Predicting mean time between failures of a maintained equipment using artificial neural network

Abstract

ABSTRACT In this study an attempt is made to use the Artificial Neural Network (ANN) model predict the Mean Time between Failures of manufacturing equipment. The equipment failure pattern was carefully studied and some key factors affecting the Mean Time between Failures were identified. An Artificial Neural Network model, the Multi-Layer perceptron, with two hidden layers and seven processing elements was built. Twelve months of maintenance data of the machine was collected. The data items were divided into 3 sets: Training, Validation and Testing sets for analysis. Using the method of Back-Propagation, the Artificial Neural Network model was trained and tested. Eight input factors were identified; the output was classified into three: Low, Medium and High Mean Time Between Failures. The analysis of the model confusion matrix indicates an overall model performance accuracy of 82% with a Normalised Mean Square Error of 0.24. It can be concluded that with the availability of adequate records, the Artificial Neural Network can serve as a useful maintenance management tool