Abstract
This paper investigates the problem of fault detection, isolation, and estimation for a networked system with actuator and sensor faults. To deal with the bandwidth constraint, an event-triggered scheduling mechanism is utilized to determine whether the sensor observation shall be transmitted to the fault filter according to the importance of information. In this study, two independent Markovian jump chains are introduced to describe the temporal occurrence of sensor fault and the random switching between the normal condition and the faulty ones of the actuator, respectively. To alleviate the compromise between the model number of fault models and computational complexity in the existing interacting multiple models (IMM) approaches, a novel event-triggered fault detection and diagnosis algorithm is proposed based on the augmented IMM framework, where the fault location to be detected is added into the model set and the fault amplitude to be estimated is augmented into the system state. Finally, a Monte Carlo simulation involving tracking a two-dimension moving target is provided to illustrate the effectiveness and efficiency of the proposed method.
Highlights
Over recent years, with the development of technology and science, modern engineering systems are faced with huger investment, larger scale, more sophisticated structure and more complex function [1]–[3]
It is clear that fault detection and diagnosis (FDD)
Fault detection is to determine whether the faults happen; Fault isolation is concentrated on pinpointing at the component where the faults are located; Fault identification is to estimate the size or severity of fault and the time of its occurrence in some cases
Summary
With the development of technology and science, modern engineering systems are faced with huger investment, larger scale, more sophisticated structure and more complex function [1]–[3]. Z. Jin et al.: Event-Triggered FDD for Networked Systems With Sensor and Actuator Faults linear time-variant systems, where the sufficient condition of the existence of a robust fault detection filter was presented via linear matrix inequality techniques. A novel fault detection, isolation and estimation method for multi-sensor systems was proposed in [16] based on the augmented IMM structure and the strong tracking filtering approach, where the unknown fault amplitude was directly augmented into the system state to avoid the dilemma of predetermining the fault extent as the model parameters in the traditional IMM approaches. The event-triggered FDD problem is studied for networked control systems with actuator and sensor faults. Where Ik−1 is the history sequence received at the estimator center up to time k − 1
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