A semi-supervised Support Vector Data Description-based fault detection method for rolling element bearings based on cyclic spectral analysis

Abstract

The early and accurate detection of rolling element bearing faults, which is closely linked to the timely maintenance and repair before a sudden breakdown, is still one of the key challenges in the area of condition monitoring. Nowadays in the frames of research advanced signal processing techniques are combined with high level machine learning approaches, focusing towards automatic fault diagnosis and decision making. A plethora of Health Indicators (HIs) have been proposed to feed in machine learning models in orderto track the system degradation. Cyclic Spectral Analysis (CSA), including Cyclic Spectral Correlation (CSC) and Cyclic Spectral Coherence (CSCoh), has been proved as a powerful tool for rotating machinery fault detection. Due to the periodic mechanism of bearing fault impacts, the HIs extracted from the Cyclostationary (CS) domain can detect bearing defects even in premature stage. On the other hand, supervised machine learning approaches with labelled training and testing datasets cannot be yet realistically applied in industrial applications. In order to overcome this limitation, a novel semi-supervised Support Vector Data Description (SVDD) with negative samples (NSVDD) fault detection approach is proposed in this paper. The NSVDD model utilizes CS indicators to build the feature space, and fits a hyper-sphere to calculate the Euclidean distances in order to isolate the healthy and faulty data. An uniform object generation method is adopted to generate artificial outliers as negative samples for the NSVDD. A systematic fault detection decision strategy is proposed to estimate the bearing status simultaneously with the detection of fault initiation. Furthermore, a multi-level anomaly detection framework is built based on data at i) single sensor level, ii) machine level and iii) entire machine fleet level. Three run-to-failure bearing datasets including signals from twelve bearings are used to implement the proposed fault detection methodology. Results show that, the CS based indicators outperform time indicators and Fast Kurtogram (FK) based Squared Envelope Spectrum (SES) indicators. Moreover, the proposed NSVDD model show superior characteristics in anomaly detection compared to three classification methodologies, i.e. the Back-Propagation Neural Network, the Random Forest and the K-Nearest Neighbour.