The application of neural networks to vibrational diagnostics for multiple fault conditions

Vibration analysis has long been used for the detection and identification of machine fault conditions. The specific characteristics of the vibration spectrum that are associated with common fault conditions are quite well known, e.g. the BPOR spectral component reflecting beating defects and the peak at the rotational frequency in the vibration spectrum indicating the degree of imbalance. The typical use of these features would be to determine when a machine should be taken out of operation in the presence of deteriorating fault conditions. Reliable diagnostics of deteriorating conditions may however be more problematic in the presence of simultaneous fault conditions. This paper demonstrates that the presence of a bearing defect makes it impossible to determine the degree of imbalance based on a single vibration feature, e.g. the peak at rotational frequency. In such a case it is necessary to employ diagnostic techniques that are suited to the parallel processing of multiple features. Neural networks are the best known technique to approach such a problem. The paper demonstrates that a neural classifier using the X and Y components of both the peak at rotational frequency and the peak at BPOR frequency as input features, can reliably diagnose the presence of bearing defect and can at the same time indicate the degree of imbalance. Different supervised and unsupervised neural classification techniques are then evaluated for their ability to reliably model the degree of imbalance, while also identifying the presence of defects.