Semi-supervised Fault Diagnosis Model Based on Improved Fuzzy C-means Clustering and Convolutional Neural Network

Abstract

The number of unlabeled data is much more than labeled data. There are some challenges in using existing complex data for fault diagnosis. Therefore, we proposed a semi-supervised fault diagnosis algorithm based on improved fuzzy C-means algorithm (IFCM) and Convolutional Neural Network (CNN). Firstly, the fault original signal is extracted by variational mode decomposition (VMD) and the singular value decomposition (SVD) to extract the fault feature vector. Secondly, IFCM is used to obtain the membership degree of the data to all categories, which integrates the labeled data into the cluster center of fuzzy C-means (FCM) to obtain better clustering results. The label of labeled data will spread to unlabeled data in the same category. Thirdly, data with a membership degree greater than the value of 0.9 will be selected as the core data set. Finally, the original signal and labels of core data set will be input into CNN for fault diagnosis. The result shows that the fault recognition rate of IFCM-CNN reaches 95%. The fault diagnosis effect is significantly better than other algorithms.

Exploring Fuzzy Local Spatial Information Algorithms for Remote Sensing Image Classification

“…Although the MFCCM algorithm utilized the significant features extracted from convolutional filtering to generate promising fuzzy clustering results in noisy environments, the algorithm exhibited limitations in terms of time complexity. Wang et al [39] proposed a semi-supervised approach combining an improved FCM algorithm (IFCM), which effectively utilized labeled data integrated into traditional FCM for clustering, and CNN for fault detection in the sparsity of labeled data. The CNN model in this method, trained using the original data corresponding to the output labeled data from IFCM, was further used for fault detection.…”