Standoff Bearing Fault Detection Using Directional Microphones and Unsupervised Neural Networks

Abstract

A fault detection method is introduced that uses standoff directional microphones to minimize the number of sensors and an unsupervised neural network to cope with noise and fault signature variability in the microphone signals. In this method, a directional microphone located up to 25 feet is used to sense acoustic signals from a test bearing. These signals are then processed to extract features representing the bearing condition and are used as inputs to an unsupervised fault detection network (FDN) to identify the presence of faults. The main advantage of the FDN is that it does not require seeded-fault data for supervised training of its weights. The proposed fault detection method is tested using microphone data from a bearing with an inner race defect. The results indicate that the FDN provided a 100% detection rate for all microphone locations.