Model-free fault detection and isolation of a benchmark process control system based on multiple classifiers techniques—A comparative study. (April 2018) - Nozari, Hasan Abbasi; Nazeri, Sina; Banadaki, Hamed Dehghan; Castaldi, Paolo
Abstract: This paper presents a combined data-driven framework for fault detection and isolation (FDI) based on the ensemble of diverse classification schemes. The proposed FDI scheme is configured in series and parallel forms in the sense that in series form the decision on the occurrence of fault is made in FD module, and subsequently, the FI module coupled to the FD module will be activated for fault indication purposes. On the other hand, in parallel form a single module is employed for FDI purposes, simultaneously. In other words, two separate multiple-classifiers schemes are presented by using fourteen various statistical and non-statistical classification schemes. Furthermore, in this study, a novel ensemble classification scheme namely blended learning (BL) is proposed for the first time where single and boosted classifiers are blended as the local classifiers in order to enrich the classification performance. Single-classifier schemes are also exploited in FDI modules along with the ensemble-classifier methods for comparison purposes. In order to show the performance of proposed FDI method, it was also tested and validated on DAMADICS actuator system benchmark. Besides, comparative study with the related works done on this benchmark is provided to show the pros and cons of the proposed FDI method. Highlights: A blended learning approach toward model-free FDI process actuator systems is proposed using multiple classifiers. Two separate model-free FDI frameworks based on series and parallel configurations are presented. The proposed FDI method can cope with complex process plants with large numbers of actuators and process variables such as DAMADICS application. A thorough comparative study between single and multiple classification schemes is carried out. The proposed FDI methods can accurately detect and isolate all possible faulty scenarios.