An Integrated Model-Based and Data-Driven Gap Metric Method for Fault Detection and Isolation

This article proposes an integrated approach of model-based and data-driven gap metric fault detection and isolation in a stochastic framework. For actuator and sensor faults, an adaptive Kalman filter combining with the generalized likelihood ratio method is suggested. For component faults, especially incipient faults, the model-based scheme maybe not a good choice due to the existence of disturbances or noises. Hence, a novel data-driven gap metric strategy is presented. The design of the appropriate fault cluster center model and radius via the gap metric technique is put forward to enhance the isolability of the incipient faults. Numerical simulation results are given to demonstrate the effectiveness of the proposed fault detection and isolation algorithm.

I. Introduction

Fault diagnosis technology has become a significant research topic in various industrial systems due to the increasing requirements for higher reliability and safety [1]. In recent years, the main direction in terms of system reliability is concentrated on the large amplitude of faults via the model-based method. In general, the model-based fault detection and isolation (FDI) is an effective tool for an accurate system model, especially the actuator and sensor faults [2]. However, there may exist incipient fault, which is easily covered by modeling error, external disturbances, or measurement noises. It is difficult to guarantee the tradeoff between the robustness against the disturbances and the sensibility for the faults. Hence, the model-based method has certain limitations for incipient fault. Though the degree of a system deviation from its normal state is relatively inconspicuous when an incipient fault occurs at the early stage, it may still pose a significant security risk to the system over time. If the incipient faults can be accurately detected and isolated earlier by an FDI technique, the system will be regularly maintained and overhauled. By doing so, people can reduce or avoid the occurrence of catastrophic faults. Therefore, a data-driven gap metric FDI (DDGMFDI) method is expected to detect and isolate incipient faults.