Industrial equipment abnormality detection method based on fuzzy set

Abstract

The invention relates to the technical field of industrial equipment abnormality detection, and provides an industrial equipment abnormality detection method based on a fuzzy set. The method comprises: firstly, using an abnormal knowledge tree for constructing an abnormal detection model of industrial equipment; secondly, configuring an attribute set, an attribute data flow, a time window size, anattribute membership function and an aggregation function according to user requirements, and obtaining an abnormity degree of the leaf node; then, according to the Pearson correlation coefficient between the attributes, clustering the attributes, and calculating the weights of the leaf nodes; secondly, aggregating leaf nodes involved in the class cluster into non-leaf nodes, and aggregating thenon-leaf nodes into root nodes; after a user selects model parameters according to requirements, establishing a topological structure of flow processing of the abnormal detection model based on a Storm real-time computing system, and visualizing abnormal degree results of the industrial equipment in different time windows. According to the mehtod, the abnormity of the industrial equipment can be detected in real time, and the abnormity detection of data with different granularities can be realized.

Description

technical field [0001] The invention relates to the technical field of industrial equipment anomaly detection, in particular to a fuzzy set-based industrial equipment anomaly detection method. Background technique [0002] With the rapid development of the information society, the scale of data has begun to show exponential growth, and how to mine valuable information from massive data has become a research hotspot. Especially in the fields of network intrusion detection, financial risk analysis, industrial control management, sensor network, etc., real-time, fast, sequential, and continuous large-scale data will be generated, and data mining methods on traditional static data sets are not suitable. Real-time data flow, at the same time, because massive data will be generated in real time and the memory of the computer is limited, it is impossible to load all the data into the memory, and by setting the size of the sliding window, part of the data flow can be intercepted and...

AI Technical Summary

Problems solved by technology

Therefore, the existing anomaly detection methods cannot effectively detect the anomaly of the overall industrial equipment from the data stream with various and complex data attributes in the industrial production process, and cannot detect the anomaly of different granularity data

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