On Modeling of Repairable Systems with Multi-Output Gaussian Convolution Process

Di Cui; Min Xie; Qiuzhuang Sun

doi:10.1109/SRSE56746.2022.10067464

On Modeling of Repairable Systems with Multi-Output Gaussian Convolution Process

Department of Systems Engineering

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

Abstract

This paper focuses on the modeling of repairable systems using the multi-output Gaussian convolution process. We propose a statistical model that captures the heterogeneity of recurrent failure time data. We use a Bayesian framework for parameter estimation. A diesel engine system is used to illustrate the application of the model. © 2022 IEEE.

Original language	English
Title of host publication	2022 4th International Conference on System Reliability and Safety Engineering (SRSE 2022)
Publisher	IEEE
Pages	75-80
ISBN (Electronic)	978-1-6654-7388-0
DOIs	https://doi.org/10.1109/SRSE56746.2022.10067464
Publication status	Published - Dec 2022
Event	4th International Conference on System Reliability and Safety Engineering, SRSE 2022 - Guangzhou, China Duration: 15 Dec 2022 → 18 Dec 2022

Publication series

Name	International Conference on System Reliability and Safety Engineering, SRSE

Conference

Conference	4th International Conference on System Reliability and Safety Engineering, SRSE 2022
Country/Territory	China
City	Guangzhou
Period	15/12/22 → 18/12/22

Funding

This work is supported by the National Natural Science Foundation of China (71971181, 72032005, and 72071071) and by Research Grant Council of Hong Kong (11203519, 11200621). It is also funded by Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA).

Research Keywords

Bayesian statistics
heterogeneity
Multi-output Gaussian processes
repairable systems
trend-renewal processes

Access Output

10.1109/SRSE56746.2022.10067464

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Cite this

@inproceedings{e8fe7d31af4742aab2c1f7868b74c559,

title = "On Modeling of Repairable Systems with Multi-Output Gaussian Convolution Process",

abstract = "This paper focuses on the modeling of repairable systems using the multi-output Gaussian convolution process. We propose a statistical model that captures the heterogeneity of recurrent failure time data. We use a Bayesian framework for parameter estimation. A diesel engine system is used to illustrate the application of the model. {\textcopyright} 2022 IEEE.",

keywords = "Bayesian statistics, heterogeneity, Multi-output Gaussian processes, repairable systems, trend-renewal processes",

author = "Di Cui and Min Xie and Qiuzhuang Sun",

year = "2022",

month = dec,

doi = "10.1109/SRSE56746.2022.10067464",

language = "English",

series = "International Conference on System Reliability and Safety Engineering, SRSE",

publisher = "IEEE",

pages = "75--80",

booktitle = "2022 4th International Conference on System Reliability and Safety Engineering (SRSE 2022)",

address = "United States",

note = "4th International Conference on System Reliability and Safety Engineering, SRSE 2022 ; Conference date: 15-12-2022 Through 18-12-2022",

}

Cui, D , Xie, M & Sun, Q 2022, On Modeling of Repairable Systems with Multi-Output Gaussian Convolution Process. in 2022 4th International Conference on System Reliability and Safety Engineering (SRSE 2022). International Conference on System Reliability and Safety Engineering, SRSE, IEEE, pp. 75-80, 4th International Conference on System Reliability and Safety Engineering, SRSE 2022, Guangzhou, China, 15/12/22. https://doi.org/10.1109/SRSE56746.2022.10067464

On Modeling of Repairable Systems with Multi-Output Gaussian Convolution Process. / Cui, Di ; Xie, Min; Sun, Qiuzhuang.
2022 4th International Conference on System Reliability and Safety Engineering (SRSE 2022). IEEE, 2022. p. 75-80 (International Conference on System Reliability and Safety Engineering, SRSE).

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

TY - GEN

T1 - On Modeling of Repairable Systems with Multi-Output Gaussian Convolution Process

AU - Cui, Di

AU - Xie, Min

AU - Sun, Qiuzhuang

PY - 2022/12

Y1 - 2022/12

N2 - This paper focuses on the modeling of repairable systems using the multi-output Gaussian convolution process. We propose a statistical model that captures the heterogeneity of recurrent failure time data. We use a Bayesian framework for parameter estimation. A diesel engine system is used to illustrate the application of the model. © 2022 IEEE.

AB - This paper focuses on the modeling of repairable systems using the multi-output Gaussian convolution process. We propose a statistical model that captures the heterogeneity of recurrent failure time data. We use a Bayesian framework for parameter estimation. A diesel engine system is used to illustrate the application of the model. © 2022 IEEE.

KW - Bayesian statistics

KW - heterogeneity

KW - Multi-output Gaussian processes

KW - repairable systems

KW - trend-renewal processes

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DO - 10.1109/SRSE56746.2022.10067464

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

T3 - International Conference on System Reliability and Safety Engineering, SRSE

SP - 75

EP - 80

BT - 2022 4th International Conference on System Reliability and Safety Engineering (SRSE 2022)

PB - IEEE

T2 - 4th International Conference on System Reliability and Safety Engineering, SRSE 2022

Y2 - 15 December 2022 through 18 December 2022

ER -

On Modeling of Repairable Systems with Multi-Output Gaussian Convolution Process

Abstract

Publication series

Conference

Funding

Research Keywords

Access Output

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GRF: New Approaches for Reliability Analysis of Industrial Systems Subject to Multivariate Degradation

GRF: Importance Analysis and Maintenance Decisions of Complex Systems with Dependent Components

Cite this

On Modeling of Repairable Systems with Multi-Output Gaussian Convolution Process

Abstract

Publication series

Conference

Funding

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

GRF: New Approaches for Reliability Analysis of Industrial Systems Subject to Multivariate Degradation

GRF: Importance Analysis and Maintenance Decisions of Complex Systems with Dependent Components

Cite this