A novel diagnostic and prognostic framework for incipient fault detection and remaining service life prediction with application to industrial rotating machines

Xiaochuan Li; Xiaoyu Yang; Yingjie Yang; Ian Bennett; David Mba

doi:10.1016/j.asoc.2019.105564

A novel diagnostic and prognostic framework for incipient fault detection and remaining service life prediction with application to industrial rotating machines

Xiaochuan Li
, Xiaoyu Yang^*
, Yingjie Yang
, Ian Bennett
, David Mba

^*Corresponding author for this work

De Montfort University
Nanjing University of Aeronautics and Astronautics
Shell Centre
University of Nigeria

Research output: Contribution to journal › Article › peer-review

47 Citations (SciVal)

Original language	English
Article number	105564
Journal	Applied Soft Computing
Volume	82
DOIs	https://doi.org/10.1016/j.asoc.2019.105564
Publication status	Published (VoR) - Sept 2019

Funding

This work was supported by the National Natural Science Foundation of China (No. 71671091 , 71701098 ), the China Scholarship council , Postgraduate Research & Practice Innovation Program of Jiangsu Province, China ( KYCX18_0233 ), and the Leverhulme Trust International Research Network project ( IN-2014-020 ).

Keywords

Canonical variable analysis
Condition monitoring
Diagnosis and prognosis
Grey forecasting model
Particle filter

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10.1016/j.asoc.2019.105564

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title = "A novel diagnostic and prognostic framework for incipient fault detection and remaining service life prediction with application to industrial rotating machines",

keywords = "Canonical variable analysis, Condition monitoring, Diagnosis and prognosis, Grey forecasting model, Particle filter",

author = "Xiaochuan Li and Xiaoyu Yang and Yingjie Yang and Ian Bennett and David Mba",

note = "Funding Information: This work was supported by the National Natural Science Foundation of China (No. 71671091 , 71701098 ), the China Scholarship council , Postgraduate Research \& Practice Innovation Program of Jiangsu Province, China ( KYCX18\_0233 ), and the Leverhulme Trust International Research Network project ( IN-2014-020 ). Publisher Copyright: {\textcopyright} 2019 Elsevier B.V.",

year = "2019",

month = sep,

doi = "10.1016/j.asoc.2019.105564",

language = "English",

volume = "82",

journal = "Applied Soft Computing",

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TY - JOUR

T1 - A novel diagnostic and prognostic framework for incipient fault detection and remaining service life prediction with application to industrial rotating machines

AU - Li, Xiaochuan

AU - Yang, Xiaoyu

AU - Yang, Yingjie

AU - Bennett, Ian

AU - Mba, David

N1 - Funding Information: This work was supported by the National Natural Science Foundation of China (No. 71671091 , 71701098 ), the China Scholarship council , Postgraduate Research & Practice Innovation Program of Jiangsu Province, China ( KYCX18_0233 ), and the Leverhulme Trust International Research Network project ( IN-2014-020 ). Publisher Copyright: © 2019 Elsevier B.V.

PY - 2019/9

Y1 - 2019/9

KW - Canonical variable analysis

KW - Condition monitoring

KW - Diagnosis and prognosis

KW - Grey forecasting model

KW - Particle filter

UR - https://www.scopus.com/pages/publications/85067627606

UR - https://www.scopus.com/pages/publications/85067627606#tab=citedBy

U2 - 10.1016/j.asoc.2019.105564

DO - 10.1016/j.asoc.2019.105564

M3 - Article

AN - SCOPUS:85067627606

SN - 1568-4946

VL - 82

JO - Applied Soft Computing

JF - Applied Soft Computing

M1 - 105564

ER -

A novel diagnostic and prognostic framework for incipient fault detection and remaining service life prediction with application to industrial rotating machines

Funding

Keywords

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