Fault detection and isolation for discrete-time Markovian jump systems with generally bounded transition probabilities: A zonotope-based method

Abstract

This paper addresses the fault detection and isolation for a class of discrete-time Markovian jump system with generally bounded transition probabilities. The proposed method is mainly based on a bank of [Formula: see text] robust observers, which can detect the actuator fault occurrence and, even more, can locate those subjected to the fault. Furthermore, the performance and the robustness of the designed [Formula: see text] robust observer are disseminated respect to disturbances and measurement noises. In particular, we propose the zonotop-based dynamic threshold to predict the ranges of the residual, which shows less conservatism in comparison to traditional methods. Finally, pertinent simulations are performed to corroborate the potential and superiority of the proposed method. In general, the proposed method offers effective fault detection and isolation scheme for Markovian jump systems, which can detect the fault timely and accurately with lowering the rate of fault missing report to a great extent, thus having practical value.

Feature extraction and fault detection scheme via improved locality preserving projection and SVDD

“…Dimensionality reduction has become a fundamental premise for most data-driven approaches due to the intrinsic features of process data, which are non-linearity, high-dimension, and complicated connection among process variables (Yin and Yan, 2021). Dimensionality reduction aims to condense high-dimensional data into a relatively small set of variables, while retaining the original data structure (Lu et al, 2021).…”