Fault diagnosis model-based

Mohindra, S., and Clark, P. A., A distributed fault diagnosis method based on digraph models Steady-state analysis, Comput. Chem. Eng. 17, 193 (1993). 

Qualitative models consider cause and effect relationships, express component malfunctions in a qualitative manner and link them with deviations in measurement data. (Qualitative fault diagnosis is based on fault tree analysis [15, 21], or... 

J. Chen and R.J. Patton. Robust model-based fault diagnosis for dynamic systems. Kluwer Academic Publishers, 1999. 

In analytical redundancy schemes, the resulting difference generated from the consistency checking of different variables is called a residual signal. The residual should be by convention zero-valued when the system is normal and should diverge from zero when a fault occurs. This zero and non-zero property of the residual is used to determine whether or not a fault has occurred. Analytical redundancy makes use of a quantitative model of the monitored process and is therefore often referred to as the model-based approach to fault diagnosis. 

R. Isermann and P. Balle. Trends in the application of model-based fault detection and diagnosis of technical processes. Control Eng. Pract, 5(5) 709-719, 1997. 

Chapter 4 represents a bridge between Chaps. 2 and 3, which are mainly devoted to the assessment of the basic ideas of modeling and identification, and Chaps. 5 and 6, in which innovative approaches to model-based control and fault diagnosis for batch... 

Chapter 6 is focused on fault diagnosis methods for chemical batch processes. Consistent with the approach followed in Chap. 5, the focus of the chapter is on model-based techniques and, in particular, on techniques based on the use of state observers. 

After performing the kinetic analysis of the reacting system, the researchers possess suitable kinetic models of different complexity to be used to design and control the entire process. The more complex model should be used to design the reactor this subject is outside the purpose of this book and is only briefly considered in Sect. 7.4. On the contrary, in Chaps. 5 and 6 the kinetic model is used to design adaptive model-based control and fault diagnosis schemes for a class of reactions taking place in batch reactors. 

Early approaches to fault diagnosis were often based on the so-called physical redundancy [11], i.e., the duplication of sensors, actuators, computers, and softwares to measure and/or control a variable. Typically, a voting scheme is applied to the redundant system to detect and isolate a fault. The physical redundant methods are very reliable, but they need extra equipment and extra maintenance costs. Thus, in the last years, researchers focused their attention on techniques not requiring extra equipment. These techniques can be classified into two general categories, model-free data-driven approaches and model-based approaches. 

Model-based approaches to fault diagnosis can be divided into qualitative methods [51] and quantitative methods [35, 36],... 

Basic Principles of Model-Based Fault Diagnosis... 

The literature focused on model-based FD presents a few applications of observers to chemical plants. In [10] an unknown input observer is adopted for a CSTR, while in [7] and [21] an Extended Kalman Filter is used in [9] and [28] Extended Kalman Filters are used for a distillation column and a CSTR, respectively in [45] a generalized Luenberger observer is presented in [24] a geometric approach for a class of nonlinear systems is presented and applied to a polymerization process in [38] a robust observer is used for sensor faults detection and isolation in chemical batch reactors, while in [37] the robust approach is compared with an adaptive observer for actuator fault diagnosis. 

Ideally, residuals should be equal to zero in the absence of faults, while they should become nonzero after the occurrence of faults. Of course, in practice, they are always nonzero due to model uncertainties and disturbances. Since the residual generation is the most important issue of quantitative model-based fault diagnosis, most of the works in this research field have been focused on this problem. A wide variety of techniques are available in the literature (see, e.g., [8, 16] for a complete overview). Since a complete review is outwith the scope of this book, in the following, only the basic concepts of the main approaches are briefly discussed. 

In the case study, the adaptive model-based approach is designed on the basis of a reduced model of the phenol-formaldehyde reaction introduced in Chap. 2. Noticeably, the results show that the fault diagnosis scheme achieves very good performance even when a strongly simplified mathematical model of the reactive system is adopted for the design. 

RM. Frank. Analytical and qualitative model-based fault diagnosis—a survey and some new results. European Journal of Control, 2 6-28, 1996. 

V. Venkatasubramanian, R. Rengaswamy, and S.N. Kavuri. A review of process fault detection and diagnosis part II Qualitative models and search strategies quantitative model-based methods. Computers and Chemical Engineering, 27 313-326, 2003. 

In Chaps. 5 and 6 model-based control and early diagnosis of faults for ideal batch reactors have been considered. A detailed kinetic network and a correspondingly complex rate of heat production have been included in the mathematical model, in order to simulate a realistic application however, the reactor was described by simple ideal mathematical models, as developed in Chap. 2. In fact, real chemical reactors differ from ideal ones because of two main causes of nonideal behavior, namely the nonideal mixing of the reactor contents and the presence of multiphase systems. 

Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques Silvio Simani, Cesare Fantuzzi and Ron J. Patton... 

This book is aimed at tackling the above problems from a joint academic and industrial perspective. Namely, advanced solutions (i.e., based on recent research results) to the four fundamental problems of modeling, identification, control, and fault diagnosis are developed in detail in seven chapters. 

In the fifth chapter, a general overview of temperature control for batch reactors is presented the focus is on model-based control approaches, with a special emphasis on adaptive control techniques. Finally, the sixth chapter provides the reader with an overview of the fundamental problems of fault diagnosis for dynamical systems, with a special emphasis on model-based techniques (i.e., based on the so-called analytical redundancy approach) for nonlinear systems then, a model-based approach to fault diagnosis for chemical batch reactors is derived in detail, where both sensors and actuators failures are taken into account. 

RTO Fault Detection and Diagnosis. Halim proposed a method that uses gradient information from the plant, which is obtained via plant experiments, and the model to both quantify plant-model mismatch and monitor RTO system performance. In this work, the angle between the two profit gradients of the model-based RTO system and that obtained via plant experiments is used as an indicator of RTO... 

Narrowing Diagnostic Focus by Control System Decomposition Model-Based Reasoning for Fault Diagnosis An Expert System Approach to Diagnosis of Product Quality Deviations... 

W. Borutzky, Bond Graph Model-based Fault Diagnosis of Hybrid Systems,... 

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