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One-class support vector machines with a bias constraint and its application in system reliability prediction

Published online by Cambridge University Press:  03 May 2019

Zhengwei Hu ,
Zhangli Hu  and
Xiaoping Du
Zhengwei Hu
Affiliation:
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
Zhangli Hu
Affiliation:
Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
Xiaoping Du*
Affiliation:
Department of Mechanical and Energy Engineering, Indiana University – Purdue University Indianapolis, Indianapolis, IN 46202, USA
*
Author for correspondence: Xiaoping Du, E-mail: [email protected]
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Abstract

Support vector machine (SVM) methods are widely used for classification and regression analysis. In many engineering applications, only one class of data is available, and then one-class SVM methods are employed. In reliability applications, the one-class data may be failure data since the data are recorded during reliability experiments when only failures occur. Different from the problems handled by existing one-class SVM methods, there is a bias constraint in the SVM model in this work and the constraint comes from the probability of failure estimated from the failure data. In this study, a new one-class SVM regression method is proposed to accommodate the bias constraint. The one class of failure data is maximally separated from a hypersphere whose radius is determined by the known probability of failure. The proposed SVM method generates regression models that directly link the states of failure modes with design variables, and this makes it possible to obtain the joint probability density of all the component states of an engineering system, resulting in a more accurate prediction of system reliability during the design stage. Three examples are given to demonstrate the effectiveness of the new one-class SVM method.

Keywords

First-order reliability methodoptimizationsupport vector machinessystem reliabilitystatistical dependence
Type
Research Article
Information
AI EDAM , Volume 33 , Issue 3 , August 2019 , pp. 346 - 358
Copyright
Copyright © Cambridge University Press 2019 

