A State-Space Degradation Model with Multiple Observations and Different Sampling Times

doi:10.23940/ijpe.18.03.p17.567572

Abstract

Abstract:

A traditional state-space model (SSM) only contains one observation equation. There are some restricted conditions when using the traditional SSM to describe the evolution process between a state indicator and multiple observation indicators instantaneously. In order to solve this problem, this paper puts forward an SSM that has multiple observation equations, which can be applied to multiple observation indicators with different sampling times. The modeling process and parameters evaluation approach of the proposed SSM are studied and given. A simulation study is conducted to indicate advantages of the proposed SSM when sampling times and observation equations are not the same for different observation indicators. Simulation results show that the proposed SSM is more accurate than the traditional SSM in system degradation prediction.

Submitted on May 2, 2016; First revised on June 27, 2017; Second revised on December 3, 2017; Accepted on December 25, 2017
References: 9

Xianglong Ni, Xin Zhang, Jianmin Zhao, and Haiping Li. A State-Space Degradation Model with Multiple Observations and Different Sampling Times [J]. Int J Performability Eng, 2018, 14(3): 567-572.

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	G. Kordelas and P. Daras, “Robust SIFT-based feature matching using Kendall's rank correlation measure,” IEEE International Conference on Image Processing, pp. 325-328, 2009. (DOI: 10.1109/ICIP.2009.5413514)
	Z. Li, Z. J. He, Y. Y. Zi, and X. F. Chen, “Bearing Condition Monitoring Based on Shock Pulse Method and Improved Redundant Lifting Scheme,” Mathematics and Computers in Simulation, vol. 79, no. 3, pp. 318-338, 2008. (DOI: 10.1016/j.matcom.2007.12.004)
	H. K. Li, H. Li, S.J. Liu, and M. Cong, “Reliability Estimation Based on Moving Average and State Space Model for Rolling Element Bearing,” International Journal of Performability Engineering, vol. 11, no. 3, pp. 243-256, 2015.
	X. L. Ni, J. M. Zhao, J. C. Chen, and H. P. Li, “Reliability Modeling for Two-stage Degraded System Based on Cumulative Damage Model,” International Journal of Performability Engineering, vol. 12, no. 1, pp. 89-94, 2016.
	J. M. Noortwijk, “A Survey of the Application of Gamma Processes in Maintenance,” Reliability Engineering and System Safety, vol. 94, no. 1, pp.94:2-21, 2009. (DOI: 10.1016/j.ress.2007.03.019)
	M. E. Orchard and G. J. Vachtsevanos, “A Particle-Filtering Approach for On-line Fault Diagnosis and Failure Prognosis,” Transactions of the Institute of Measurement and Control, vol. 31, pp. 221-246, 2009.
	J. Z. Sun, H. F. Zuo, W. B. Wang, and M. G. Pecht, “Application of a State Space Modeling Technique to System Prognostics Based on a Health Index for Condition-Based Maintenance,” Mechanical Systems and Signal Processing, vol. 28, pp. 585-596, 2012. (DOI:10.1016/j.ymssp.2011.09.029)
	Y. F. Zhou, L. Ma, J. Mathew, Y. Sun, and R. Wolff, “Asset Life Prediction Using Multiple Degradation Indicators and Failure Events: a Continuous State Space Model Approach,” EKSPLOATACJA I NIEZAWODNOSC-Maintenance and Reliability, vol. 4, pp. 72-81, 2009.
	Y. F. Zhou, Y. Sun, J. Mathew, R. Wolff, and L. Ma, “Latent Degradation Indicators Estimation and Prediction: A Monte Carlo Approach,” Mechanical system and Signal Processing, vol. 25, no. 1, pp. 222-236, 2011. (DOI: 10.1016/j.ymssp.2010.08.012)