Intelligent Bearing Condition Monitoring for Electrical Machines Through Vibration Signal Based on Autoencoder and Almost-Parameter-Free Classifiers

Feature extraction is a critical task in the design of intelligent condition monitoring (CM) systems. In most cases, expert knowledge is required. This study investigates feature extraction for bearing condition monitoring using artificial intelligence (AI). A hierarchical extreme learning machine (HELM) is used to design a condition monitoring system (CMS) for bearings that can be used for fault classification and anomaly detection. Acceleration signals are processed using an autoencoder to extract features and anomalies were detected or faults were classified depending on the task of the CMS. Almost-parameter-free machine learning-based classifiers have been used to solve the classification task. In general, the study employed a (minimum)-distance classifier (MiDC), and one-class k-nearest neighbors (OC-k-NN). This was done to show that deep learning-based feature extraction provides excellent spatial separation and facilitates classification with minimal training effort. The study compared the distance based classifiers with the performance of several artificial intelligence-based approaches, such as support vector machine (SVM) one-class support vector machine (OC-SVM), isolation forests (IF), and multi-layer perceptron (MLP). The proposed method was evaluated using both the CWRU dataset and an own dataset. The proposed method achieved F1-scores above 0.98 for both anomaly detection and fault classification with the CWRU dataset and an own daset.