Equipment operation state judgment method based on fuzzy clustering optimal k value selection algorithm

Abstract

Equipment operation state judgment method based on the fuzzy clustering optimal k value selection algorithm comprises the steps of collecting test data according to the operation condition of equipment to be tested, and preprocessing the data; establishing a double-target model according to the processed test data; using a CDG optimization algorithm to carry out optimal solution on the double-target model; converting the result after optimization solution by using a DB index, and calculating to obtain an optimal clustering number k; according to the obtained optimal clustering number k, analyzing the preprocessed test data by using a fuzzy clustering algorithm FCM, and dividing the test data into k clusters; and counting the data center of each cluster, the characteristics of the data in each cluster and the range included by each cluster, and judging the running state of the current equipment according to the characteristic conditions of the clusters. According to the method, the result error caused by determining the optimal clustering number k in the clustering algorithm is reduced, and the method can be more accurately used for judging the running state of the equipment.

Description

technical field [0001] The invention belongs to the field of data mining, and in particular relates to a method for judging the operating state of equipment based on a fuzzy clustering optimal k value selection algorithm. Background technique [0002] With the rapid development of modern industry and science and technology, the structure of industrial equipment is becoming more and more complex. In order to effectively avoid equipment failure, it is necessary to monitor the operating status of the equipment in real time. However, due to the complexity of the equipment, there are many parameters involved in judging its operating status, and the traditional monitoring method is inefficient. It is necessary to use a suitable algorithm for effective classification and judgment. [0003] With the development of Internet technology, cluster analysis plays an important role in many fields. Clustering is an unsupervised learning method, which can actively group data points, so that...

AI Technical Summary

Problems solved by technology

However, these metrics tend to decrease monotonically as the number of clusters tends to the number of data points in the data points, and computing validity metrics requires providing the correct cluster centers

Images