A Novel Two-Stage Unsupervised Fault Recognition Framework Combining Feature Extraction and Fuzzy Clustering for Collaborative AIoT

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 2; pp. 1291 - 1300
• Main Authors: Hu, Xufeng, Li, Yibin, Jia, Lei, Qiu, Meikang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive weighted Gath–Geva clustering (AWGG); Artificial intelligence; artificial Intelligence of Things (AIoTs); Big Data; Cluster analysis; Clustering; Clustering algorithms; Collaboration; Data mining; Fault diagnosis; fault recognition; Feature extraction; Feature recognition; Indexes; industrial big data; Internet of Things; Labor; Personnel; stacked sparse autoencoder (SSAE); Unsupervised learning
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2021.3076077

Currently, with the development of the Internet of Things (IoTs) and artificial intelligence, a new IoT structure known as the artificial Intelligence of Things (AIoTs) comes into play. With the development of AIoT, a large amount of unlabeled industrial big data has been accumulated. The analysis of large amounts of unlabeled data is labor-intensive and time-consuming for diagnostic personnel. To improve this situation, a novel two-stage unsupervised fault recognition algorithm, namely, deep adaptive fuzzy clustering algorithm (DAFC) is proposed for unsupervised fault clustering in this article. DAFC amalgamates stacked sparse autoencoder (SSAE) into adaptive weighted Gath-Geva (AWGG) clustering to form an unsupervised fault recognition framework for clustering analysis of unlabeled industrial big data. SSAE can extract the highly abstract features of the original data, and adopt different unsupervised strategies to fine-tune the network in two stages. AWGG is an improvement of Gath-Geva clustering, and can adaptively obtain optimal clustering results without presetting the number of clusters. Experimental results on two different datasets show that the proposed DAFC can stably extract fault features from unlabeled data, and automatically obtain the optimal clustering results without knowing the number of clusters in advance. To the best of our knowledge, this article is the first attempt to fine-tune SSAE in an unsupervised manner, and to propose an unsupervised fault recognition framework that requires no prior knowledge or data labels at all. DAFC can be a feasible industrial big data application for collaborative AIoT. Diagnostic personnel analyze the clustering results obtained by DAFC instead of the original unlabeled data, greatly saving time and labor costs.