Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions

Heidari-Bafroui, Hojat; Ohadi, Abdolreza

doi:10.1016/j.neucom.2013.12.018

Cited by 170 publications

(27 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the wavelet energy entropy is presented, which is a combination of the energy and Shannon entropy of a signal's wavelet coefficients [46]. The energy of the wavelet coefficients under DL j is…”

Section: Choice With Entropy Analysismentioning

confidence: 99%

A method for improving wavelet threshold denoising in laser-induced breakdown spectroscopy

Zhang

Sun

et al. 2015

Spectrochimica Acta Part B: Atomic Spectroscopy

View full text Add to dashboard Cite

Section: Choice With Entropy Analysismentioning

confidence: 99%

A method for improving wavelet threshold denoising in laser-induced breakdown spectroscopy

Zhang

Sun

et al. 2015

Spectrochimica Acta Part B: Atomic Spectroscopy

View full text Add to dashboard Cite

“…In the literature, several indices have been presented for fault diagnosis. In this work, the SE, energy and RMS indices, which have proven to be efficient in other electric applications related to the diagnosis of faults in induction motors and transformers [36][37][38][39], are analyzed as potential indicators to diagnose SWSC faults.…”

Section: Fault Indicesmentioning

confidence: 99%

Shannon Entropy Index and a Fuzzy Logic System for the Assessment of Stator Winding Short-Circuit Faults in Induction Motors

et al. 2019

View full text Add to dashboard Cite

The induction motor (IM) is one of the most important elements in industry. Although IMs are robust machines, they are susceptible to faults, where the stator winding short-circuit fault is one of the most common ones. In this work, the Shannon entropy (SE) index and a fuzzy logic (FL) system are proposed to diagnose short-circuit faults, considering both different severity levels and different load conditions. In the proposed methodology, a filtering stage based on brick-wall band-pass filters is firstly carried out. After this stage, the SE index is computed to quantify the fault severity and a FL system is applied to diagnose the IM condition in an automatic way. Unlike other works that propose some types of space transformations, the proposal is only based on a filtering stage and a time domain index, requiring low computational resources. The obtained results demonstrate the effectiveness of the proposal, i.e., the SE index quantifies the fault severity, regardless of the mechanical load, and the proposed FL system achieves a positive classification rate of 98%.

show abstract

“…As a core component of rotating machinery, rolling bearings play an important role in the operation of mechanical equipment [1]. 40% mechanical equipment failures are related to bearings [2]. Therefore, it has important practical significance for the fault diagnosis of rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Based on EEMD-KPCA-MTS for Rolling Bearing

Han¹,

Cheng²

2020

Proceedings of the International Academic Conference on Frontiers in Social Sciences and Management Innovation (IAFSM 2019)

View full text Add to dashboard Cite

To improve the accuracy of fault diagnosis for rolling bearing, an integrated fault diagnosis method based on EEMD(Ensemble Empirical Mode Decomposition), KPCA(Kernel Principal Component Analysis) and MTS (Mahalanobis Taguchi System)is proposed. Firstly, EEMD decompose the non-stationary and nonlinear vibration signals into a series of IMFs (intrinsic mode functions). Then some of IMFs sensitive to the fault information is selected by the IMF sensitive discriminant algorithm and establish the initial feature vector. Then, KPCA further reduces the dimensionality of the vector. Finally, an effective MTS fault detectors and classifiers is established to identify fault types of sample data. The experimental results show that compared with conventional single fault diagnosis methods, the EEMD-KPCA-MTS model has strong adaptability and accuracy.

show abstract

Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions

Cited by 170 publications

References 23 publications

A method for improving wavelet threshold denoising in laser-induced breakdown spectroscopy

A method for improving wavelet threshold denoising in laser-induced breakdown spectroscopy

Shannon Entropy Index and a Fuzzy Logic System for the Assessment of Stator Winding Short-Circuit Faults in Induction Motors

Fault Diagnosis Based on EEMD-KPCA-MTS for Rolling Bearing

Contact Info

Product

Resources

About