Expert systems for fault analysis

The initial aim of this research was to investigate the application of expert Systems, or Knowledge Base Systems technology to the automated synthesis of Hazard and Operability Studies. Due to the generic nature of Fault Analysis problems and the way in which Knowledge Base Systems work, this goal has evolved into a consideration of automated support for Fault Analysis in general, covering HAZOP, Fault Tree Analysis, FMEA and Fault Diagnosis in the Process Industries. This thesis described a proposed architecture for such an Expert System. The purpose of the System is to produce a descriptive model of faults and fault propagation from a description of the physical structure of the plant. From these descriptive models, the desired Fault Analysis may be produced. The way in which this is done reflects the complexity of the problem which, in principle, encompasses the whole of the discipline of Process Engineering. An attempt is made to incorporate the perceived method that an expert uses to solve the problem; keywords, heuristics and guidelines from techniques such as HAZOP and Fault Tree Synthesis are used. In a truly Expert System, the performance of the system is strongly dependent on the high quality of the knowledge that is incorporated. This expert knowledge takes the form of heuristics or rules of thumb which are used in problem solving. This research has shown that, for the application of fault analysis heuristics, it is necessary to have a representation of the details of fault propagation within a process. This helps to ensure the robustness of the system - a gradual rather than abrupt degradation at the boundaries of the domain knowledge.

Decision-making in product quality based on failure knowledge

Decision-making in product quality is an indispensable stage in product development, in order to reduce product development risk. Based on the identification of the deficiencies of quality function deployment (QFD) and failure modes and effects analysis (FMEA), a novel decision-making method is presented that draws upon a knowledge network of failure scenarios. An ontological expression of failure scenarios is presented together with a framework of failure knowledge network (FKN). According to the roles of quality characteristics (QCs) in failure processing, QCs are set into three categories namely perceptible QCs, restrictive QCs, and controllable QCs, which present the monitor targets, control targets and improvement targets respectively for quality management. A mathematical model and algorithms based on the analytic network process (ANP) is introduced for calculating the priority of QCs with respect to different development scenarios. A case study is provided according to the proposed decision-making procedure based on FKN. This methodology is applied in the propeller design process to solve the problem of prioritising QCs. This paper provides a practical approach for decision-making in product quality. Copyright © 2011 Inderscience Enterprises Ltd.