Method and monitoring system for detecting and separating micro fault in industrial process

Abstract

The invention discloses a method and monitoring system for detecting and separating a micro fault in an industrial process. The method comprises steps of: acquiring and using sensor data in a normal condition of the industrial process as training data and establishing a principal component analysis model of the training data; giving an appropriate sliding time window width and computing an improvement reconstruction contribution value of each variable of each sample in the training data; determining the control limit of the improvement reconstruction contribution of each variable; acquiring and using sensor data in a real-time condition as test data; computing the improvement reconstruction contribution of each variable in the test data and comparing the improvement reconstruction contribution with the corresponding control limit in order to analyze the fault of the test data; and if a fault analysis result indicates that a fault happens, identifying the variable with a maximum improvement reconstruction contribution value as a fault variable in order to achieve fault separation. Compared with a method in the prior art, the method does not require a mathematic model of the industrial process and may detect and separate the micro fault in the industrial process.

Description

technical field [0001] The invention belongs to the field of industrial process monitoring and fault diagnosis, and in particular relates to a method for detecting and separating minor faults in industrial process and a monitoring system thereof. Background technique [0002] Modern industrial processes are large in scale and complex in structure. Once the process is abnormal, it may cause huge economic losses and even endanger personal safety. Process monitoring and fault diagnosis technology can effectively improve system reliability, equipment maintainability and reduce accident risk, and has become one of the research hotspots in the field of process control. In addition, more serious faults are usually evolved from small faults, and many major catastrophic accidents in history were also caused by small anomalies in the system that were not detected and resolved in time. Therefore, the harm of minor faults cannot be ignored, timely detection and separation of minor faul...

AI Technical Summary

Problems solved by technology

However, the traditional principal component analysis method has poor detection performance for small faults, resulting in a high rate of fault false positives (low detection rate)

In addition, the traditional reconstruction contribution graph method is also prone to mislocalization of fault variables when dealing with the problem of micr

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