Gear crack level classification based on multinomial logit model and cumulative link model

Yizhen Hai; Kwok-Leung Tsui; Ming J. Zuo

doi:10.1109/PHM.2012.6228904

Gear crack level classification based on multinomial logit model and cumulative link model

Yizhen Hai
, Kwok-Leung Tsui
, Ming J. Zuo

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

10 Citations (Scopus)

Abstract

In order to avoid machine related catastrophes, the early detection of cracks is in urgent demand. Sensors are put into the rotating parts of machine and vibration signal data are collected to diagnose machine health. This paper proposes a comprehensive method to look into the development of damage with multinomial logit model (MLM) and cumulative link model (CLM). We first select features according to analysis of variance (ANOVA), and then compare the MLM, CLM method with weighted k-nearest neighbor method (WKNN) - a black box machine learning algorithm and we conclude that these methods have their pros and cons in the diagnosis of faults. © 2012 IEEE.

Original language	English
Title of host publication	Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012
DOIs	https://doi.org/10.1109/PHM.2012.6228904
Publication status	Published - 2012
Event	2012 3rd Annual IEEE Prognostics and System Health Management Conference, PHM-2012 - Beijing, China Duration: 23 May 2012 → 25 May 2012

Conference

Conference	2012 3rd Annual IEEE Prognostics and System Health Management Conference, PHM-2012
Place	China
City	Beijing
Period	23/05/12 → 25/05/12

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10.1109/PHM.2012.6228904

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@inproceedings{7814e7de482244efb2a513d819ada604,

title = "Gear crack level classification based on multinomial logit model and cumulative link model",

abstract = "In order to avoid machine related catastrophes, the early detection of cracks is in urgent demand. Sensors are put into the rotating parts of machine and vibration signal data are collected to diagnose machine health. This paper proposes a comprehensive method to look into the development of damage with multinomial logit model (MLM) and cumulative link model (CLM). We first select features according to analysis of variance (ANOVA), and then compare the MLM, CLM method with weighted k-nearest neighbor method (WKNN) - a black box machine learning algorithm and we conclude that these methods have their pros and cons in the diagnosis of faults. {\textcopyright} 2012 IEEE.",

author = "Yizhen Hai and Kwok-Leung Tsui and Zuo, \{Ming J.\}",

year = "2012",

doi = "10.1109/PHM.2012.6228904",

language = "English",

booktitle = "Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012",

note = "2012 3rd Annual IEEE Prognostics and System Health Management Conference, PHM-2012 ; Conference date: 23-05-2012 Through 25-05-2012",

}

Hai, Y, Tsui, K-L & Zuo, MJ 2012, Gear crack level classification based on multinomial logit model and cumulative link model. in Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012., 6228904, 2012 3rd Annual IEEE Prognostics and System Health Management Conference, PHM-2012, Beijing, China, 23/05/12. https://doi.org/10.1109/PHM.2012.6228904

Gear crack level classification based on multinomial logit model and cumulative link model. / Hai, Yizhen; Tsui, Kwok-Leung; Zuo, Ming J.
Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012. 2012. 6228904.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Gear crack level classification based on multinomial logit model and cumulative link model

AU - Hai, Yizhen

AU - Tsui, Kwok-Leung

AU - Zuo, Ming J.

PY - 2012

Y1 - 2012

N2 - In order to avoid machine related catastrophes, the early detection of cracks is in urgent demand. Sensors are put into the rotating parts of machine and vibration signal data are collected to diagnose machine health. This paper proposes a comprehensive method to look into the development of damage with multinomial logit model (MLM) and cumulative link model (CLM). We first select features according to analysis of variance (ANOVA), and then compare the MLM, CLM method with weighted k-nearest neighbor method (WKNN) - a black box machine learning algorithm and we conclude that these methods have their pros and cons in the diagnosis of faults. © 2012 IEEE.

AB - In order to avoid machine related catastrophes, the early detection of cracks is in urgent demand. Sensors are put into the rotating parts of machine and vibration signal data are collected to diagnose machine health. This paper proposes a comprehensive method to look into the development of damage with multinomial logit model (MLM) and cumulative link model (CLM). We first select features according to analysis of variance (ANOVA), and then compare the MLM, CLM method with weighted k-nearest neighbor method (WKNN) - a black box machine learning algorithm and we conclude that these methods have their pros and cons in the diagnosis of faults. © 2012 IEEE.

UR - https://www.scopus.com/pages/publications/84866164294

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84866164294&origin=recordpage

U2 - 10.1109/PHM.2012.6228904

DO - 10.1109/PHM.2012.6228904

M3 - RGC 32 - Refereed conference paper (with host publication)

BT - Proceedings of IEEE 2012 Prognostics and System Health Management Conference, PHM-2012

T2 - 2012 3rd Annual IEEE Prognostics and System Health Management Conference, PHM-2012

Y2 - 23 May 2012 through 25 May 2012

ER -

Gear crack level classification based on multinomial logit model and cumulative link model

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