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.