Diagnosis of faults

As discussed earlier, the successful diagnosis of faults in automated control systems is highly dependent on the mental model the worker has built up of the current state of the plant processes. Such a model takes time to construct. An individual who has to act quickly may not be able to make the necessary diagnoses without time to build up and consult his or her mental model. Even in a highly automated plant, provision needs to be made to display major process deviations quickly. 

The correct interpretation of measured process data is essential for the satisfactory execution of many computer-aided, intelligent decision support systems that modern processing plants require. In supervisory control, detection and diagnosis of faults, adaptive control, product quality control, and recovery from large operational deviations, determining the mapping from process trends to operational conditions is the pivotal task. Plant operators skilled in the extraction of real-time patterns of process data and the identification of distinguishing features in process trends, can form a mental model on the operational status and its anticipated evolution in time. 

Under suitable simplifying assumptions, a kinetic mechanism based on 13 components and 89 second-order reactions is developed. The relevant kinetic parameters (preexponential factors, activation energies, and heats of reaction) are computed on the basis of literature information. In the subsequent chapters, this kinetic model is used to test the techniques for identification, thermal stability analysis, control, and diagnosis of faults presented. 

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. 

To diagnose and find faults in electrical installations and equipment is probably one of the most difficult tasks undertaken by an electrician. The knowledge of fault finding and the diagnosis of faults can never be completely learned because no two fault situations are exactly the same. As the systems we install become more complex, then the faults developed on these systems become more complicated to solve. To be successful the Individual must have a thorough knowledge of the installation or piece of equipment and have a broad range of the skills and competences associated with the electrical industries. 

Fan, J.Y., M. Nikolaou, and R.E. White, An Approach to Fault Diagnosis of Chemical Processes via Neural Networks, AJChF Journal, 39(1), 1993, 82-88. (Relational model development, neural networks)... 

Watanahe, K. and D.M. Himmelhlau, Incipient Fault Diagnosis of Nonlinear Processes with Multiple Causes of Faults, Chemical Engineeiing... 

A fault may interfere with the effectiveness or the func tioning of the unit (Watanabe, K., and D.M. Himmelblau, Incipient Fault Diagnosis of Nonhnear Processes with Multiple Causes of Faults, Chemical Engineering Science, 39(3), 1984, 491-508). The first question addresses the effectiveness. The second two address the functioning. Fault detection is a unit monitoring activity, done automatically or periodically, to determine whether the unit operation has changed. 

Process transients and equipment failures may require workers to develop a new strategy to control the process. Detection, diagnosis, and fault-compensation are tasks in which workers may have little experience and the information needs may be different from those of familiar tasks. Again, methods of task and error analyses, particularly those concerned with human cognitive functions, may be useful in deciding what information should be displayed to help workers detect process transients, diagnose their causes and develop new strategies. 

B. Waiczack and D.L. Massart, Application of radial basis functions-partial least squares to non-linear pattern recognition problems diagnosis of process faults. Anal. Chim. Acta, 331 (1996) 187-193. 

A fault can be regarded as a not allowed deviation of at least one property or one characteristic parameter of the system compared to the normal operating conditions. The occurrence of a fault may have serious consequences in the process and may cause the facilities standstill, even to damage them. Several examples illustrating the gravity of the occurrence of sensor or actuators faults are reported in [25], [26], [62]. The fault detection refers to the determination of the presence of faults as well as their occurrence moment while the diagnosis is the determination of its amplitude and its behavior. 

Related studies to the diagnosis of bioprocess have been limited and have used mainly heuristic approaches. Moreover, they have been concerned with the detection of a disfunction of the bioprocess (detection of a desestabiliza-tion, state of the biomass, etc.) rather than the detection and the location of sensor and/or actuators faults. The interested reader will be able to refer to the following references [4], [5], [12], [14], [27], [28], [43], [45], [47], [52], [59], [60], [61], [63], [64], [71]. 

A great number of approaches have been proposed in the literature to perform the diagnosis of a system. Before briefly detailing them, it is mandatory to define desirable characteristics common to all fault diagnosis systems, as proposed by ... 

Because it offers a framework to manage uncertain and conflicting information, the Evidence theory can be relevant to combine and to cross check fault signals. In the context of fault diagnosis, the frame of discernment 17 will be the set of all possible states of the system, i.e. all the faults that can occur on the supervised process. In other terms, we have ... 

The previous section concentrated on the management of a hard and soft sensors network. This is an important step since the information sources must be carefully checked before being further used. This section will be devoted to the diagnosis of the overall biological state of the process. In particular, it will illustrate that the use of the Evidence theory approach improves the fault diagnosis system in terms of modularity and d3mamical adaptation. 

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. 

Dunia R, Qin SJ. Joint diagnosis of process and sensor faults using principal component analysis. Control Engineering Practice 1998, 6, 457 169. 

In this chapter, an FD framework for batch chemical processes is developed, where diagnosis of sensor, actuator, and process faults can be achieved via an integrated approach. The proposed approach is based on physical redundancy for detection of sensor faults [38], while an analytical redundancy method, based on a bank of diagnostic observers, is adopted to perform process/actuator fault detection, isolation, and identification [4],... 

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. 

M. Karpenko, N. Sepehri, and D. Scuse. Diagnosis of process valve actuator faults using a multilayer neural network. Control Engineering Practice, 11 1289-1299, 2003. 

C. Rojas-Guzman and M.A. Kramer. Comparison of belief networks and rule-based expert systems for fault diagnosis of chemical processes. Engineering Application of Artificial Intelligence, 6 191, 1993. 

N.J. Scenna. Some aspects of fault diagnosis in batch processes. Reliability Engineering and... 

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