Abstract
Fault detection and isolation (FDI) is considered as one of the most critical problems in biological processes. Therefore, in this paper, we consider a new FDI framework that aims to improve the monitoring of biological processes. To do that, a machine learning-based statistical hypothesis approach, which can identify the model, detect and isolate the faults, will be developed. In the developed approach, so-called partial Gaussian process regression (PGPR)-based generalized likelihood ratio test (GLRT), first, the GPR model that can accurately model biological processes is presented. Then, the fault detection phase is performed using the GLRT chart. Finally, the PGPR-based GLRT, which can effectively isolate the faults, is developed. The FDI performances of the developed PGPR-based GLRT approach are compared with partial support vector regression (SVR), extreme learning machines (ELM), Kernel ridge regression (KRR) and relevance vector machines (RVM)-based GLRT methods in terms of missed detection rate (MDR), false alarm rate (FAR), root mean square error (RMSE), execution time (ET) and isolation accuracy. The obtained results show that the proposed technique can reliably detect and isolate various faults using two examples: a synthetic data and a biological process representing a Cad System in
E. coli
(CSEC) model.