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
References
N. H. Kim, D. An and J. H. Choi, Prognostics and Health Management of Engineering Systems: An Introduction, Springer (2016).
A. K. S. Jardine, D. Lin and D. Banjevic, A review on machinery diagnostics and prognostics implementing condition-based maintenance, Mechanical Systems and Signal Processing, 20 (2006) 1483–1510.
H. Hotelling, Multivariate quality control, Techniques of Statistical Analysis, McGraw-Hill, New York (1947).
C. A. Lowry, W. H. Woodall, C. W. Champ and S. E. Rigdon, A multivariate EWMA control charts, Technometrics, 34 (1992) 46–53.
W. H. Woodall and M. M. Ncube, Multivariate CUSUM quality control procedures, Technometrics, 27 (1985) 285–292.
C. A. Lowry and D. C. Montgomery, A review of multivariate control charts, IIE Transactions, 27 (1995) 800–810.
A. J. Hayter and K. L. Tsui, Identification and quantification in multivariate quality control problems, Journal of Quality Technology, 26 (1994) 197–208.
R. Sun and F. Tsung, A kernel distance based multivariate control chart using support vector methods, International Journal of Production Research, 41 (2003) 2975–2989.
S. J. Bae, G. Do and P. Kvam, On data depth and the application of nonparametric multivariate statistical process control charts, Applied Stochastic Models in Business and Industry, 32 (2016) 660–676.
T. Sukchotrat, S. B. Kim and F. Tsung, One-class classification-based control charts for multivariate process monitoring, IIE Transactions, 42 (2010) 107–120.
S. He, W. Jiang and H. Deng, A distance-based control chart for monitoring multivariate processes using support vector machines, Annals of Operations Research, 263 (2018) 191–207.
H. Rasay, M. S. Fallahnezhad and Y. Zaremehrjerdi, Application of multivariate control charts for condition based maintenance, International Journal of Engineering, 31 (2018) 597–604.
S. J. Bae, B. M. Mun, W. Chang and B. Vidakovic, Condition monitoring of a steam turbine generator using wavelet spectrum based control chart, Reliability Engineering and System Safety, 184 (2019) 13–20.
V. Vapnik, Statistical Learning Theory, Wiley, New York (1998).
A. Karatzoglou, D. Meyer and K. Hornik, Support vector machines in R, Journal of Statistical Software, 15(9) (2006) 1–28.
B. Schölkopf, A. J. Smola, R. C. Williamson and P. L. Bartlett, New support vector algorithms, Neural Computation, 12 (2000) 1207–1245.
D. M. J. Tax and R. P. W. Duin, Support vector domain description, Pattern Recognition Letters, 20 (1999) 1191–1199.
D. M. J. Tax and R. P. W. Duin, Support vector data description, Machine Learning, 54 (2004) 45–66.
B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola and R. C. Williamson, Estimating the support of a high-dimensional distribution, Neural Computation, 13 (2001) 1443–1471.
R. L. Mason and J. C. Young, Multivariate Statistical Process Control with Industrial Applications, American Statistical Association and Society for Industrial and Applied Mathematics, Philadelphia, PA (2002).
R. Y. Liu, Control charts for multivariate processes, Journal of the American Statistical Association, 90 (1995) 1380–1387.
J. W. Tukey, Mathematics and the picturing of data, Proceedings of the International Congress of Mathematicians, Vancouver, 2 (1975) 523–531.
Y. Zuo and R. Serfling, General notions of statistical depth function, Annals of Statistics, 28 (2000) 461–482.
P. C. Mahalanobis, On the Generalized Distance in Statistics, National Institute of Science of India (1936).
F. B. Alt, Multivariate quality control, The Encyclopedia of Statistical Science, John Wiley, New York (1984).
J. E. Jackson, Multivariate quality control, Communications in Statistics — Theory and Methods, 14 (1985) 2657–2688.
K. V. Mardia, Measures of multivariate skewness and kurtosis with applications, Biometrika, 57 (1970) 519–530.
N. Henze and B. Zirkler, A class of invariant consistent tests for multivariate normality, Communications in Statistics — Theory and Methods, 19 (1990) 3595–3617.
L. Sun, C. Chen and Q. Cheng, Feature extraction and pattern identification for anemometer condition diagnosis, International Journal of Prognostics and Health Management, 3 (2012) 8–18.
B. Zong, Q. Song, M. R. Min, W. Cheng, C. Lumezanu, D. Cho and H. Chen, Deep autoencoding gaussian mixture model for unsupervised anomaly detection, 6th International Conference on Learning Representations (2018).
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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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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DOI: https://doi.org/10.1007/s12206-020-2203-z