Bootstrap Gaussian Process Classifiers for Rotating Machinery Anomaly Detection

Rotating machinery anomaly detection is of paramount significance for industries to prevent catastrophic breakdown and improve productivity and personnel safety. The kernel classifier support vector machine (SVM) has shown excellent performance towards this purpose, but it is difficult to optimize relevant hyper-parameters. In this paper, we propose a new anomaly detection approach by merging Gaussian process classifiers (GPCs) and bootstrap methods. GPCs are Bayesian probabilistic kernel classifiers and provide a well established Bayesian framework to determine the optimal or near optimal kernel hyper-parameters. They are largely unexplored for anomaly detection applications; consequently we take the initiatives to investigate GPCs' performance in these scenarios. Bootstrap methods are incorporated to improve GPCs' performance for small machinery anomaly samples by resampling at random. The proposed approach is evaluated on a motor testbed and wavelet packet is utilized to perform vibration analysis. Experiment results show bootstrap GPCs are highly effective and outperform GPCs and SVM with cross validation for anomaly detection. Moreover GPCs also prove to outperform SVM. Thus the proposed approach is promising for rotating machinery anomaly detection.