An anomalous frequency band identification method utilising available healthy historical data for gearbox fault detection

Abstract

Informative frequency band identification methods are used to automatically design bandpass filters to enhance fault signatures in vibration measurements. Blind and targeted features can be used to guide the frequency band selection process. Blind features’ performance is impeded when there are dominant non-stationary extraneous components, whereas targeted features’ performance is impeded when the characteristic frequency of the machine component is unknown, erroneously estimated or the damaged component is not targeted. An anomalous frequency band identification method is proposed that utilises the available historical data to detect weak damage components that deviate from the baseline or reference condition. This makes it possible to ignore dominant extraneous components that are also present in the historical dataset. The proposed method is analysed and compared against conventional and feature ratio methods on numerical and experimental datasets. The results demonstrate that the proposed method has much potential for identifying informative frequency bands for fault detection.