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References

Akbani, R, Kwek, S and Japkowicz, N (2004) Applying support vector machines to imbalanced datasets. Machine Learning: ECML 2004, 39–50.Google Scholar
Boser, BE, Guyon, IM and Vapnik, VN (1992) A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory, pp. 144–152. ACM.Google Scholar
Chen, K-Y, Chen, L-S, Chen, M-C and Lee, C-L (2011) Using SVM based method for equipment fault detection in a thermal power plant. Computers in Industry 62, 42–50.Google Scholar
Cheng, Y and Du, X (2016) System reliability analysis with dependent component failures during early design stage—a feasibility study. Journal of Mechanical Design 138, 051405.Google Scholar
Chiralaksanakul, A and Mahadevan, S (2005) First-order approximation methods in reliability-based design optimization. Journal of Mechanical Design 127, 851–857.Google Scholar
Click, RL and Duening, TN (2004) Business Process Outsourcing: The Competitive Advantage. Hoboken, NJ: John Wiley & Sons.Google Scholar
Cortez, P (2010) Data mining with neural networks and support vector machines using the R/rminer tool. In Advances in Data Mining. Applications and Theoretical Aspects, Part of the Lecture Notes in Computer Science book series (LNCS, volume 6171), pp. 572–583.Google Scholar
Cruse, TA (1997) Reliability-Based Mechanical Design. Boca Raton, FL: CRC Press.Google Scholar
das Chagas Moura, M, Zio, E, Lins, ID and Droguett, E (2011) Failure and reliability prediction by support vector machines regression of time series data. Reliability Engineering & System Safety 96, 1527–1534.Google Scholar
Dreiseitl, S, Osl, M, Scheibböck, C and Binder, M (2010) Outlier detection with one-class SVMs: An application to melanoma prognosis. AMIA Annual Symposium Proceedings, Vol. 2010, p. 172. American Medical Informatics Association.Google Scholar
Du, X and Sudjianto, A (2004) First order saddlepoint approximation for reliability analysis. AIAA journal 42, 1199–1207.Google Scholar
Frias-Martinez, E, Sanchez, A and Velez, J (2006) Support vector machines versus multi-layer perceptrons for efficient off-line signature recognition. Engineering Applications of Artificial Intelligence 19, 693–704.Google Scholar
Green, RC, Wang, L, Alam, M and Singh, C (2013) Intelligent state space pruning for Monte Carlo simulation with applications in composite power system reliability. Engineering Applications of Artificial Intelligence 26, 1707–1724.Google Scholar
Gryllias, KC and Antoniadis, IA (2012) A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Engineering Applications of Artificial Intelligence 25, 326–344.Google Scholar
Hanna, S (2007) Inductive machine learning of optimal modular structures: estimating solutions using support vector machines. AI EDAM 21, 351–366.Google Scholar
Hoyland, A and Rausand, M (2004) System Reliability Theory: Models, Statistical Methods, and Applications. Hoboken, NJ: Wiley-Interscience.Google Scholar
Hu, Z and Du, X (2016) A physics-based reliability method for components adopted in new series systems. 2016 Annual Reliability and Maintainability Symposium (RAMS), pp. 1–7. IEEE.Google Scholar
Hu, Z and Du, X (2017 a) System reliability analysis with in-house and outsourced components. System Reliability and Safety (ICSRS), 2017 2nd International Conference on, pp. 146–150. IEEE.Google Scholar
Hu, Z and Du, X (2017 b) System reliability prediction with shared load and unknown component design details. AI EDAM 31, 223–234.Google Scholar
Hu, Z and Du, X (2018 a) Integration of statistics- and physics-based methods – A feasibility study on accurate system reliability prediction. Journal of Mechanical Design 140, 074501.Google Scholar
Hu, Z and Du, X (2018 b) A partial safety factor method for system reliability prediction with outsourced components. ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, pp. V02BT03A050–V002BT003A050. American Society of Mechanical Engineers.Google Scholar
Hu, Z and Du, X (2018 c) Saddlepoint approximation reliability method for quadratic functions in normal variables. Structural Safety 71, 24–32.Google Scholar
Hu, Z and Du, X (2019) An exploratory study for predicting component reliability with new load conditions. Frontiers of Mechanical Engineering 14, 76–84.Google Scholar
Hu, Z, Nannapaneni, S and Mahadevan, S (2017) Efficient kriging surrogate modeling approach for system reliability analysis. AI EDAM 31, 143–160.Google Scholar
Lawless, J (1983) Statistical methods in reliability. Technometrics 25, 305–316.Google Scholar
Li, H-s, Lü, Z-z and Yue, Z-f (2006) Support vector machine for structural reliability analysis. Applied Mathematics and Mechanics 27, 1295–1303.Google Scholar
Ma, J and Perkins, S (2003). Time-series novelty detection using one-class support vector machines. Neural Networks, 2003. Proceedings of the International Joint Conference on, Vol. 3, pp. 1741–1745. IEEE.Google Scholar
Mahadevan, S (1997) Physics-based reliability models. In Cruse, TA (ed.), Reliability-based Mechanical Design. Boca Raton, FL: CRC Press, pp. 197–232.Google Scholar
Mahadevan, S and Shah, SL (2009) Fault detection and diagnosis in process data using one-class support vector machines. Journal of Process Control 19, 1627–1639.Google Scholar
Manevitz, LM and Yousef, M (2001) One-class SVMs for document classification. Journal of Machine Learning Research 2, 139–154.Google Scholar
Meeker, WQ and Escobar, LA (2014). Statistical Methods for Reliability Data. Hoboken, NJ: John Wiley & Sons.Google Scholar
Peng, X (2011) TPMSVM: a novel twin parametric-margin support vector machine for pattern recognition. Pattern Recognition 44, 2678–2692.Google Scholar
Rosenblatt, M (1952) Remarks on a multivariate transformation. The Annals of Mathematical Statistics 23, 470–472.Google Scholar
Schölkopf, B, Platt, JC, Shawe-Taylor, J, Smola, AJ and Williamson, RC (2001) Estimating the support of a high-dimensional distribution. Neural Computation 13, 1443–1471.Google Scholar
Tian, Y, Shi, Y and Liu, X (2012) Recent advances on support vector machines research. Technological and Economic Development of Economy 18, 5–33.Google Scholar
Truong, TX and Kim, J-M (2012) Fire flame detection in video sequences using multi-stage pattern recognition techniques. Engineering Applications of Artificial Intelligence 25, 1365–1372.Google Scholar
Vapnik, V (2013) The Nature of Statistical Learning Theory. New York: Springer Science & Business Media.Google Scholar
Vapnik, VN and Vapnik, V (1998). Statistical Learning Theory. New York: Wiley.Google Scholar
Wang, H-Q, Cai, Y-N, Fu, G-Y, Wu, M and Wei, Z-H (2018) Data-driven fault prediction and anomaly measurement for complex systems using support vector probability density estimation. Engineering Applications of Artificial Intelligence 67, 1–13.Google Scholar
Yong Cang, Z (1993) High-order reliability bounds for series systems and application to structural systems. Computers and Structures 46, 381–386.Google Scholar
Zhao, YG and Ono, T (1999) A general procedure for first/second-order reliability method (FORM/SORM). Structural Safety 21, 95–112.Google Scholar