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Condition monitoring scheme via one-class support vector machine and multivariate control charts

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Abstract

A condition-based maintenance (CBM) has been widely employed to reduce maintenance cost by predicting the health status of many complex systems in prognostics and health management (PHM) framework. Recently, multivariate control charts used in statistical process control (SPC) have been actively introduced as monitoring technology. In this paper, we propose a condition monitoring scheme to monitor the health status of the system of interest. In our condition monitoring scheme, we first define reference data set using one-class support vector machine (OC-SVM) to construct the control limit of multivariate control charts in phase I. Then, parametric control chart or non-parametric control chart is selected according to the results from multivariate normality tests. The proposed condition monitoring scheme is applied to sensor data of two anemometers to evaluate the performance of fault detection power.

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Abbreviations

x :

Train data

y :

New observation data

z :

Reference data

m :

Number of observations

p :

Number of variable

ξ i :

Non-zero slack variable

V :

Upper bound on the fraction of outliners

αi, βi :

Lagrangian multiplier

k(·):

Kernel function

μ :

Mean vector

Σ:

Variance-covariance matrix

\(\overline z \) :

Sample mean vector

S :

Sample variance-covariance matrix

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Acknowledgments

This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20174030201750), and the research fund of Hanyang University (HY-2018, No. 201800000002372).

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Authors and Affiliations

  1. Department of Industrial Engineering, Hanyang University, Seoul, Korea

    Byeong Min Mun, Munwon Lim & Suk Joo Bae

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  1. Byeong Min Mun
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  2. Munwon Lim
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  3. Suk Joo Bae
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Corresponding author

Correspondence to Suk Joo Bae.

Additional information

Byeong Min Mun received the Ph.D. degree from the Department of Industrial Engineering, Hanyang University, Seoul, Korea. He is a Research Professor in the Department of Industrial Engineering at Hanyang University, Seoul, Korea. His current research interests include reliability, big data, and artificial intelligence.

Munwon Lim is in Ph.D. course in Department of Industrial Engineering, Hanyang University, Seoul, Korea. Her current research interests include signal processing, data mining, prognostics and health management.

Suk Joo Bae received the Ph.D. degree from the School of Industrial and Systems Engineering at the Georgia Institute of Technology, Atlanta, GA, USA, in 2003. He is a Professor in the Department of Industrial Engineering at Hanyang University, Seoul. He published more than 70 papers in journals such as Technometrics, Journal of Quality Technology, IIE Transactions, IEEE Transactions on Reliability, and Reliability Engineering & System Safety.

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Mun, B.M., Lim, M. & Bae, S.J. Condition monitoring scheme via one-class support vector machine and multivariate control charts. J Mech Sci Technol 34, 3937–3944 (2020). https://doi.org/10.1007/s12206-020-2203-z

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