Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis

Wan, Shuting; Zhang, Xiong

doi:10.3390/e20050388

Cited by 44 publications

(22 citation statements)

References 21 publications

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“…Authors Methodologies 1 Qin et al [38] ensemble empirical mode decomposition + energy entropy 2 Su et al [55] empirical mode decomposition + energy entropy 3 Jing et al [56] wavelet transform + energy entropy 4 Wan et al [57] Teager energy entropy ratio of wavelet packet transform 5 Yao et al [58] wavelet packet energy entropy + local outlier factor algorithm 6 Dong et al [59] local mean decomposition + energy entropy 7…”

Section: Indexmentioning

confidence: 99%

“…Mahalanobis distance is employed to monitor health conditions and track performance degradation. The Teager energy entropy ratio gram is proposed to accurately identify the resonant frequency band under strong background noise, which takes the wavelet packet transform as the signal frequency band decomposition method [57]. Similar to this method, Yao et al [58] use wavelet packet energy entropy and the local outlier factor algorithm for real-time chatter detection and suppression of intelligent spindle.…”

Section: Indexmentioning

confidence: 99%

See 1 more Smart Citation

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Liu

Zhi

Zhang

et al. 2019

Entropy

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Rotating machinery plays an important role in various kinds of industrial engineering. How to assess their conditions is a key problem for operating safety and condition-based maintenance. The potential anomaly, fault and failure information can be obtained by analyzing the collected condition monitoring data of the previously deployed sensors in rotating machinery. Among the available methods of analyzing sensors data, entropy and its variants can provide quantitative information contained in these sensing data. For implementing fault detection, diagnosis, and prognostics, this information can be utilized for feature extraction and selecting appropriate training data for machine learning methods. This article aims to review the related entropy theories which have been applied for condition monitoring of rotating machinery. This review consists of typical entropy theories presentation, application, summary, and discussion.

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Section: Indexmentioning

confidence: 99%

Section: Indexmentioning

confidence: 99%

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Liu

Zhi

Zhang

et al. 2019

Entropy

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show abstract

“…As a nonlinear instantaneous energy tracking approach, frequency weighted energy operator (FWEO) is in a position to strengthen the impact symptom effectively [19]. And Shannon entropy has a good capacity of reflecting the uniformity of a given time series [26]. Then a new frequency indicator called weighted energy entropy (FWEE), which combining the respective specialties of FWEO and Shannon entropy, is proposed to overcome the drawback of traditional kurtosis indicator.…”

Section: Frequency Weighted Energy Entropy Indicatormentioning

confidence: 99%

Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

Wang

Yan

2019

Applied Sciences

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The fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward to extract and enhance the fault feature effectively. In this diagnosis method, the fast spectral coherence is fused with the equal step size search strategy for the cyclic frequency parameter and the filter length parameter optimization, and a new frequency weighted energy entropy (FWEE) indicator which combining the advantages of the frequency weighted energy operator (FWEO) and the Shannon entropy, is developed for deconvolution signal evaluation during parameter optimization process. In addition, a novel singular value order determination approach based on fitting error minimum principle is utilized by SVDD to enhance the fault feature. During the process of defect identification, OCYCBD with the optimal parameters is firstly used to recover the informative source from the collected vibration signal. FWEO is further utilized to highlight the potential impulsive characteristics, and the instantaneous energy signal of deconvolution result can be acquired. The whole interferences contained in the instantaneous energy signal can’t be removed due to the weak fault signature and the severe background noise. Then, SVDD is applied to purify the instantaneous energy signal of deconvolution signal, by which the residual interference component is eliminated and the fault feature is strengthened immensely. Finally, frequency domain analysis is performed on the denoised instantaneous energy signal, and the defect location identification of wind turbine bearing can be achieved through analyzing the obvious spectral lines in the obtained enhanced energy spectrum. The collected signals from the experimental platform and the engineering field are both utilized to verify the feasibility of proposed method, and its superiority is further demonstrated through comparing with several well known diagnosis methods. The results indicate this novel method has distinct advantage on bearing weak feature extraction and enhancement.

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“…The fault signal of the inner ring of the drive end is selected to analyze the feature of the EMD method, and compared with the application effect of the wavelet method. Specific experimental procedures are detailed in References [26][27][28]. The experimental conditions are as follows: the drive end bearing adopts 6205-2RS JEM SKF deep groove ball bearing, EDM bearing single point damage, the damage diameter is 0.1778 mm, the motor speed is 1748 r/min, the sampling frequency is 12 KHz, the sampling time is 0.25 s (107.dat).…”

Section: Bearing Fault Data Analysismentioning

confidence: 99%

Theoretical Analysis of Empirical Mode Decomposition

Chen

et al. 2018

Symmetry

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This work suggests a theoretical principle about the oscillation signal decomposition, which is based on the requirement of a pure oscillation component, in which the mean zero is extracted from the signal. Using this principle, the validity and robustness of the empirical mode decomposition (EMD) method are first proved mathematically. This work also presents a modified version of EMD by the interpolation solution, which is able to improve the frequency decomposition of the signal. The result shows that it can provide a primary theoretical basis for the development of EMD. The simulation signal verifies the effectiveness of the EMD algorithm. At the same time, compared with the existing denoising algorithm, it has achieved good results in the denoising of rolling bearing fault signals. It contributes to the development and improvement of adaptive signal processing theory in the field of fault diagnosis. It provides practical value research results for the rapid development of adaptive technology in the field of fault diagnosis. then theoretically explained briefly how EMD operates on harmonic functions and why it selects the highest frequency oscillation, leaving the lower frequency oscillation in the signal, and also derived the obtainable frequency resolutions of the method and the existence of a critical frequency limit that allows separation of the closest harmonics. In order to improve the frequency resolution and the mode mixing effect [1,18], many efforts have been made for these years. Meanwhile, it has been found that methods associated with the local mean decomposition are more benefit to this purpose [19][20][21].Unfortunately, due to the lack of a complete and generally accepted theoretical framework, the EMD method still gives rise to puzzles, because the local mean of the signal depends on its characteristic local time-scales. To this end, this work suggested a theoretical principle involving the oscillation signal decomposition, which is based on the requirement of the pure oscillation component with mean zero extracted from the signal in it. The principle not only firstly demonstrates the validity and robustness of EMD mathematically, but also provides a theoretical framework for the analysis of EMD. Theoretical Principle of Oscillation Signal Decomposition Theoretical PrincipleConsider an oscillation signal x(t) varying with time t and use this signal as a signal for analysis, as shown in Figure 1. Assuming that this signal is composed of a pure oscillation component c(t) of proper rotation, with mean zero and a residual term (the trend or the baseline signal) r(t) from which the oscillation component is removed.Since c(t) is an oscillatory function with mean zero, its integral in the interval of two local maxima (or minima) points t k and t k + 2 should be equal to zero. Therefore, from Equation (1), one has

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Teager Energy Entropy Ratio of Wavelet Packet Transform and Its Application in Bearing Fault Diagnosis

Cited by 44 publications

References 21 publications

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Related Entropy Theories Application in Condition Monitoring of Rotating Machineries

Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

Theoretical Analysis of Empirical Mode Decomposition

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