Use of time domain features for the neural network based fault diagnosis of a machine tool coolant system

Abstract

A procedure is presented for the fault diagnosis of a machine tool coolant system by the use of artificial neural networks (ANNs). The characteristic features of time domain signals of the system with normal and faulty conditions have been used as inputs to ANNs, consisting of input, hidden and output layers. The input layer consisted of nodes for features extracted from the time domain response of the coolant system with simulated faults and severity levels. The inputs were normalized within the range 0.0 and 1.0. The output layer consisted of nodes indicating the status of the system: normal or defective, with classification of faults and severity levels. Two hidden layers with different numbers of neurons were used. Two schemes were used to develop ANNs for the separate and simultaneous detection of fault type and severity level. The ANNs were trained using a back-propagation algorithm with the normalized set of extracted features for known conditions. The ANNs were tested using the data of simulated test faults of both trained and novel types. The diagnostic system detected the fault type and severity level quite accurately in both schemes. The reduced number of inputs lead to faster training, requiring far fewer iterations compared with the case of using the entire time response curve.