Enhanced Singular Spectrum Decomposition and Its Application to Rolling Bearing Fault Diagnosis

Singular spectrum analysis (SSA) has proven to be a powerful technique for processing non-stationary signals and has been widely used in the fault diagnosis of rolling bearings. Based on the SSA, an adaptive signal decomposition algorithm called singular spectrum decomposition (SSD) was developed. The SSD realizes the adaptive selection of two critical parameters of SSA (i.e., embedding dimension selection and principal components grouping) by concentrating on the frequency components of the signal. Despite that SSD makes the SSA techniques more automated and has shown its potentials in detecting bearing faults, it may fail to separate the fault bearing signals whose frequencies are not outstanding among the frequency components of the signal. Hence, this paper presents an enhanced SSD (ESSD) approach to better detect bearing faults by introducing the differentiation and integration operators into SSD. Specifically, the raw vibration signal is first differentiated to highlight the fault signal components. Then, the new signal retrieved through the differentiation process is subjected to SSD to yield a number of singular spectrum components (SSCs). Finally, each SSC is integrated to obtain the enhanced SSC (ESSC). The simulation analysis indicates that the ESSD improves the anti-interference capability of the SSD. The ESSD provides more pleasant results in an experimental bearing fault signals’ analysis compared with the original SSD, variational mode decomposition (VMD), and Kurtogram algorithms, which illustrates the superiority of the ESSD for detecting bearing faults.

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