Identification based fault detection: Residual selection and optimal filter. (September 2021) - Zhou, Jinming; Zhu, Yucai
Abstract: In this work, an identification based fault detection method is proposed. The idea is to identify a dynamic process model from test data and to generate residuals using the identified model for fault detection. The method intends to improve fault detection performance while taking disturbance and model error into account. To this end, a fault detection performance index is introduced in a statistical framework. Then it is shown that the output error residual is more suitable for fault detection than the prediction error residual. Further an optimal detection filter maximizing the performance index is developed. Practical issues for implementing the detection filter are also addressed. Finally, the proposed method is illustrated through a numerical example and Tennessee Eastman process. Highlights: Proposing an identification based fault detection method. Demonstrating output error residual is more suitable than the prediction error residual. Developing optimal detection filters to enhance the detection performance. Comprehensive case studies including method illustrations and comparison with other methods..