Hybrid Methods for Detection and Identification of Faults in Dynamic Systems

Abstract

Since the 1960s, when automation became essential to productivity, methods for the detection and identification of faults have been proposed. Physical systems are diversified and can be mechanical, electrical, pneumatic, electronic, or a combination of these. In addition, real plants have a large number of these devices, which are for its own operation, sensoring or control. Therefore the solutions given for detection of faults are generally very specific or particular. This paper aims to describe and analyze two hybrid methods of detection and fault identification based on residue and to check whether their inclusion with other methods, combining different techniques, can produce a better fault detection and identification system. The methods use the state observers for the generation of residues, which serve for the detection and identification and the set called the bank of signatures to identify the faults. Thereafter, the methods use different approaches to diagnose the fault: the first uses the approach of the mean square error, and the second uses a decision tree.

Robust Fault Diagnosis for Discrete-Time Switched System with Unknown State Delays Subject to Component Faults

“…where ( ) Thus, the uniform ultimate boundedness of system state estimation error and fault reconstruction error are guaranteed and output estimation error satisfies Part C: Proof for Theorem 3: Here we give the proof that LMI (12) is equivalent to the formula (15). Similarly, LMI (13) can be transformed into the inequality (16).…”

“…Remark 2: In Part C of the appendix, the strict proof of Theorem 3 is shown. It is also the necessary condition proven in [1,15]. The introduction of the auxiliary slack matrix i G can couple the product term between j P and ( )…”

“…Faults may occur at any time, and then destroy the system stability or even make the system tend to collapse. Nowadays, fruitful model-based fault diagnosis works have been published in several excellent papers and books [3,5,14,15,17,23,26]. Among these approaches, the most commonly used one is first to design the observers or filters to reconstruct the fault signal or residual signal, and then generate an alarm when the residual function is larger than the threshold or the fault reconstruction signal is not zero [18].…”

Static output feedback control design for constrained linear discrete‐time systems using data cluster analysis

“…In this example, we apply the proposed approach to a practical simulated example that represents the characteristics of a coupled tanks process as illustrated in Fig. 3 [42]. The output liquid flow of Tank1 connects to the input of Tank2.…”

Application of Exergy-Based Fault Detection in a Gas-To-Liquids Process Plant

“…A typical example of a hybrid FDI is the combination of statistical and machine learning techniques [ 17 ]. Combinations of signal- and observer-based techniques have also proven to be successful when applied to a classical two-tank system [ 18 ]. As noted in [ 16 ], a simple combination of techniques will not necessarily provide superior results.…”