Fault diagnosis

Nc number of compounds involved in the reaction Ny number of considered actuator/process faults r scalar residual 

U set of admissible inputs vectors x vector of state variables 

X set of admissible state vectors y vector of measured output variables y set of admissible output vectors Euclidean norm 

Caccavale et al., Control and Monitoring of Chemical Batch Reactors, Advances in Industrial Control, 

Of vector of unknown parameters characterizing the fault magnitude /u. normalization factor of residuals 

---

J. C. Hoskins, K. M. Kahyur, and D. M. Himmelblau, "The AppHcation of Artificial Neural Networks to Fault Diagnosis in Chemical Processing," paper presented tAIChE Spring Meetings Houston, Tex., 1988. 

J. J. Ferrada, M. D. Gordon, and I. W. Osbome-Lee, "AppHcation of Neural Networks for Fault Diagnosis in Plant Production," paper presented tAIChE National Meetings San Francisco, 1989. 

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)... 

Fathi, Z., W.F. Ramirez, and J. Korhicz, Analytical and Knowledge-Based Redundancy for Fault Diagnosis in Process Plants, AlChE Journal, 39(1), 1993, 42-56. (Fault diagnosis)... 

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. 

Monitoring, fault diagnosis, and implementing corrective actions... 

Equipment Failures Safety system Ignition Sources Furnaces, Flares, Incinerators, Vehicles, Electrical switches. Static electricity, Hot surfaces. Cigarettes Human Failures Omission, Commission, Fault diagnosis. Decisions Domino Effects Other containment failures. Other material release External Conditions Meteorology, Visibility... 

FIGURE 4.6. Decision/Action Diagram for Fault Diagnosis in a Crude Distillation Plant (Duncan and Cray, 1975)... 

Marshall, E. C., Duncan, K. D., Baker, S. M. (1981). The Role of Withheld Information in the Training of Process Plant Fault Diagnosis. Ergonomics 24,711-724. 

Strang. G., Wavelets and dilation equations A brief introduction. SIAM Rev. 31, 614 (1989). Ungar, L. H., Powell, B. A., and Kamens, S. N., Adaptive Networks for fault diagnosis and process control. Comput. Chem. Eng. 14, 561 (1990). 

The pivotal component of many engineering tasks—e.g., process fault diagnosis and product quality control—is the recognition of certain distinguishing temporal patterns (i.e., features) during the operation of the... 

C. Hoskins and D.M. Himmelblau, Fault diagnosis in complex chemical plants using artificial neural networks. AlChE J, 37 (1991) 137-141. 

This chapter provides a complementary perspective to that provided by Kramer and Mah (1994). Whereas they emphasize the statistical aspects of the three primary process monitoring tasks, data rectification, fault detection, and fault diagnosis, we focus on the theory, development, and performance of approaches that combine data analysis and data interpretation into an automated mechanism via feature extraction and label assignment. 

Kramer, M. A., and Palowitch, B. L., Rule-based approach to fault diagnosis using the signed directed graph, AlChE J. 33(7), 1067-1078 (1987). 

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

Wilcox, N. A., and Himmelblau, D. M., The possible cause and effect graphs (PCEG) model for fault diagnosis—I methodology, Comput. Chem. Eng. 18(2) 103-116 (1994). 

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

To face these faults, fault diagnosis modules are developed. They are based on the observations and interpretations of symptoms, i.e. the changes of an observable quantity from normal behavior. Fault Diagnosis task can be split in three main steps. 

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 ... 

A traditional approach to fault diagnosis in the wider application context is based on hardware i.e. physical) redundancy methods which use multiple lines of sensors, actuators, computers and software to measure and/or control a particular variable. Typically, a voting scheme is applied to the hardware redundant system to decide if and when a fault has occurred and its likely location amongst redundant system components. The use of multiple redundancy in this way is common, for example with digital fly-by-wire flight control... 

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. 

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. 

TOGA An Expert System for Transformer Fault Diagnosis... 

An expert system is a computer program to reproduce a problem-solving manner according to an expert s knowledge and inference procedures in a computer system. Expert systems have been used for fault diagnosis in bioprocess operations, the improvement of medium composition, and the optimization of culture conditions, since it can handle a large amount of information and data concerning bioprocess operation and optimization [13]. 

The various direct methods of estimating dispersion are essentially dimensional measurements on more or less a microscopic scale and this is just one example of the value of microscopy for fault diagnosis in rubber products. Dispersion measurements are normally made on cured rubber although it is possible to prepare test pieces from some uncured materials. 

---