Industrial big data early failure detection method based on image semi-supervising cost-sensitive

Abstract

The invention provides an industrial big data early failure detection method based on image semi-supervising cost-sensitive, and relates to the technical field of failure detection and diagnosis. Theindustrial big data early failure detection method based on image semi-supervising cost-sensitive includes the steps: acquiring the data of the industrial process, and utilizing an image semi-supervising tag propagation method to update tags without marked data and delineating the suspected early failure; for the suspected early failure points, performing cost-sensitive Bayesian classification, and completing secondary updating of the tags of the suspected early failure points; and finally establishing an EDC-SVM (Example dependent cost-sensitive support vector machine) classifier, and performing on-line failure diagnosis on the industrial process. The industrial big data early failure detection method based on image semi-supervising cost-sensitive takes minimization of the diagnosis costas the failure diagnosis target, divides the suspected early failure, and solves the problem that the industrial failure detection error classification cost is high. And at the same time, on the premise of guaranteeing accuracy of classification, the industrial big data early failure detection method based on image semi-supervising cost-sensitive can reduce the misjudgment cost during the failuredetection process, and can improve safety of the industrial process.

Description

technical field [0001] The invention relates to the technical field of fault detection and diagnosis, in particular to a graph-based semi-supervised cost-sensitive early fault detection method for industrial big data. Background technique [0002] With the rapid development of modern industry, the production equipment in modern enterprises is increasingly large-scale, continuous, high-speed and automatic. The structure and composition of equipment are very complicated, the production scale is very large, and the connection between various departments is also particularly close. The actual production process is linear, nonlinear, time-invariant, time-varying, etc. For the characteristics of different production processes, different fault monitoring methods should be selected, so as to effectively detect faults. [0003] Traditional classification algorithms usually aim at minimizing the global classification misclassification rate, and assume that the misclassification costs ...

AI Technical Summary

Problems solved by technology

Due to the different degrees of harm, the cost of misdiagnosis of equipment is not equal, and the cost of safety hazards and economic losses required to misdiagnose the fault state as a normal state is often greater than the cost of the opposite situation.

In addition, since the acquisition of fault samples is at the cost of a certain degree of equipment damage, the number of fault samples will be much less than that of normal samples. The conclusion of the diagnostic method is more inclined to the judgment of the normal state, and cannot effectively avoid the loss caused by the fault

Therefore, the goal of minimizing the misclassification rate cannot meet the actual fault diagnosis requirements.

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