A framework for neural quality control systems design

Stimulated by the growing demand for improving system performance and reliability, fault-tolerant system design has been receiving significant attention. This paper proposes a new framework for fault-tolerant and quality control design based on the learning capabilities of neural networks. In highly nonlinear systems, with slow process variation, and a high degree of reactivity, quick changes (in the environment, for example) cause fast responses. This class of systems is broad enough so that it is not only of theoretical interest but also of practical applicability. Moreover, the fault-tolerance ability of the adaptive controller will be further improved by exploiting information estimated from a fault-detection and diagnosis unit designed by a pattern-matching strategy in multiple faults models interfaced with the overall system. Results of the approach are presented in a practical case of a plastic injection moulding process in industry.

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