FAULT DIAGNOSIS OF GEARBOXES USING LSSVM AND WPT

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

HEIDARI M.; HOMAEI H.; GOLESTANIAN H.; HEIDARI A.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Automotive Science and Engineering Year:2015 | Volume:5 | Issue:4 Page(s): 0-0

Download Full-Text

View:

309

Download:

Cites:

Information Journal Paper

Title

FAULT DIAGNOSIS OF GEARBOXES USING LSSVM AND WPT

Author(s)

HEIDARI M. | HOMAEI H. | GOLESTANIAN H. | HEIDARI A. | Issue Writer Certificate

Keywords

GEARBOX

FAULT DIAGNOSIS

WAVELET

LEAST SUPPORT VECTOR MACHINE

Abstract

This paper concentrates on a new procedure which experimentally recognizes gears and bearings faults of a typical GEARBOX system using a least square support vector machine (LSSVM). Two WAVELET selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative WAVELET Energy are used and compared to select an appropriate WAVELET for feature extraction. The FAULT DIAGNOSIS method consists of three steps, firstly the six different base WAVELETs are considered. Out of these six WAVELETs, the base WAVELET is selected based on WAVELET selection criterion to extract statistical features from WAVELET coefficients of raw vibration signals. Based on WAVELET selection criterion, Daubechies WAVELET and Meyer are selected as the best base WAVELET among the other WAVELETs considered from the Maximum Relative Energy and Maximum Energy to Shannon Entropy criteria respectively. Finally, the GEARBOX faults are classified using these statistical features as input to LSSVM technique. The optimal decomposition level of WAVELET is selected based on the Maximum Energy to Shannon Entropy ratio criteria. In addition to this, Energy and Shannon Entropy of the WAVELET coefficients are used as two new features along with other statistical parameters as input of the classifier. Some kernel functions and multi kernel function as a new method are used with three strategies for multi classification of GEARBOXes. The results of fault classification demonstrate that the LSSVM identified the fault categories of GEARBOX more accurately with multi kernel and OAOT strategy.

Cites

No record.

References

No record.

Cite

APA: Copy

HEIDARI, M., HOMAEI, H., GOLESTANIAN, H., & HEIDARI, A.. (2015). FAULT DIAGNOSIS OF GEARBOXES USING LSSVM AND WPT. AUTOMOTIVE SCIENCE AND ENGINEERING (INTERNATIONAL JOURNAL OF AUTOMOTIVE ENGINEERING), 5(4), 0-0. SID. https://sid.ir/paper/321798/en

Vancouver: Copy

HEIDARI M., HOMAEI H., GOLESTANIAN H., HEIDARI A.. FAULT DIAGNOSIS OF GEARBOXES USING LSSVM AND WPT. AUTOMOTIVE SCIENCE AND ENGINEERING (INTERNATIONAL JOURNAL OF AUTOMOTIVE ENGINEERING)[Internet]. 2015;5(4):0-0. Available from: https://sid.ir/paper/321798/en

IEEE: Copy

M. HEIDARI, H. HOMAEI, H. GOLESTANIAN, and A. HEIDARI, “FAULT DIAGNOSIS OF GEARBOXES USING LSSVM AND WPT,” AUTOMOTIVE SCIENCE AND ENGINEERING (INTERNATIONAL JOURNAL OF AUTOMOTIVE ENGINEERING), vol. 5, no. 4, pp. 0–0, 2015, [Online]. Available: https://sid.ir/paper/321798/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops