Applying Expert System and Fuzzy Logic to an Intelligent Diagnosis System for Fabric Inspection

Abstract

In this study, an intelligent diagnosis system capable of tracing possible breakdown causes of fabric defects is applied to fabric inspection processing. Apart from the basic structure ( e.g., user interface, inference engine, and knowledge base) of the expert system, there is also a diagnosis system developed using fuzzy set theory. This system, which uses the Turbo C program, can act as an expert consultant for operators tracing the causes of breakdowns at any time, and the expert system, developed in an artificial intelligence language PROLOG, can provide the operator with a knowledge base for further consultation in fabric inspection. The accuracy and efficiency of system implementation are also discussed. Implementing automation techniques for production processes and management procedures has become increasingly important in textile engineering. At present, however, fabric inspection processing and tracing breakdowns that occur during weaving still heavily depend on the experience of the technical operator. Results obtained from inspection and diagnosis are exclusively influenced by the mental and physical condition of the inspector.In addition to the time needed to train a new operator to become expert in the specific technical knowledge of weaving engineering, there is the problem of retraining once the trained operator leaves the job. This approach is not only time-consuming but also economically infeasible.To solve these problems, we have applied an intelligent diagnosis system developed using an artificial intelligence programming language PROLOG and Turbo C to fabric inspection processing and diagnosis of breakdowns during weaving.Expert systems are computer-based and use a large body of heuristic and logical rules for solving a specific class of problems [11. These systems have been evaluated for many years by many companies to guide plant operations [ 12,13,14 ] . They work in ways similar to those of very experienced operating and technical personnel and make evaluations to enhance operations or diagnose problems. These systems are potentially important for early detection and diagnosis of mechanical or process problems in the plant.Apart from the basic structure [ 1 ] ( e.g., user interface, inference engine, and knowledge base) of a conventional expert system, the intelligent diagnosis system also has a diagnosis component based on fuzzy set theory. By combining these two components, we can construct an intelligent diagnosis system that has the ability to function as an expert consultant to help a new operator trace breakdowns, to provide the operator with a knowledge base and a data base for various weaving problems, and to apply these bases to fabric inspection. . Theory . AUTOMATIC DIAGNOSIS USING FUZZY SET THEORY ' ,The automatic diagnosis procedure can be demonstrated by an electric circuit, as shown in Figure 1 a, in which switches xi -xm represent m kinds of breakdown causes and lamps yi -yn represent n kinds of symptoms. respectively. Assuming that the breakdown is caused by a switching e...

Applying GA and Fuzzy Logic to Breakdown Diagnosis for Spinning Process

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Searching for Solutions to Fuzzy Logical Equation for Breakdown Diagnosis Using GA

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