Improved singular spectrum decomposition-based 1.5-dimensional energy spectrum for rotating machinery fault diagnosis

Abstract

Fault diagnosis of rotating machinery has always been being a challenge thanks to the various effects of nonlinear factors. To address this problem, combining the concepts of improved singular spectrum decomposition with 1.5-dimensional energy spectrum in this paper, a novel method is presented for diagnosing the partial faults of rotating machinery. Within the proposed algorithm, waveform matching extension is firstly introduced to suppress the end effect of singular spectrum decomposition and obtain several singular spectrum components (SSCs) whose instantaneous features have physical meaning. Meanwhile, a new sensitive index is put forward to choose adaptively the sensitive SSCs containing the principal fault characteristic signatures. Subsequently, 1.5-dimensional energy spectrum of the selected sensitive SSC is conducted to acquire the defective frequency and identify the fault type of rotating machinery. The validity of the raised algorithm is proved through the applications in the fault detection of gear and rolling bearing. It turned out that the proposed method can improve signal’s decomposition results and is able to detect effectively the local faults of gear or rolling bearing. The studies provide a new perspective for the improvement in damage detection of rotating machinery.