Normalized Relative RBC-Based Minimum Risk Bayesian Decision Approach for Fault Diagnosis of Industrial Process

In the industrial process, it is helpful to take the previous fault diagnosis results into consideration during the current determination of faulty variables. In this paper, an unsupervised data-driven fault diagnosis method based on normalized relative reconstruction-based contribution (RBC) and the minimum risk Bayesian (MRB) decision theory is presented. Normalized relative RBC is used to represent the characteristic of the observation of the samples, and beta distribution is adopted to approximate the probability density distribution of the variable being faulty or normal. On the basis of the adjustable loss function, the conditional risk is obtained. The fault diagnosis method based on MRB decision is proposed, which will reduce the influence of smearing effect, improve the diagnosis rate, handle faults with a small magnitude, and identify multiple process faults. Numerical simulation examples and the Tennessee Eastman process are given to show the effectiveness and superiority of the proposed method.