Fault diagnosis knowledge-based

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)... 

Groth T, Moden H. Related articles. A knowledge-based system for real-time quality control and fault diagnosis of multitest analyzers. Comput Methods Programs Biomed 1991 Feb-Mar 34(2-3) 175-190. 

Implementation of an Expert System for On-Line Fault Diagnosis in a Commercial Scale Chemical Process Impact and Requirements of Knowledge-Based Software Expert Systems in the Pulp and Paper Industry ICI Process Engineering Expert Systems - Present and Future Development of a Diagnostic Expert System for a Whipped Toppings Process... 

J. Brandt, B. Hitzmann, Knowledge-based fault detection and diagnosis in flow-injection analysis, Anal. Chim. Acta 291 (1994) 29. 

Frank, M.P. 1990. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancya survey and some new resdIts.Automatica 26(3), 459-474. 

This contribution presents a real-time knowledge-based system for the supervision of a multi channel FIA system applied in the on-line bioprocess monitoring of glucose, maltose, polysaccharide, ammonium, and alkaline protease measurements. In this contribution the special conditions of fault detection in a multi channel FIA system are explained. Examples of typical faults are given to explain how on-line symbolic knowledge processing can be combined with numerical analysis of data to perform a fast and reliable fault detection and fault diagnosis. 

To illustrate the inference capability of the knowledge based system for a multi channel FIA system an other example is presented. In this example special rules achieve an accurate diagnosis by taken into consideration the a priori knowledge (such as knowledge about the specific structure of the multi channel FIA system) as well as heuristics of the experienced operators. In the example, the fault of a decreased flow rate is also discussed and how it effects different FIA channels. The disturbance of the signal shape is dependant on the specific measuring principal. In... 

Model-based fault detection methods make use of a model to obtain the a priori knowledge needed for fault diagnosis. The development of a model can be a challenging task. Modelling assumptions, decisions on which effects shall be taken into account and how to model them clearly affect the quality of diagnosis results. Model-based FDI depends on the model accuracy. However, once an appropriate model has been developed it allows to obtain information about the expected system behaviour and to compare it with measurements of the real system behaviour. The diagnosis techniques and the diagnosis precision depend on the kind of model used. 

Frank, P. (1990). Fault Diagnosis in Dynamic Systems Using Analytical and Knowledge-based Redundtmcy - A Survey and Some New Results. Automatica, 26.459-474. 

The knowledge model used for fault diagnosis is developed from some fundamental understanding of the physics of the process. In this chapter, a unified approach to bond graph model-based qualitative and quantitative FDl is addressed. Causality in bond graph models has been exploited in different ways to arrive at various qualitative and quantitative diagnosis schemes. The relative merits and demerits of these schemes are assessed. 

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