Hybrid Model of Rolling-Element Bearing Vibration Signal

Jabłoński, Adam

doi:10.3390/en15134819

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Generating synthetic data is far cheaper, faster, and more convenient than collecting real data from individual machine failures [50]. In order to verify the effectiveness of the proposed method, a numerical analysis is conducted by using simulated fault signals.…”

Section: Numerical Verificationmentioning

confidence: 99%

A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis

Zhang,

Wang,

Jiang

et al. 2024

Energies

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Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial component in wind turbines, and their failure is one of the most frequent reasons for system breakdown. Wind turbine bearing faults are usually very localized during their early stages which is precisely when they need to be detected. Hence, the early diagnosis of bearing faults holds paramount practical significance. In order to solve the problem of weak pulses being masked by noise in early failure signals of rolling element bearings, a novel fault diagnosis method is proposed based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and an improved TFR demodulation method. Initially, the decomposition of vibration signals using CEEMDAN is carried out to obtain several intrinsic mode functions (IMFs). Subsequently, a novel KC indicator that combines kurtosis and the correlation function is designed to select the effective components for signal reconstruction. Finally, an innovative approach based on the continuous wavelet transform (CWT) for multi-scale demodulation analysis in the domain of time–frequency representation (TFR) is also introduced to extract the envelope spectrum. Further fault diagnosis can be achieved by the identification of the fault characteristic frequency (FCF). This study focuses on the theoretical exploration of bearing faults diagnosis algorithms, employing modeling and simulation techniques. The effectiveness and feasibility of the proposed method are validated through the analysis of simulated signals and experimental signals provided by the Center for Intelligent Maintenance Systems (IMS) of the University of Cincinnati and the Case Western Reserve University (CWRU) Bearing Data Center. The method demonstrates the capability to identify various types of bearing faults, including outer race and inner race faults, with a high degree of computational efficiency. Comparative analysis indicates a significant enhancement in fault diagnostic performance when compared to existing methods. This research contributes to the advancement of effective bearing fault diagnosis methodologies for wind turbines, thereby ensuring their reliable operation.

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Section: Numerical Verificationmentioning

confidence: 99%

A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis

Zhang,

Wang,

Jiang

et al. 2024

Energies

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“…Te bearing faults are easy to mathematically model according to the diferential equations of motion so as to obtain their time-domain waveforms. Te bearing digital twin model is established in Simulink, and the relevant equations are as follows [30,31]:…”

Section: Construction and Update Of The Bearing Digital Twin Modelmentioning

confidence: 99%

Fault Diagnosis Method for Bearing Based on Digital Twin

Xie

Yang

et al. 2022

Mathematical Problems in Engineering

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The bearing is an essential component of rotating machinery, as its reliability and running state have a direct impact on the machinery’s performance. Considering that deep learning-based fault diagnosis methods for bearing require a large amount of labelled sample data, a novel fault diagnosis framework based on digital twin is proposed. In the case of fault data available, self-organizing maps with minimum quantization error and support vector machine are employed to analyze the data. Where fault data is unavailable, a bearing digital twin model is first constructed to simulate the data, and the convolutional neural network combined with transfer learning is utilized to diagnose the bearing faults. Then, the law of bearing performance degradation is investigated. The effectiveness of the proposed method is verified using bearing vibration data.

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“…Therefore, the diagnostic analysis of rolling bearing faults is of great practical importance. Rolling bearing fault diagnosis focuses on the inner ring, outer ring, and rolling elements [1][2][3] . The traditional diagnostic method is resonance demodulation analysis of bearing fault vibration signals [4] .…”

Section: Introductionmentioning

confidence: 99%

Application of K-Core analysis based on complex network community structure to rolling bearing fault diagnosis

Sun,

Cai,

Gao

et al. 2024

J. Phys.: Conf. Ser.

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A complex network is a method to analyze nonlinear time series on multiple temporal and spatial scales. K-core analysis of several typical nonlinear series was studied, and then based on this, a fast algorithm method was employed to divide the community structure. According to the definition of k-core, the rolling bearings complex network was divided into different cores at the router level, and the main characteristic quantities such as degree distribution and clustering coefficient of every k-core can be analyzed. The results show that there is a corresponding relationship between the characteristic parameters and the community structure of the bearing. The analysis results show that compared with the traditional method, the method of complex network community structure and k-core analysis has a better diagnosis effect.

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Hybrid Model of Rolling-Element Bearing Vibration Signal

Cited by 6 publications

References 28 publications

A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis

A Novel Wind Turbine Rolling Element Bearing Fault Diagnosis Method Based on CEEMDAN and Improved TFR Demodulation Analysis

Fault Diagnosis Method for Bearing Based on Digital Twin

Application of K-Core analysis based on complex network community structure to rolling bearing fault diagnosis

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