APPLICATION OF FUZZY-LOGIC AND NEURAL-NETWORK TECHNOLOGIES IN CONE CRUSHER CONTROL

Fuzzy Logic presents a robust technique for accommodating measurement uncertainty and error contaminated signals, and a proven technology fo r representation of heuristic knowledge and automation of subjective m anual operations. Neural Network Technology, on the other hand, provid es a valuable tool for modelling and prediction of non-linear and diff icult processes. A brief summary of Fuzzy Logic and Neural Network pri nciples is presented to provide a basis for the introduction of two ap plications, one in Fuzzy Logic and the other utilising a Fuzzy Neural Network. The applications are part of a major project aiming to develo p a new generation of fully automated controls systems for Autocone co ne crushers. The Fuzzy application is used in conjunction with a numbe r of novel wear sensors to predict the rates of liner wear and under v arious operational conditions, including feed size, moisture content a nd crusher's setting. The Neural Network application has been develope d as part of a Knowledge-Based Condition Monitoring System and provide s a novel technique for vibration analysis of the crusher as a fault d iagnosis routine.