Fault Diagnosis in Steady-State Process Systems

The different machine learning paradigms reviewed in the previous chapters are considered in depth in the context of fault diagnosis in steady-state process systems. Data-driven process fault diagnosis consists of two stages, an offline training stage and an online application stage, and both of these are revisited before considering a number of case studies. In the offline training stage, consideration is given to practical issues, such as the selection of the number of features used to represent normal operating conditions, the derivation of control limits in the feature space, where the distribution of the data is generally unknown, as well as various performance metrics, such as alarm rates, alarm run lengths, detection delays and receiver operating characteristic curves. These issues are subsequently illustrated by simulations, including that of a simple nonlinear system, the benchmark Tennessee Eastman system widely investigated in the process engineering literature and a sugar refinery. In these case studies, the performance of fault diagnostic models representative of the major classes of machine learning models, as well as principal component analysis, is considered. Moreover, a variety of performance measures, each with their different strengths and weaknesses, are discussed in depth.