessed data and transmits the extracted feature data to the data analysis module for analysis;

[0030] The data analysis module uses an algorithm to analyze the extracted characteristic parameters, and transmits the analysis results to the model training module for model training; the model training module trains a fault diagnosis model based on historical data and fault cases, and transmits the trained model to the fault diagnosis module for diagnosis; the rule diagnosis module uses predefined rules and thresholds to make fault judgments, and transmits the preliminary diagnosis results to the intelligent diagnosis module for further diagnosis; the intelligent diagnosis module identifies complex or unknown faults based on machine learning or artificial intelligence algorithms, and transmits the identification results to the fault prediction module for fault prediction; the fault prediction module predicts possible faults in advance through trend analysis and prediction models, and transmits the prediction results to the fault location module for fault location;

[0031] The fault location module determines the specific location and component where the fault occurs, and transmits the final diagnosis result to the user interface module for display and alarm; the monitoring interface module displays the operating status and monitoring data of the inverter in real time, and transmits the monitoring data to the fault alarm module for alarm detection; the fault alarm module sends an alarm signal when a fault or abnormality is detected, and transmits the alarm information to the report generation module for report generation; the report generation module automatically generates regular operation reports and fault analysis reports, and transmits the generated reports to the remote access module for remote viewing; the remote access module allows users to remotely access and monitor the system through the network, and provides remote viewing and management functions for monitoring data and diagnostic results.

[0032] In the above embodiment, the inverter fault monitoring system includes a data acquisition module, a data processing module, a fault diagnosis module and a user interface module. The data acquisition module collects data through the sensor interface module, and after being processed by the data filtering module, it is temporarily stored in the data buffer module, and then transmitted to the data processing module by the data transmission module. The preprocessing module of the data processing module cleans and normalizes the data, the feature extraction module extracts key features, the data analysis module uses algorithms to analyze feature parameters, and the model training module trains the diagnosis model based on historical data and fault cases and transmits it to the fault diagnosis module. The rule diagnosis module of the fault diagnosis module uses rules and thresholds to preliminarily judge the fault, the intelligent diagnosis module identifies complex faults based on machine learning, the fault prediction module predicts the fault in advance through trend analysis, and the fault location module determines the fault location and transmits the result to the user interface module. The monitoring interface module of the user interface module displays the operating status and monitoring data in real time, the fault alarm module issues an alarm when a fault is detected, the report generation module automatically generates an operation report and a fault analysis report, and the remote access module allows users to remotely access and monitor the system through the network, thereby realizing efficient data collection, processing, analysis and fault diagnosis warning.

[0033] Example 2

[0034] like Figure 1-2 As shown, a fault monitoring system for a frequency converter, the intelligent diagnosis module includes an abnormal data detection module for detecting and identifying abnormal points in data in real time using an advanced algorithm, a high-dimensional data dimension reduction module for converting high-dimensional data into low-dimensional data using PCA (principal component analysis) dimension reduction technology, a fault feature selection module for selecting the most representative feature from multiple fault features, an integrated learning algorithm module for applying an integrated learning algorithm to build a more powerful fault diagnosis model, a deep signal processing module for deeply processing signals using a deep learning model, a model adaptive update module for realizing adaptive learning and updating of the model and continuously optimizing the fault diagnosis model according to new data and feedback, an efficient computing framework module for building an efficient computing framework to optimize the use of computing resources, an adaptive threshold determination module for dynamically adjusting the determination threshold of various faults based on real-time data and historical data analysis, and a fault impact assessment module for evaluating the impact of faults on the overall operation of the system, analyzing the severity of the faults and possible chain reactions;

[0035] The abnormal data detection module uses advanced algorithms to detect and identify abnormal points in the data, and transmits the abnormal point data to the high-dimensional data dimension reduction module to reduce the dimension of the data after abnormal detection; the high-dimensional data dimension reduction module reduces the dimension of the data, and transmits the reduced data to the fault feature selection module for feature extraction; the fault feature selection module extracts and selects the most representative fault features, and transmits these feature data to the integrated learning algorithm module; the integrated learning algorithm module applies the integrated learning method to learn and diagnose the feature data, and transmits the preliminary diagnosis results to the deep signal processing module for further deep processing; the deep signal processing module uses the deep learning model to deeply process the signal, and transmits the processed The fault mode and characteristics are fed back to the model adaptive update module; the model adaptive update module continuously optimizes the fault diagnosis model according to the new data and feedback, and transmits the optimized model to the high-efficiency computing framework module for efficient calculation and execution; the high-efficiency computing framework module provides optimized computing resource usage to support the real-time execution of the model, and transmits the diagnosis results to the adaptive threshold judgment module for dynamic threshold adjustment; the adaptive threshold judgment module dynamically adjusts the judgment threshold of various types of faults based on real-time data and historical data analysis, and transmits the judgment results to the fault impact assessment module; the fault impact assessment module evaluates the severity of the fault and possible chain reactions based on the adaptive threshold judgment results, and provides a detailed fault impact analysis report.

