A Multi-Stage Hybrid Fault Diagnosis Approach for Rolling Element Bearing Under Various Working Conditions

Abstract

To timely detect bearing operating condition, and accurately identify bearing fault type and fault severity, a novel multi-stage hybrid fault diagnosis strategy for rolling bearing is proposed in this paper, which mainly consists of three stages (i.e. fault initial detection, fault type recognition and fault severity assessment). Firstly, the procedure of permutation entropy (PE)-based fault initial detection is performed to estimate bearing operating condition. If the bearing fault exists, the next two stages are conducted for fault type recognition and fault severity assessment. Specifically, in the second and third stages, for each dataset under different fault conditions, hybrid-domain features including time-domain, frequencydomain and time-frequency domain are firstly extracted to establish high-dimensional feature space based on statistical analysis and variational mode decomposition (VMD). Then, locality preserving projection (LPP) is introduced to compress high-dimensional dataset into low-dimensional feature space which can reflect preferably intrinsic information of the raw signal and remove information redundancy embedded in hybriddomain features. Finally, the obtained low-dimensional dataset is imported into Fuzzy C-means (FCM) clustering for recognizing bearing fault type and fault severity. The efficacy of the proposed approach is verified by experimental bearing data under different working conditions. The results indicate that our proposed method can both assess effectively bearing health status and recognize accurately bearing fault type and fault severity. In addition, our proposed approach has higher diagnosis precision than traditional single-stage diagnosis method mentioned in this paper.INDEX TERMS Permutation entropy, variational mode decomposition, locality preserving projection, rolling bearing, fault diagnosis. I. INTRODUCTIONResearch on fault detection of rolling element bearing has drawn much attention in recent years. Rolling element bearings are the major parts of rotating machinery and widely used in many industrial fields. Tiny faults existing in rolling element bearing easily cause the stop running of mechanical system, bring the tremendous economic losses and even give rise to the serious accident and personnel casualties [1]- [5]. Consequently, it is of much concern to explore a novel and The associate editor coordinating the review of this manuscript and approving it for publication was Youqing Wang .

Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis

“…where u k indicates the k-th mode, and ω k means the mid-frequency corresponding to the k-th mode. First, the penalty factorα and Lagrangian multiplier λ(t) are added into Equation 1, so the variational problem (i.e., the augmented Lagrangian) is rewritten as [55]:…”

“…According to Ref. [55], the threshold ε is usually set as 10 −6 . Additional details of VMD can be found in the original article [14].…”

Fault Diagnosis of Rolling-Element Bearing Using Multiscale Pattern Gradient Spectrum Entropy Coupled with Laplacian Score

“…Rolling bearings are regarded as an important joint of modern mechanical equipment; due to the influence of various factors (e.g., manufacturing error, material defect, improper installation, inadequate lubrication, poor sealing, high speed, and heavy load working), its running state will inevitably change and even appear with various faults (e.g., wear, notch, scratch, smudginess, and fretting corrosion). Microlesion emerged in rolling bearing will bring great security risks to enterprises directly, light will cause some economic losses and heavy will result in personnel casualties [2][3][4][5]. On the one hand, owing to the unstable operation condition of mechanical system and the impact of some nonlinear elements (e.g., friction, clearance, and stiffness), the practical bearing fault data has the properties of nonlinear, nonstationary, and low signal-to-noise ratio (SNR), which implies that it is difficult to make effective diagnosis directly through fast Fourier transform (FFT) and its variants [6].…”

Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation

“…Rolling bearing is one of the most common components in rotating machines. Whether the rolling bearing runs normally is directly related to the running state of the whole rotating machinery [ 2 , 3 ]. Due to the bad working environment and long-term working conditions, several key parts of the rolling bearing are easy to be damaged at the same time, resulting in composite faults [ 4 ].…”