Adaptive noise cancelling and time–frequency techniques for rail surface defect detection

Abstract

Adaptive noise cancelling (ANC) is a technique which is very effective to remove additive noises from the contaminated signals. It has been widely used in the fields of telecommunication, radar and sonar signal processing. However it was seldom used for the surveillance and diagnosis of mechanical systems before late of 1990s. As a promising technique it has gradually been exploited for the purpose of condition monitoring and fault diagnosis. Time–frequency analysis is another useful tool for condition monitoring and fault diagnosis purpose as time–frequency analysis can keep both time and frequency information simultaneously. This paper presents an ANC and time–frequency application for railway wheel flat and rail surface defect detection. The experimental results from a scaled roller test rig show that this approach can significantly reduce unwanted interferences and extract the weak signals from strong background noises. The combination of ANC and time–frequency analysis may provide us one of useful tools for condition monitoring and fault diagnosis of railway vehicles.