Pulse Bag Filter Fault Classifier Research and Analysis

Abstract

Aiming at the problem of fault alarm flooding, a fault classifier is established to automatically classify fault types in combination with unsupervised learning methods in machine learning. The k-means method is used to classify the alarm types in dimensionality reduction, and the optimal K value is selected as 11 through various evaluation indexes, and the faults are added to the new category labels according to the center of each cluster after clustering. The random forest and support vector classification algorithm is used to establish a fault classifier model on the fault alarm dataset with new labels, and the unified analysis of the fault type prediction results of the test set unlabeled fault data set is compared with the random forest algorithm, support vector classification, and k-means method. The results show that the unity rate of the prediction results of the three types of classifiers on the test set when taking different K values can reach up to 67%, and the uniformity rate of the prediction results of the three models exceeds 0.998, which has a high degree of uniformity, indicating that the establishment of the fault alarm classifier for the pulse bag dust collector is feasible and has good classification accuracy, which can help maintenance personnel quickly locate and troubleshoot faults, and improve the intelligence level of equipment.