Deep signal separation for adaptive estimation of instantaneous phase from vibration signals

Abstract

Accurate extraction of instantaneous phase information is an essential step in condition monitoring and intelligent diagnosis of rotating machinery under variable speed conditions, which will directly affect the reliability of the tacho-less order tracking (TLOT) results. At present, instantaneous phase information extraction techniques are mainly divided into time–frequency analysis and signal decomposition. These methods are constrained because they are not available to extract harmonic components adaptively. Therefore, it is important to develop an intelligent model to accurately separate harmonic component with strict physical meaning for TLOT. In order to address the aforementioned issues, a deep binary mask (BM) signal separation model is presented for rotating machinery TLOT. The deep BM signal separation method can adaptively separate the fundamental harmonic component of vibration signals without prior knowledge. The outcomes indicate that the deep BM signal separation model is more productive and flexible in terms of accuracy and self-adaptation compared to some advanced TLOT algorithms.