Rolling Element Bearing Fault Diagnosis Using an Integrated Approach Incorporating Teager–Kaiser Energy Operator and Singular Spectrum Analysis

Due to the coupling of multiple fault feature information and contamination of heavy background noise, it is a challenging task to accurately identify Rolling Bearing Compound Fault (RBCF). A method for isolating and identifying the RBCF is proposed by integrating the Adaptive Periodized Singular Spectrum Analysis (APSSA) with Rényi entropy (RE). The adaptive selection of embedding dimension of Hankel matrix in APSSA without setting parameters empirically is proposed, and a selection criterion for singular values is established to preprocess the vibration signals of rolling bearing and enhance fault periodic component. A RE-based threshold value is introduced to further isolate and decouple the impulse segments of vibration signal in the time domain. Considering inner raceway fault, outer raceway fault, ball fault and skidding, a comprehensive simulation model of compound fault is constructed by the response mechanism of different excited resources. Simulated and experimental data are applied to validate the effectiveness and practicability of proposed method. The results demonstrate that RBCF can be identified correctly by the proposed method under strong background noise.

Section: Introductionmentioning

confidence: 99%

Isolation and identification of rolling bearing compound faults based on adaptive periodized singular spectrum analysis and Rényi entropy

Li,

Yan,

Hou

et al. 2024

“…Nevertheless, detecting early-stage faults in bearings can be challenging due to the absence of conspicuous features and the high background noise at such stages [7]. For this reason, multiple signal processing methods, such as wavelet transform [8,9], empirical wavelet transform (EWT) [10], variational modal decomposition [7], Teager energy operator [11], and order tracking techniques [12], have been introduced into the realm of bearing fault diagnosis [13]. However, relying solely on these signal processing methods for early bearing fault diagnosis can result in faulty misclassification.…”

Section: Introductionmentioning

confidence: 99%

A new rolling bearing fault diagnoses method based on period-doubling bifurcation in the Hindmarsh–Rose model

Liu,

He,

Zhang

et al. 2023

The Hindmarsh–Rose (HR) model is a three-dimensional oscillators susceptible to initial values, making it capable of amplifying even the slightest variations. On this basis, we proposed a rolling bearing fault identification method based on period-doubling bifurcation in the HR model and constructed a bearing fault experimental platform to validate our approach in this paper. Initially, we analyze the HR model’s bifurcation characteristics using the discrete mapping method to identify oscillators suitable for detecting bearing faults. We then select the multiplicative period bifurcation points of the HR model to differentiate between different types of bearing faults. Next, we decompose and reconstruct vibration signals using the Hilbert–Huang transform and calculate the amplitude characteristics of the fault frequency band as the input for the HR detection oscillator. Finally, bearing faults are identified based on the phase trajectory of period-doubling. Furthermore, a comparative analysis is conducted between the proposed methodology and the employment of the empirical wavelet transform. Our approach presents a new perspective for utilizing nonlinear oscillators in bearing fault diagnosis.

“…Singular spectrum analysis (SSA) [24], an excellent tool for analyzing time series, has also been widely used in the field of fault diagnosis. Patel et al [25] proposed the Teager-Kaiser energy operator and combined it with SSA to effectively improve the accuracy and robustness of SSA. Abolghasemi et al [26] proposed recursive SSA, which effectively detects and locates rotor imbalance faults by combining filters and power spectra.…”

Section: Introductionmentioning

confidence: 99%

Early faint fault diagnosis of wheelset axlebox bearings in urban rail trains based on ICiSSA-MOMEDA

Yang,

Sun,

Yao

et al. 2023

Circulant Singular Spectrum Analysis (CiSSA) has good performance in the decomposition and extraction of periodic components of non-stationary signals. However, its decomposition of signals in complex environments suffers from spectral aliasing and difficulty in extracting feature information. Therefore, based on CiSSA, an improved circulant singular spectrum analysis and multi-point optimal minimum entropy deconvolution adjusted (ICiSSA-MOMEDA) is proposed and applied to the early faint fault diagnosis of axlebox bearings of urban rail train wheelsets. Firstly, the optimal embedding dimension is computed adaptively and accurately by an improved Cao's method. Then, the initial components obtained from the decomposition are reorganised by the K-ARs method. ICiSSA effectively solves the problems of spectrum confusion and fault information dispersion. Finally, ICiSSA is combined with MOMEDA to improve its ability to detect weak fault information. The superiority of ICiSSA-MOMEDA is verified based on the analysis of actual bearing data and comparison with other methods.