Enhanced frame expansion via configuring filterbank topology for rapid processing of multi-sensor vibration data with applications to turbo-machinery fault diagnosis

Abstract

Multi-sensor deployment is usually utilized in recent structural health monitoring of rotating machinery. Conventional wavelet transform is widely applied in relevant data processing tasks. However, wavelet transform still suffers from deficiencies such as lacking of frequency adjustability and shift variance property. In this article, an enhanced frame expansion is constructed via filterbank topology approach, which addresses these problems but at the same time can be implemented via fast numerical algorithm. In this scheme, different discrete complex wavelet frames are deployed at different processing stages within the filterbank structure. Enhanced frame expansion is obtained via integration of multiple translation-invariant frames. The derived timescale analyzing properties of enhanced frame expansion, whose wavelet functions form approximate Hilbert transform pairs, were investigated. It is also verified that the enhanced frame expansion satisfies the necessary constraints of complex-valued wavelet frame and other beneficial merits. Another major advantage presented by the enhanced frame expansion, which is different from complex-valued wavelet frame, dwells in its nondyadic frequency-scale paving. Moreover, the constructed enhanced frame expansion is also of low computational burden due to its inheriting of tree structure filterbank. The proposed enhanced frame expansion is applied to vibration feature extraction from multi-sensor data of a turbo-machinery. The processing results from the displacement signal as well as the acceleration signal indicate the rub-impact fault.