On-line Fault Diagnosis of Electric Machine based on the Hidden Markov Model Conference Paper

abstract

• 2016 IEEE. Extensive work has been presented in the literature related to fault diagnosis and prognosis of machines and related components. With machine learning and statistics applied, methods are proposed in a novel perspective. In this work, the statistics model based on Hidden Markov Model(HMM) is selected because of its wide and strong applications on fault diagnostics in the industry. Based on this method, with the model well-trained, it can encompass the traditional difficulties such as building an exact mathematical model or complex parameters estimation. As the machine fault progresses, the fault features are projected on a certain class and therefore the fault and its severity are identified. Its theoretical base and practical performance are presented and its strength on fault tolerant operation are further come up with.

name of conference

• 2016 IEEE Transportation Electrification Conference and Expo (ITEC)

authors

• Ehsani, Mehrdad
• Zhan, Wei

published proceedings

• 2016 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE AND EXPO (ITEC)

author list (cited authors)

• Zhang, J., Zhan, W., & Ehsani, M.

citation count

• 3

complete list of authors

• Zhang, Jiayuan||Zhan, Wei||Ehsani, Mehrdad

publication date

• January 2016

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Diagnosis
• Electric Machine
• Fault Degree
• Fault Tolerant Operation
• Hmm

Digital Object Identifier (DOI)

• 10.1109/itec.2016.7520276

International Standard Book Number (ISBN) 13

• 9781509004034

start page

• 1

end page

• 7

URL

• http://dx.doi.org/10.1109/itec.2016.7520276