Equipment fault warning and state monitoring method

Abstract

The invention relates to the technical field of equipment fault monitoring, and particularly discloses an equipment fault warning and state monitoring method. The equipment fault warning and state monitoring method is characterized by including two processes of model building and model operation; the process of model building includes the steps of acquiring training data, performing data preprocessing operation on the training data, then adopting a nonparametric learning algorithm to select a memory matrix, training a residual generator and acquiring a residual threshold of each parameter; the process of model operation includes the steps of acquiring real-time data, performing data preprocessing operation on the real-time data, then calculating all parameter residuals of the real-time data, analyzing the residuals to judge whether the equipment state is normal or not, and further positioning fault causes. The method has the advantages of data-driven method based universality, robustness and high adaptive capability, the shortcoming that warning results are difficult to analyze and explain is avoided, and additionally accuracy and efficiency of fault warning are both improved due to introduction of the nonparametric learning algorithm.

Description

(1) Technical field [0001] The invention relates to the technical field of equipment failure monitoring, in particular to an equipment failure early warning and state monitoring method. (2) Background technology [0002] Equipment failure early warning and status monitoring According to equipment operation rules or observed possible precursors, before the equipment actually fails, timely forecast the abnormal condition of the equipment and take corresponding measures to minimize the loss caused by equipment failure. With the increasing scale and complexity of equipment and engineering control systems, in order to ensure the safety and stability of the production process, it is particularly urgent and important to monitor and diagnose process abnormalities in a timely and effective manner through reliable state monitoring technology. [0003] Existing equipment failure early warning technologies are mainly divided into three categories: method based on mechanism model, me...

AI Technical Summary

Problems solved by technology

However, it should be noted that the early warning accuracy of this type of method has a strong dependence on the richness of expert knowledge in the knowledge base and the level of expert knowledge; at the same time, it is difficult for some experts to use a rea

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