Outlier Detection for Control Process Data Based on Improved ARHMM

In view of the difficulty of accurate online detection for massive data collecting real-timely in a strong noise environment during control process, an order self-learning Autoregressive Hidden Markov Model (ARHMM) algorithm is proposed to carry out online outlier detection in industrial control process. The algorithm utilizes AR model to fit the time series and makes use of HMM as basic detection tool, which can avoid the deficiency of presetting the threshold in traditional detection methods. In order to update parameters of ARHMM online, the structure of traditional Brockwell–Dahlhaus–Trindade (BDT) algorithm is improved to be a double-iterative structure in which iterative calculation from both time and order is applied respectively. With the purpose of reducing the influence of outlier on parameter update of ARHMM, the strategies of detection-before-update and detection-based-update are adopted, which also improve the robustness of algorithm. Subsequent simulation by model data and practical application verify the accuracy, robustness and property of online detection of the algorithm. According to the result, it is obvious that new algorithm proposed in this paper is more suitable for outlier detection of control process data in process industry.