Application of fuzzy logic and neural network technologies in cone crusher control

Abstract

Fuzzy Logic presents a robust technique for accommodating measurement uncertainty and error contaminated signals, and a proven technology for representation of heuristic knowledge and automation of subjective manual operations. Neural Network Technology, on the other hand, provides a valuable tool for modelling and prediction of non-linear and difficult processes. A brief summary of Fuzzy Logic and Neural Network principles is presented to provide a basis for the introduction of two applications, one in Fuzzy Logic and the other utilising a Fuzzy Neural Network. The applications are part of a major project aiming to develop a new generation of fully automated control systems for Autocone cone crushers. The Fuzzy application is used in conjunction with a number of novel wear sensors to predict the rates of liner wear under various operational conditions, including feed size, moisture content and crusher's setting. The Neural Network application has been developed as part of a Knowledge-Based Condition Monitoring System and provides a novel technique for vibration analysis of the crusher as a fault diagnosis routine.