Selection of mother wavelet for effective wavelet transform of bearing vibration signals

H. S. Kumar, Srinivasa P. Pai, N. S. Sriram, G. S. Vijay

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Condition monitoring (CM) and fault diagnosis of equipments has gained greater attention in recent years, due to the need to reduce the down time and enhance the life/ condition of the equipments. The rolling element bearings (REB) are the most critical components in rotary machines. Hence, bearing fault detection and diagnosis is an integral part of the preventive maintenance activity. Vibration signal analysis provides wide range of information for analysis. So in this paper, vibration signals for four conditions of a deep groove ball bearing namely Normal(N), bearing with defect on inner race(IR), bearing with defect on ball(B), and bearing with defect on outer race(OR) have been acquired from a customized bearing test rig under maximum speed and variable load conditions. Depending on the machinery operating conditions and the extent of bearing defect severity, the measured vibration signals are non-stationary in nature. Non-stationary signals are effectively analyzed by wavelet transform technique, which is a popular and widely used time-frequency technique. The focus of this paper is to select a best possible mother wavelet for applying WT on bearing vibration signals. The two selection criteria includes minimum Shannon entropy criteria (MSEC) and Maximum Energy to Shannon Entropy Ratio criteria R(s). This helps in effective bearing CM using WT.

Original languageEnglish
Title of host publicationAdvanced Manufacturing and Automation
PublisherTrans Tech Publications Ltd
Pages169-176
Number of pages8
ISBN (Electronic)9783038352532, 9783038352532
DOIs
Publication statusPublished - 01-01-2014
Event4th International Workshop of Advanced Manufacturing and Automation, IWAMA 2014 - Shanghai, China
Duration: 27-10-201428-10-2014

Publication series

NameAdvanced Materials Research
Volume1039
ISSN (Print)1022-6680
ISSN (Electronic)1662-8985

Conference

Conference4th International Workshop of Advanced Manufacturing and Automation, IWAMA 2014
CountryChina
CityShanghai
Period27-10-1428-10-14

Fingerprint

Bearings (structural)
Wavelet transforms
Defects
Condition monitoring
Failure analysis
Entropy
Deep groove ball bearings
Preventive maintenance
Signal analysis
Fault detection
Machinery

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Kumar, H. S., Pai, S. P., Sriram, N. S., & Vijay, G. S. (2014). Selection of mother wavelet for effective wavelet transform of bearing vibration signals. In Advanced Manufacturing and Automation (pp. 169-176). (Advanced Materials Research; Vol. 1039). Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/AMR.1039.169
Kumar, H. S. ; Pai, Srinivasa P. ; Sriram, N. S. ; Vijay, G. S. / Selection of mother wavelet for effective wavelet transform of bearing vibration signals. Advanced Manufacturing and Automation. Trans Tech Publications Ltd, 2014. pp. 169-176 (Advanced Materials Research).
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Kumar, HS, Pai, SP, Sriram, NS & Vijay, GS 2014, Selection of mother wavelet for effective wavelet transform of bearing vibration signals. in Advanced Manufacturing and Automation. Advanced Materials Research, vol. 1039, Trans Tech Publications Ltd, pp. 169-176, 4th International Workshop of Advanced Manufacturing and Automation, IWAMA 2014, Shanghai, China, 27-10-14. https://doi.org/10.4028/www.scientific.net/AMR.1039.169

Selection of mother wavelet for effective wavelet transform of bearing vibration signals. / Kumar, H. S.; Pai, Srinivasa P.; Sriram, N. S.; Vijay, G. S.

Advanced Manufacturing and Automation. Trans Tech Publications Ltd, 2014. p. 169-176 (Advanced Materials Research; Vol. 1039).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Kumar HS, Pai SP, Sriram NS, Vijay GS. Selection of mother wavelet for effective wavelet transform of bearing vibration signals. In Advanced Manufacturing and Automation. Trans Tech Publications Ltd. 2014. p. 169-176. (Advanced Materials Research). https://doi.org/10.4028/www.scientific.net/AMR.1039.169