[0036] In the above embodiment, first, the abnormal data detection module uses an advanced algorithm to detect and identify abnormal points in the data, and transmits the abnormal point data to the high-dimensional data dimension reduction module for dimension reduction processing. The data after dimension reduction is transmitted to the fault feature selection module, from which the most representative fault features are selected. These feature data are then transmitted to the integrated learning algorithm module to build a more powerful fault diagnosis model, and the preliminary diagnosis results are transmitted to the deep signal processing module for further deep processing. The deep signal processing module uses a deep learning model to deeply process the signal, and feeds back the processed fault mode and features to the model adaptive update module. The model adaptive update module continuously optimizes the fault diagnosis model according to new data and feedback, and transmits the optimized model to the efficient computing framework module for efficient calculation and execution. The efficient computing framework module transmits the diagnosis results to the adaptive threshold determination module for dynamic threshold adjustment. Finally, the determination results of the adaptive threshold determination module are transmitted to the fault impact assessment module to evaluate the impact of the fault on the overall operation of the system, thereby forming a complete intelligent diagnosis process to ensure that the inverter fault monitoring system can efficiently and accurately detect and diagnose faults and ensure the normal operation of the equipment.

[0037] Among them, the historical data acquisition module collects and stores all historical data during the operation of the system to ensure the integrity and availability of the data, and transmits these data to the data trend analysis module for trend and pattern analysis; the data trend analysis module analyzes the trends and patterns in the historical data, identifies common fault precursors and characteristics, extracts valuable information, and transmits the analysis results to the static threshold setting module for threshold setting; the static threshold setting module sets static threshold parameters according to the analysis results provided by the data trend analysis module, and these thresholds are used to preliminarily determine whether there are potential faults, and these set thresholds are used for subsequent determinations.

[0038] Among them, the real-time data acquisition module collects various data during the operation of the system in real time to ensure the timeliness and accuracy of the data, and transmits the collected data to the online learning module for online learning and model updating; the online learning module uses real-time data to perform online learning and model updating, dynamically adjusts the fault diagnosis model to adapt to new data changes and environments, and transmits the learning and updating results to the dynamic threshold adjustment module for threshold adjustment; the dynamic threshold adjustment module adjusts the threshold parameters in real time according to the online learning and model updating results provided by the online learning module, so that it can more sensitively reflect the current system operation status and potential faults, and uses the adjusted threshold for real-time judgment.

[0039] Working principle: When the present invention is in use, the processing module filters and preprocesses the data, and then transmits it to the fault diagnosis module. The fault diagnosis module analyzes the data through rule diagnosis and intelligent diagnosis, predicts possible faults and determines the specific fault location. Finally, the user interface module displays real-time monitoring data and operating status, and generates reports and alarms, allowing remote access and management of the system. The sensor interface module collects data through various sensors, the data filtering module processes the data and temporarily stores it, and the data transmission module transmits the data to the data processing module for further processing. The preprocessing module cleans and normalizes the data, the feature extraction module extracts key features, the data analysis module uses algorithms to analyze feature parameters, the model training module trains models based on historical data and fault cases, and the fault diagnosis module uses rules and thresholds to make fault judgments and transmits preliminary diagnosis results to the intelligent diagnosis module. The intelligent diagnosis module identifies complex or unknown faults based on machine learning or artificial intelligence algorithms, the prediction module uses trend analysis to predict possible faults in advance, the fault location module determines the specific location of the fault, and transmits the final diagnosis results to the user interface module. The user interface module and monitoring interface module display the operation status and monitoring data in real time. The fault alarm module issues an alarm when a fault is detected. The report generation module automatically generates regular operation reports and fault analysis reports. The remote access module allows remote access and monitoring of the system through the network.

[0040] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A fault monitoring system for a frequency converter, characterized in that: It includes a data acquisition module for collecting various necessary data during the operation of the frequency converter, a data processing module for receiving and processing the original data transmitted from the data acquisition module, a fault diagnosis module for diagnosing the operating status of the frequency converter using the analysis results provided by the data processing module, and a user interface module for providing an interface for users to interact with the system, display monitoring and diagnosis results, and provide alarm and report generation functions; The data acquisition module includes a sensor interface module for connecting various sensors to collect data, a data filtering module for filtering the collected data, a data buffer module for temporarily storing the collected data, and a data transmission module for transmitting the collected data to the data processing module for further processing; The data processing module includes a preprocessing module for cleaning and normalizing data, a feature extraction module for extracting key feature parameters from the preprocessed data, a data analysis module for analyzing feature parameters using an algorithm, and a model training module for training a fault diagnosis model based on historical data and fault cases; The fault diagnosis module includes a rule diagnosis module for making fault judgments using predefined rules and thresholds, an intelligent diagnosis module for identifying complex or unknown faults based on machine learning or artificial intelligence algorithms, a fault prediction module for predicting possible faults in advance through trend analysis and prediction models, and a fault location module for determining the specific location and components where the fault occurs; The user interface module includes a monitoring interface module for displaying the operating status and monitoring data of the inverter in real time, a fault alarm module for sending an alarm signal when a fault or abnormality is detected, a report generation module for automatically generating regular operation reports and fault analysis reports, and a remote access module for allowing users to remotely access and monitor the system through the network.

