Diagnosis and Fault-tolerant Control Volume 2

From Fault Diagnosis to Fault-tolerant Control

Vicenc Puig, Silvio Simani

• Publisher: Wiley-ISTE
• Year: 2021
• Print ISBN: 9781789450590
• eBook ISBN: 9781119882343
• Edition: 1
• Topic: Computer Science
• Subtopic: Industrial Management
• Index: Computer Science

This book presents recent advances in fault diagnosis and fault-tolerant control of dynamic processes. Its impetus derives from the need for an overview of the challenges of the fault diagnosis technique and sustainable control, especially for those demanding systems that require reliability, availability, maintainability, and safety to ensure efficient operations. Moreover, the need for a high degree of tolerance with respect to possible faults represents a further key point, primarily for complex systems, as modeling and control are inherently challenging, and maintenance is both expensive and safety-critical. Diagnosis and Fault-tolerant Control 2 also presents and compares different fault diagnosis and fault-tolerant schemes, using well established, innovative strategies for modeling the behavior of the dynamic process under investigation. An updated treatise of diagnosis and fault-tolerant control is addressed with the use of essential and advanced methods including signal-based, model-based and data-driven techniques. Another key feature is the application of these methods for dealing with robustness and reliability.

1
Nonlinear Methods for Fault Diagnosis

Silvio SIMANI¹ and Paolo CASTALDI²

¹Department of Engineering, University of Ferrara, Emilia-Romagna, Italy

²Department of Electrical, Electronic and Information Engineering, University of Bologna, Italy

1.1. Introduction

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 large number of methods can be found in current literature based on the use of mathematical models of the technical process under diagnosis and on exploiting advanced control theory.

Model-based fault diagnosis methods usually use residuals that indicate changes between the process and the model. One general assumption is that the residuals are changed significantly so that 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 application examples considered in this book.

For all of the methods considered, it is essential 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 concerns the accuracy of the model describing the behavior of the monitored system. This issue has become a central research theme over recent years, as modeling uncertainty has risen 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, because, 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 is presented.

Finally, the chapter presents and summarizes special features and problems regarding the different methods.

1.2. Fault diagnosis tasks

According to the definitions available in the related literature, model-based FDI can be defined as the detection, isolation and identification of faults in a system by using methods that can extract features from measured signals and use a priori information on the process available in terms of mathematical models. Faults are, thus, detected by setting fixed or variable thresholds on residual signals generated from the difference between actual measurements and their estimates obtained by using the process model.

A number of residuals can be designed, with each having sensitivity to individual faults occurring in different locations of the system. The analysis of each residual, once the threshold is exceeded, then leads to fault isolation.

Figure 1.1 shows the general model-based FDI system. It comprises two main stages of residual generation and residual evaluation. This structure was first suggested in Chow and Willsky (1980) and now is widely accepted by the fault diagnosis community.

Figure 1.1. Fault diagnosis module

The blocks shown in Figure 1.1 perform the following tasks:

1. 1) The residual generation module generates residual signals using the available inputs and outputs from the process under diagnosis. This residual (or fault symptom) should indicate that a fault has occurred. It should normally be zero or close to zero under no fault condition, and significantly different from zero when a fault occurs. This means that the residual is characteristically independent of process inputs and outputs in ideal conditions. Referring to Figure 1.1, this block is called the residual generator.
2. 2) The residual evaluation module examines residuals for the likelihood of faults and a decision rule is then applied to determine if any faults have occurred. The residual evaluator block in Figure 1.1 may perform a simple threshold test (geometrical methods) on th...

Table of contents

• Cover
• Table of Contents
• Title Page
• Copyright
• 1 Nonlinear Methods for Fault Diagnosis
• 2 Linear Parameter Varying Methods
• 3 Fuzzy and Neural Network Approaches
• 4 Model Predictive Control Methods
• 5 Nonlinear Modeling for Fault-tolerant Control
• 6 Virtual Sensors and Actuators
• 7 Conclusions
• 8 Open Research Issues
• List of Authors
• Index
• Summary of Volume 1
• End User License Agreement