Detection of various failure causes in complex mechanical systems by the use of Artificial Neural Networks

The paper presents a methodology based on Artificial Neural Networks (ANN) to perform on-line a diagnosis of the health state of a machinery. The procedure at issue permits to detect the presence of backlash and to determine possible structural failures inside a mechanical system. Backlash and damages are important causes of vibrations in machines, therefore vibrations monitoring gives indirect information on these parasite effects. An ANN is used to classify the system behaviour among a predefined number of classes, receiving as input vibrational signals (simulated or measured). An application is discussed for devices purposely built for indexing motion, where compliance plays an important rôle, affecting the dynamic behavior of the whole machine. An analysis of parameters sensibility for the proposed procedure on simulated cases highlighted the best values and choices for these parameters. Tests of the procedure on experimental data collected on actual devices match closely the good results achieved with simulations.