2. A fault monitoring system for a frequency converter according to claim 1, characterized in that: The intelligent diagnosis module includes an abnormal data detection module for using advanced algorithms to detect and identify abnormal points in data in real time, a high-dimensional data dimensionality reduction module for converting high-dimensional data into low-dimensional data using PCA (principal component analysis) dimensionality reduction technology, a fault feature selection module for selecting the most representative features from multiple fault features, an integrated learning algorithm module for applying integrated learning algorithms to build a more powerful fault diagnosis model, a deep signal processing module for using deep learning models to perform deep processing on signals, a model adaptive update module for realizing adaptive learning and updating of models and continuously optimizing the fault diagnosis model according to new data and feedback, an efficient computing framework module for building an efficient computing framework to optimize the use of computing resources, an adaptive threshold determination module for dynamically adjusting the determination thresholds of various types of faults based on real-time data and historical data analysis, and a fault impact assessment module for evaluating the impact of faults on the overall operation of the system and analyzing the severity of the faults and possible chain reactions.

3. A fault monitoring system for a frequency converter according to claim 2, characterized in that: The abnormal data detection module uses advanced algorithms to detect and identify abnormal points in the data, and transmits the abnormal point data to the high-dimensional data dimension reduction module to perform dimension reduction processing on the data after abnormal detection; the high-dimensional data dimension reduction module performs dimension reduction processing on the data, and transmits the reduced data to the fault feature selection module for feature extraction; the fault feature selection module extracts and selects the most representative fault features, and transmits these feature data to the integrated learning algorithm module; the integrated learning algorithm module applies the integrated learning method to learn and diagnose the feature data, and transmits the preliminary diagnosis results to the deep signal processing module for further deep processing; the deep signal processing module uses the deep learning model to perform deep processing on the signal, and feeds back the processed fault mode and features to the model adaptive update module; the model adaptive update module continuously optimizes the fault diagnosis model according to the new data and feedback, and transmits the optimized model to the efficient computing framework module for efficient computing and execution; The efficient computing framework module provides optimized computing resource usage to support real-time execution of the model, and transmits the diagnostic results to the adaptive threshold determination module for dynamic threshold adjustment; the adaptive threshold determination module dynamically adjusts the determination thresholds of various types of faults based on real-time data and historical data analysis, and transmits the determination results to the fault impact assessment module; The fault impact assessment module assesses the severity of the fault and possible chain reactions based on the adaptive threshold determination result, and provides a detailed fault impact analysis report.

4. The fault monitoring system for a frequency converter according to claim 1, characterized in that: The sensor interface module connects various sensors to collect data, and transmits the collected data to the data filtering module for filtering; the data filtering module filters the collected data, and transmits the filtered data to the data buffer module for temporary storage; the data buffer module temporarily stores the filtered data, and transmits the data to the data transmission module for further transmission.

5. The fault monitoring system for a frequency converter according to claim 1, characterized in that: The data transmission module transmits the buffered data to the data processing module for further processing; the preprocessing module cleans and normalizes the data transmitted from the data acquisition module and transmits the preprocessed data to the feature extraction module for feature extraction; the feature extraction module extracts key feature parameters from the preprocessed data and transmits the extracted feature data to the data analysis module for analysis.

6. The inverter fault monitoring system according to claim 1, characterized in that: The data analysis module uses an algorithm to analyze the extracted characteristic parameters, and transmits the analysis results to the model training module for model training; the model training module trains the fault diagnosis model based on historical data and fault cases, and transmits the trained model to the fault diagnosis module for diagnosis; the rule diagnosis module uses predefined rules and thresholds to make fault judgments, and transmits the preliminary diagnosis results to the intelligent diagnosis module for further diagnosis; the intelligent diagnosis module identifies complex or unknown faults based on machine learning or artificial intelligence algorithms, and transmits the identification results to the fault prediction module for fault prediction; The fault prediction module predicts possible faults in advance through trend analysis and prediction models, and transmits the prediction results to the fault location module for fault location.

7. The inverter fault monitoring system according to claim 1, characterized in that: The fault location module determines the specific location and component where the fault occurs, and transmits the final diagnosis result to the user interface module for display and alarm; the monitoring interface module displays the operating status and monitoring data of the inverter in real time, and transmits the monitoring data to the fault alarm module for alarm detection; the fault alarm module sends an alarm signal when a fault or abnormality is detected, and transmits the alarm information to the report generation module for report generation; the report generation module automatically generates regular operation reports and fault analysis reports, and transmits the generated reports to the remote access module for remote viewing; the remote access module allows users to remotely access and monitor the system through the network, and provides remote viewing and management functions for monitoring data and diagnostic results.

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