Unsupervised Deep Tensor Multitask Anomaly Detection With Rule Adaptation for Online Early Fault Evaluation

Abstract

This paper tries to solve the problem of online early fault detection (EFD) with unlabeled streaming data by addressing the following challenges: 1) no state information is available for online data; 2) online working condition is not determined in advance; 3) False alarm should be avoided. This paper first proposes a deep tensor multi-task anomaly detection model (Tensor-MAD) with rule adaptation for the online EFD. Running on multi-task learning architecture, Tensor-MAD builds a new tensorized pooling filter to keep the essential information of each task’s operating status from noisy data. With hypersphere-based one-class detection rule representation, Tensor-MAD further constructs a new rule adaptation mechanism to transfer the detection rule from offline labeled data to the online unlabeled data. A training algorithm with an alternating minimization scheme is also provided to update tensor decomposition and rule adaptation. Then the optimal information filter level and the rule adaptation degree can be determined. Based on the obtained anomalies, this paper proposes a non-parametric alarm threshold setting method based on the sequential accumulation of anomaly probability. This threshold can be adaptively chosen once an expected false alarm rate is given. A rationality proof is also provided. Experimental results on the IEEE PHM Challenge 2012 bearing dataset demonstrate that the proposed approach can adaptively and accurately evaluate the early fault occurrence from unlabeled streaming data. More importantly, the proposed approach has a much lower false alarm rate and a faster convergence speed, providing an easy-to-deploy and reliable solution for online EFD.