Failure rate analysis of jaw crusher using artificial neural network

摘要

Crusher is the primary equipment which is employed for comminuting the mineral in processing plants. Hence, any kind of failure of equipment will accordingly hinder the performance of the plant Therefore, to minimize sudden failures, proper brainstorming needs to be done to improve performance and operational reliability of jaw crushers. This paper considers the methods for analysing failure rates of jaw crusher through 2-parameter Weibull distribution using ANN (Artificial Neural Network). 40 numbers of Weibull distribution parameters are evaluated to examine R2 (Regression coefficient) using ANN. ANN multilayer perceptron model constructed with back-propagation algorithm using shape, scale and time parameters as input and failure rate as an output from Weibull distribution.