Bearing and Gear Fault Detection Using Artificial Neural Networks

Rotating machinery plays an important role in industrial applications. When these machines recently are getting more complicated, fault diagnosis techniques have become more and more significant. In order to keep the machine performing at its best, one of the principal tools for the diagnosis of rotating machinery problems is the vibration analysis, which can be used to extract the fault features and then identify the fault patterns. In addition, there is a demand for techniques that can make decision on the running health of the machine automatically and reliably. Artificial intelligent techniques have been successfully applied to automated detection and diagnosis of machine conditions. They largely increase the reliability of fault detection and diagnosis systems. Accordingly, the aim of this paper is to apply a feed-forward efficient neural network to classify a large number of vibration signals acquired from rotating machinery in different states: normal, good gear but faulty bearing, good bearing but faulty gear and faulty gear and bearing. The parameters given to the neural networks have been extracted from the power spectral density of the signals. The main impact of this neural network is to generate answers that give the combined state of gears and bearings simultaneously whereas most of previous neural networks have focalized mainly on gears or on bearings alone.