Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform

Zhang, Chunlin; Li, Bing; Chen, Binqiang; Cao, Hongrui; Ye, Zi; He, Zhengjia

doi:10.1016/j.ymssp.2015.03.030

Cited by 110 publications

(51 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, it provides analytic bases by separating positive and negative frequencies in high-pass channels [20]. Employing fractional sampling rate and analytic bases in FAWT provides shift-invariance, tunable oscillatory bases, and flexible time-frequency covering [21]. These properties make this transform suitable for analysing the transient and oscillatory components of the signals.…”

Section: Computation Of Features In Fawt Frameworkmentioning

confidence: 99%

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Kumar

Pachori

Acharya

2017

Entropy

119

View full text Add to dashboard Cite

Myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. It expands rapidly and, if not treated timely, continues to damage the heart muscles. An electrocardiogram (ECG) is generally used by the clinicians to diagnose the MI patients. Manual identification of the changes introduced by MI is a time-consuming and tedious task, and there is also a possibility of misinterpretation of the changes in the ECG. Therefore, a method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method is proposed in this work. First, the segmentation of ECG signals into beats is performed. Then, FAWT is applied to each ECG beat, which decomposes them into subband signals. Sample entropy (SEnt) is computed from these subband signals and fed to the random forest (RF), J48 decision tree, back propagation neural network (BPNN), and least-squares support vector machine (LS-SVM) classifiers to choose the highest performing one. We have achieved highest classification accuracy of 99.31% using LS-SVM classifier. We have also incorporated Wilcoxon and Bhattacharya ranking methods and observed no improvement in the performance. The proposed automated method can be installed in the intensive care units (ICUs) of hospitals to aid the clinicians in confirming their diagnosis.

show abstract

Section: Computation Of Features In Fawt Frameworkmentioning

confidence: 99%

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Kumar

Pachori

Acharya

2017

Entropy

119

View full text Add to dashboard Cite

show abstract

“…The FAWT is applied to the CHF and normal HRV signals to decompose them into sub-band signals. We have used FAWT-based decomposition due to its flexibility to control the Q-factor (Q), redundancy and dilation factor [29,30]. We have combined these sub-band signals so that we can observe the complexity of HRV signals at different frequency scales.…”

Section: Discussionmentioning

confidence: 99%

“…The values of the parameters used in the present work are m = 5, n = 6, u = 1, v = 2 and β = (0.8u)/v [30,40,42]. The 5th level FAWT-based decomposition of signals in Figure 2 is shown in Figure 3.…”

Section: Fawt-based Accumulated Entropiesmentioning

confidence: 99%

“…A 5 represents the approximation signal with a lower frequency components than D 1 to D 5 . FAWT has been used to detect CAD automatically in [40,42] and to find faults in rotating machinery [30]. For implementation of FAWT, we have used the Matlab toolbox provided at http://web.itu.edu.tr/ ibayram/AnDWT/.…”

Section: Fawt-based Accumulated Entropiesmentioning

confidence: 99%

“…The Flexible Analytic Wavelet Transform (FAWT) [29,30] is used to decompose the HRV signals up to the fifth level of decomposition. Decomposed sub-band signals are used to obtain different frequency scales of HRV signals.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals

Kumar

Pachori

Acharya

2017

Entropy

View full text Add to dashboard Cite

Abstract:In the present work, an automated method to diagnose Congestive Heart Failure (CHF) using Heart Rate Variability (HRV) signals is proposed. This method is based on Flexible Analytic Wavelet Transform (FAWT), which decomposes the HRV signals into different sub-band signals. Further, Accumulated Fuzzy Entropy (AFEnt) and Accumulated Permutation Entropy (APEnt) are computed over cumulative sums of these sub-band signals. This provides complexity analysis using fuzzy and permutation entropies at different frequency scales. We have extracted 20 features from these signals obtained at different frequency scales of HRV signals. The Bhattacharyya ranking method is used to rank the extracted features from the HRV signals of three different lengths (500, 1000 and 2000 samples). These ranked features are fed to the Least Squares Support Vector Machine (LS-SVM) classifier. Our proposed system has obtained a sensitivity of 98.07%, specificity of 98.33% and accuracy of 98.21% for the 500-sample length of HRV signals. Our system yielded a sensitivity of 97.95%, specificity of 98.07% and accuracy of 98.01% for HRV signals of a length of 1000 samples and a sensitivity of 97.76%, specificity of 97.67% and accuracy of 97.71% for signals corresponding to the 2000-sample length of HRV signals. Our automated system can aid clinicians in the accurate detection of CHF using HRV signals. It can be installed in hospitals, polyclinics and remote villages where there is no access to cardiologists.

show abstract

Data‐driven multiscale sparse representation for bearing fault diagnosis in wind turbine

et al. 2019

View full text Add to dashboard Cite

With the increase of the wind turbine capacity, failures occur on the drivetrain of wind turbines frequently. Since faults of bearings in the wind turbine can lead to long downtime and even casualties, fault diagnosis of the drivetrain is very important to reduce the maintenance cost of the wind turbine and improve economic efficiency. However, the traditional diagnosis methods have difficulty in extracting the impulsive components from the vibration signal of the wind turbine because of heavy background noise and harmonic interference. In this paper, we propose a novel method based on data‐driven multiscale dictionary construction. Firstly, we achieve the useful atom through training the K‐means singular value decomposition (K‐SVD) model with a standard signal. Secondly, we deform the chosen atom into different shapes and construct the final dictionary. Thirdly, the constructed dictionary is used to sparsely represent the vibration signal, and orthogonal matching pursuit (OMP) is performed to extract the impulsive component. The proposed method is robust to harmonic interference and heavy background noise. Moreover, the effectiveness of the proposed method is validated by numerical simulation and two experimental cases including the bearing fault of the wind turbine generator in the field test. The overall results indicate that compared with traditional methods, the proposed method is able to extract the fault characteristics from the measured signals more efficiently.

show abstract

Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform

Cited by 110 publications

References 41 publications

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework

Use of Accumulated Entropies for Automated Detection of Congestive Heart Failure in Flexible Analytic Wavelet Transform Framework Based on Short-Term HRV Signals

Data‐driven multiscale sparse representation for bearing fault diagnosis in wind turbine

Contact Info

Product

Resources

About