Bayesian-based anomaly detection in the industrial processes

In general, the industrial processes are semi-automatic, and are controlled by the operators. Since the operation principles of the industrial processes are complicated, it is difficult to label observations. The disturbances may be contained in the observations. Therefore, the unsupervised anomaly detection method is promising for research in the industrial processes. In the paper, a multivariate anomaly detection method is proposed, which is unsupervised and online. The priori probability of anomaly occurrence is necessary, and a hazard function selection method is defined at first. Secondly, Bayesian-based method is adopted for anomaly detection. In final, the Dempster-Shafer theory is introduced for fusing the univariate anomaly detection results. The numerical simulation is used for illustrating the anomaly detection power of the proposed method, and the TE process is implemented for testing the fault detection effectiveness. A real data set collected from a bathyscaphe is applied for demonstrating the power of leakage detection