Incipient Fault Detection, Diagnosis, and Prognosis using Canonical Variate Dissimilarity Analysis

Abstract

Abstract Industrial process monitoring deals with three main activities, namely, fault detection, fault diagnosis, and fault prognosis. Respectively, these activities seek to answer three questions: ‘Has a fault occurred?’, ‘Where did it occur and how large?’, and ‘How will it progress in the future?’ As opposed to abrupt faults, incipient faults are those that slowly develop in time, leading ultimately to process failure or an emergency situation. A recently developed multivariate statistical tool for early detection of incipient faults under varying operating conditions is the Canonical Variate Dissimilarity Analysis (CVDA). In CVDA, a dissimilarity-based statistical index was derived to improve the detection sensitivity upon the traditional canonical variate analysis (CVA) indices. This study aims to extend the CVDA detection framework towards diagnosis and prognosis of process conditions. For diagnosis, contribution maps are used to convey the magnitude and location of the incipient fault effects, as well as their evolution in time. For prognosis, CVA state-space prediction and Kalman filtering during faulty conditions are proposed in this work. By covering the three main process monitoring activities in one framework, our work can serve as a baseline strategy for future application to large process industries.