Nonlinear Methods for Fault Diagnosis

The model–based approach to fault diagnosis in technical processes has been receiving more and more attention over the last four decades, in the contexts of both research and real plant application. Stemming from this activity, a great variety of methods are found in current literature, based on the use of mathematical models of the technical process under diagnosis and exploiting advanced control theory. Model–based fault diagnosis methods usually use residuals which indicate changes between the process and the model. One general assumption is that the residuals are changed significantly so that a detection is possible. This means that the residual size after the appearance of a fault is large and long enough to be detectable. This chapter provides an overview on different fault diagnosis strategies, with particular attention to the Fault Detection and Isolation (FDI) methods related to the dynamic processes and the application examples considered in this book. All the methods considered require that the technical process can be described by a mathematical model. As there is almost never an exact agreement between the model used to represent the process and the plant, the model–reality discrepancy is of primary interest. Hence, the most important issue in model–based fault detection is concerned with the accuracy of the model describing the behaviour of the monitored system. This issue has become a central research theme over recent years, as modelling uncertainty arises from the impossibility of obtaining complete knowledge and understanding of the monitored process. The main focus of this chapter is the mathematical description aspects of the process whose faults are to be detected and isolated. The chapter also studies the general structure of the controlled system, its possible fault locations and modes. Residual generation is then identified as an essential problem in model–based FDI, since, if it is not performed correctly, some fault information could be lost. The general framework for the residual generation is also recalled. Residual generators based on different methods, such as input–output, state and output observers, parity relations and parameter estimations, are just special cases in this general framework. In the following, some commonly used residual generation and evaluation techniques are discussed and their mathematical formulation presented. Finally, the chapter presents and summarises special features and problems regarding the different methods.