Artificial neural networks and genetic algorithms for gear fault detection

摘要

Condition monitoring is gaining importance in industry because of the need to increase machine availability. The use of vibration and acoustic emission (AE) signals is quite common in the field of condition monitoring of rotating machinery with potential applications of artificial neural networks (ANNs) in automated detection and diagnosis. Multi-layer perceptrons (MLPs) and radial basis functions (RBFs) are most commonly used ANNs, though interest on probabilistic neural networks (PNNs) is increasing in general and in the area of machine condition monitoring. Genetic algorithms (GAs) have been used to make the classification process faster and accurate using minimum number of features which primarily characterise the system conditions with optimised structure or parameters of ANNs. In a recent work, results of MLPs with GAs were presented for fault detection of gears using only time-domain features of vibration signals. In this approach, the features were extracted from finite segments of two signals: one with normal condition and the other with defective gears. In the present work, comparisons are made between the performance of three different types of ANNs, both without and with automatic selection of features and classifier parameters for the dataset of . The roles of different vibration signals, obtained under both normal and light loads and at low and high sampling rates, are investigated. The results show the effectiveness of the extracted features from the acquired and preprocessed signals in diagnosis of the machine condition. The procedure is illustrated using the vibration data of an experimental setup with normal and defective gears.