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

Bin Pang,Tian Tian,Guiji Tang

doi:10.1109/access.2019.2924962

Bin Pang, Tian Tian + Show 1 more

Open Access

https://doi.org/10.1109/access.2019.2924962

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Journal: IEEE Access	Publication Date: Jan 1, 2019
Citations: 32	License type: CC BY 4.0

Affiliation: North China Electric Power University

Abstract

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.

Highlights

Rolling bearing is among the most critical and defect-prone components of rotating machinery
In order to overcome the deficiency of singular spectrum decomposition (SSD) in detecting weak faults of rolling bearings, a modified SSD termed enhanced SSD (ESSD) is developed in this work
In the process of ESSD, the differentiation operator is firstly applied to the vibration signal of rolling bearings to improve the signalto-interferences ratio (SIR) of the signal and enhance the high-frequency components in which the fault features are always concentrated

Summary

INTRODUCTION

Rolling bearing is among the most critical and defect-prone components of rotating machinery. Scholars have attempted to introduce more efficient signal decomposition methods such as ensemble EMD (EEMD) [19], complementary EEMD (CEEMD) [20], adaptive local iterative filtering (ALIF) [21], empirical WT (EWT) [22], time-varying filtering based EMD (TVF-EMD) [23], swarm decomposition (SWD) [24] and variational mode decomposition (VMD) [25] into bearing fault diagnosis. These methods greatly promote the development of bearing fault diagnosis technology.

SINGULAR SPECTRUM DECOMPOSITION

EXPERIMENTAL ANALYSIS

CONCLUSION