Enhanced Empirical Wavelet Transform with Application to Rolling Bearings Fault Diagnosis

Bearings are among the most widely used core components in mechanical equipment. Their failure creates the potential for serious accidents and economic losses. Vibration signature analyses are the most common approach to assess the viability of bearings due to its ease of measurement and high correlation with structural dynamics. However, the collected vibration signals of rolling bearings are usually nonstationary and are inevitably accompanied by noise interference. This makes it difficult to extract the feature frequency for the failed bearing and affects the diagnosis accuracy. The majorization-minimization-based total variation (TV-MM) denoising algorithm effectively removes the noise interference from the signal and highlights the related feature information. The value of its main parameter λ determines the quality of the denoising effect. However, manually selecting parameters requires professional experience in a process that it is time-consuming and laborious, while the use of genetic algorithms is cumbersome. Therefore, an improved particle swarm algorithm (IPSO) is used to find the optimal solution of λ. The IPSO utilises the mutation concept in genetic algorithms to reinitialise the particles with a certain probability after each update. In addition, the empirical wavelet transform (EWT) is an adaptive signal processing method suitable for processing nonlinear and nonstationary signals. Therefore, this paper presents an ensemble analysis method that combines the IPSO, TV-MM, and EWT. First, IPSO is used to optimise the denoising parameter λ. The TV-MM under this parameter effectively removes the background noise interference and improves the accuracy of the subsequent modal decomposition. Then, the EWT is used for the adaptive division to produce a set of sequences. Finally, Hilbert envelope demodulation is performed on each component to realise fault diagnosis. The results from simulations and signals received from defective bearings with outer race fault, inner race fault, and rolling element fault demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings.

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An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis

Applied Sciences ◽

10.3390/app8122352 ◽

2018 ◽

Vol 8 (12) ◽

pp. 2352 ◽

Cited By ~ 16

Author(s):

Yonggang Xu ◽

Kun Zhang ◽

Chaoyong Ma ◽

Xiaoqing Li ◽

Jianyu Zhang

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Rolling Bearing ◽

Vibration Acceleration ◽

Rolling Bearings ◽

Bearing Fault ◽

Acceleration Signal ◽

Trend Component ◽

Empirical Wavelet Transform ◽

The Fourier Transform

As essential but easily damaged parts of rotating machinery, rolling bearings have been deeply researched and widely used in mechanical processes. The real-time detection of bearing state and simple, rapid, and accurate diagnosis of bearing fault are indispensable to the industrial system. The bearing’s inner ring and outer ring vibration acceleration can be measured by high-precision sensors, and the running state of the bearing can be effectively extracted. The empirical wavelet transform (EWT) can adaptively decompose the vibration acceleration signal into a series of empirical modes. However, this method not only runs slowly, but also causes inexplicable empirical modes due to the unreasonable boundaries of the frequency domain division. In this paper, a new method is proposed to improve the empirical wavelet transform by dividing the boundaries from the spectrum, named the fast empirical wavelet transform (FEWT). The proposed method chooses different points in the Fourier transform of the spectrum (key function) to reconstruct the trend component of the spectrum. The minimum points in the trend component divide the spectrum into a series of bands. A more reasonable set of boundaries can be found by choosing appropriate trend components to obtain effective empirical modes. The simulation results show that the proposed method is effective and that the acquired empirical mode is more reasonable than the EWT method. Combining kurtosis with fault feature extraction of inner and outer rings of bearings, the method is successfully applied to the fault diagnosis of rolling bearings.

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Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing

Entropy ◽

10.3390/e21050490 ◽

2019 ◽

Vol 21 (5) ◽

pp. 490 ◽

Cited By ~ 3

Author(s):

Yonggang Xu ◽

Junran Chen ◽

Chaoyong Ma ◽

Kun Zhang ◽

Jinxin Cao

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Rolling Bearing ◽

Practical Significance ◽

Measured Signal ◽

Rolling Bearings ◽

Fault Features ◽

Empirical Wavelet Transform ◽

Simulation Signal ◽

Spectrum Decomposition

The rolling bearings often suffer from compound fault in practice. Compared with single fault, compound fault contains multiple fault features that are coupled together and make it difficult to detect and extract all fault features by traditional methods such as Hilbert envelope demodulation, wavelet transform and empirical node decomposition (EMD). In order to realize the compound fault diagnosis of rolling bearings and improve the diagnostic accuracy, we developed negentropy spectrum decomposition (NSD), which is based on fast empirical wavelet transform (FEWT) and spectral negentropy, with cyclic extraction as the extraction method. The infogram is constructed by FEWT combined with spectral negentropy to select the best band center and bandwidth for band-pass filtering. The filtered signal is used as a new measured signal, and the fast empirical wavelet transform combined with spectral negentropy is used to filter the new measured signal again. This operation is repeated to achieve cyclic extraction, until the signal no longer contains obvious fault features. After obtaining the envelope of all extracted components, compound fault diagnosis of rolling bearings can be realized. The analysis of the simulation signal and the experimental signal shows that the method can realize the compound fault diagnosis of rolling bearings, which verifies the feasibility and effectiveness of the method. The method proposed in this paper can detect and extract all the fault features of compound fault completely, and it is more reliable for the diagnosis of compound fault. Therefore, the method has practical significance in rolling bearing compound fault diagnosis.

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Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽

10.3390/e23080975 ◽

2021 ◽

Vol 23 (8) ◽

pp. 975

Author(s):

Yancai Xiao ◽

Jinyu Xue ◽

Mengdi Li ◽

Wei Yang

Keyword(s):

Machine Learning ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Wind Turbine ◽

Wind Turbines ◽

Gear Tooth ◽

Support Vector ◽

Grey Wolf Optimizer ◽

Empirical Wavelet Transform ◽

Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

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Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

Applied Sciences ◽

10.3390/app9081696 ◽

2019 ◽

Vol 9 (8) ◽

pp. 1696 ◽

Cited By ~ 2

Author(s):

Wang ◽

Lee

Keyword(s):

Wavelet Transform ◽

Fault Diagnosis ◽

Background Noise ◽

Vibration Signal ◽

Spectral Subtraction ◽

Accurate Identification ◽

Side Band ◽

Empirical Wavelet Transform ◽

Gear Fault ◽

Fault Characteristic

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

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