Outlier detection based on Gaussian process with application to industrial processes

Due to the extensive usage of data-based techniques in industrial processes, detecting outliers for industrial process data become increasingly indispensable. This paper proposes an outlier detection scheme that can be directly used for either process monitoring or process control. Based on traditional Gaussian process regression, we develop several detection algorithms, of which the mean function, covariance function, likelihood function and inference method are specially devised. Compared with traditional detection methods, the proposed scheme has less postulation and is more suitable for modern industrial processes. The effectiveness of the proposed scheme is verified by experiments on both synthetic and real-life data sets. (C) 2019 Elsevier B.V. All rights reserved.