Design Of Fault Detection System Research Articles

Integrated design of fault detection systems in time-frequency domain

Problems related to the integrated design of robust fault detection (FD) systems are studied. First, it is revealed that due to the time window introduced to realize the 2-norm based evaluation function, an optimal design of a FD system with the 2-norm based evaluation function may not ensure the expected optimal performance when the system is realized in real applications. To solve this problem, an integrated design method of FD systems using the absolute value of residual signal as evaluation function is then proposed. It leads to a residual generator which is much easier to be realized. Different from the usual 2-norm based approaches whose mathematical basis is the relationship between the energy of the output and input signals of a dynamic system, a relationship between the instant power of the output signal and the energy of the past input signal of a dynamic system is established and further used for FD system design. Another new kind of evaluation function based on the absolute value of wavelet transform of residual signal and the corresponding integrated design approach for FD systems are further proposed.

Simulink Blockset for Fault Detection Using Interval Models

This paper describes a tool to aid in the analysis and design of fault detection systems for processes which present bounded structured uncertainty (only bounds for the values of their physical parameters are known). This uncertainty is considered in the fault detection methodology by using interval models to represent the processes. The tool has been implemented using the well-known Matlab/Simulink CACSD framework. This framework is very useful in the analysis and design of control systems and can also be used in the analysis and design of supervisory systems (which must include fault detection capabilities).

Robust Design of a Fault Diagnostic System for a Supersonic Wind Tunnel

By using the unknown input decoupling technique, this paper presents a robust fault detection and isolation (FDI) system design for a supersonic wind tunnel. A decoupled fault/residual map is designed in the sensor FDI system to achieve multiple fault isolation for principal sensors. Since the relations between actuator faults and the generated residuals are insignificant, a decoupled fault/residual map for actuator is not achievable. Nevertheless, based on the fault specific directions, single fault isolation can still be achieved for actuators with the proposed FDI system.

Robust fault detection of dynamic systems via genetic algorithms

This paper presents a new approach to the design of robust fault detection systems via a genetic algorithm. To achieve robustness, a number of performance indices are introduced, which are expressed in the frequency domain to account for the frequency distributions of incipient faults, noise and modelling uncertainty. All objectives are then reformulated into a set of inequality constraints on performance indices. A genetic algorithm is thus used to search an optimal solution to satisfy these inequality constraints. The approach developed is applied to a flight control system example and results show that incipient sensor faults can be detected reliably in the presence of modelling uncertainty.

Concept of the simulation environment for prototyping design of fault detection and diagnostic systems

In recent years a number of new methods for fault detection and diagnosis (FDD) have been proposed in the literature. However, for a practitioner wishing to solve a concrete FDD problem it is often not easy to choose the right method for the problem at hand. Since the FDD design is normally iterative, use of CAD support is of great practical interest in order to make the design more efficient. In this paper we deal with the preliminary FDD design process referred to as prototyping in which simulation plays an important role. First, the main stages of the prototyping design cycle are identified. Accordingly, a concept is proposed for the simulation environment to support the needs of the designer during realisation of particular stages of the prototyping design cycle. Practical realisation of the concept is, however, confined by several problems. One of these is related to the fact that the FDD design often imposes processing of different knowledge items like heuristic facts, qualitative models or analytical models. To support their processing, it is necessary to make use of different program tools which, at the present level of development, lack the cost-effective mechanisms for easy integration. In turn, such a mismatch demands an extra effort from the designer in arranging transfer of data between the packages, as well as in handling the various tools. These problems are illustrated by an example of the environment developed for support of the FDD prototyping design of a DC motor.

DIALOGS: Diagnostics with Logistic Support for System Supervision

This paper discusses recent developments within the DIALOGS project which are aimed at implementing a range of advanced fault detection techniques in real industrial applications. The DIALOGS methodology of representing the plant's physical and functional structure as a hierarchy is presented. The mechanism to map the target faults onto this hierarchy is discussed resulting in the choice of appropriate fault detection algorithm. An example fault detection system design is presented for a heat exchanger pilot plant application. This design example shows the complementary relationships between the analytically derived fault detection algorithms such as Static Characteristics and Parity Space, statistical residual evaluation using GLR and CUSUM, and logic based reasoning to make the final diagnostic hypothesis. A possible mechanism to present this information to the operators is also presented.

Fault diagnosis for the Space Shuttle main engine

A conceptual design of a model-based fault detection and diagnosis system is developed for the Space Shuttle main engine. The design approach consists of process modeling, residual generation, and fault detection and diagnosis. The engine is modeled using a discrete time, quasilinear state-space representation. Model parameters are determined by identification. Residuals generated from the model are used by a neural network to detect and diagnose engine component faults. Fault diagnosis is accomplished by training the neural network to recognize the pattern of the respective fault signatures. Preliminary results for a failed valve, generated using a full, nonlinear simulation of the engine, are presented. These results indicate that the developed approach can be used for fault detection and diagnosis. The results also show that the developed model is an accurate and reliable predictor of the highly nonlinear and very complex engine.