Monitoring and diagnosis of rolling element bearings using artificial neural networks

Vibration monitoring of components in manufacturing plants involves the collection of vibration data from plant components and detailed analysis to detect features that reflect the operational state of the machinery. The analysis leads to the identification of potential failures and their causes and makes it possible to perform efficient preventive maintenance. Work on the design of a vibration monitoring methodology for rolling element bearings (REB) based on neural network technology is presented. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data that may be distorted or noisy. The significance of this work relies on the fact that REB failures are responsible for a large fraction of the malfunctions in manufacturing equipment. The technique enhances traditional vibration analysis and provides a means of automating the monitoring and diagnosis of vibrating equipment