Application of psychoacoustic filtering for machine fault detection

A method for nondestructive machine fault detection based on the evaluation of acoustic machine signatures is proposed. Various mechanical defects of rotary machines can be reflected in altered acoustic signatures. The proposed method is based on the psychoacoustic modelling of human auditory perception. For the purpose of machine fault detection, the gammatone filterbank is applied in the preprocessing of acoustic signals. Filtered signals are then rectified and features are calculated as mean values of rectified signals. A set of features represents an extracted machine signature. The machine states are evaluated by comparing the extracted signature with the database of previously collected machine signatures. The proposed approach is illustrated by a case study where the quality of commercially produced compressors is estimated. Results show that major faults that occur in a production can be reliably detected.