A comparison of artificial neural networks and other statistical methods for rotating machine condition classification

Statistical estimates of vibration signals such as the mean and variance can provide indication of faults in rotating machinery. Using these estimates jointly can give a more robust classification than using each individually. Artificial neural network architectures and some statistical algorithms are compared with emphasis on training requirements and real-time implementation as well as overall performance