Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

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

Volume 14, Number 3, March 2018, pp. 567-572
DOI: 10.23940/ijpe.18.03.p17.567572

Xianglong Ni, Xin Zhang, Jianmin Zhao, and Haiping Li

Mechanical Engineering College, Shijiazhuang, 050003, China

(Submitted on May 2, 2016; First revised on June 27, 2017; Second revised on December 3, 2017; Accepted on December 25, 2017)


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.

 

References: 9

  1. 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)
  2. 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)
  3. 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.
  4. 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.
  5. 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)
  6. 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.
  7. 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)
  8. 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.
  9. 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)

 

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

Attachments:
Download this file (IJPE-2018-03-17.pdf)IJPE-2018-03-17.pdf[A State-Space Degradation Model with Multiple Observations and Different Sampling Times]264 Kb
 

CURRENT ISSUE

Prev Next

Temporal Multiscale Consumption Strategies of Intermittent Energy based on Parallel Computing

Huifen Chen, Yiming Zhang, Feng Yao, Zhice Yang, Fang Liu, Yi Liu, Zhiheng Li, and Jinggang Wang

Read more

Decision Tree Incremental Learning Algorithm Oriented Intelligence Data

Hongbin Wang, Ci Chu, Xiaodong Xie, Nianbin Wang, and Jing Sun

Read more

Spark-based Ensemble Learning for Imbalanced Data Classification

Jiaman Ding, Sichen Wang, Lianyin Jia, Jinguo You, and Ying Jiang

Read more

Classification Decision based on a Hybrid Method of Weighted kNN and Hyper-Sphere SVM

Peng Chen, Guoyou Shi, Shuang Liu, Yuanqiang Zhang, and Denis Špelič

Read more

An Improved Algorithm based on Time Domain Network Evolution

Guanghui Yan, Qingqing Ma, Yafei Wang, Yu Wu, and Dan Jin

Read more

Auto-Tuning for Solving Multi-Conditional MAD Model

Feng Yao, Yi Liu, Huifen Chen, Chen Li, Zhonghua Lu, Jinggang Wang, Zhiheng Li, and Ningming Nie

Read more

Smart Mine Construction based on Knowledge Engineering and Internet of Things

Xiaosan Ge, Shuai Su, Haiyang Yu, Gang Chen, and Xiaoping Lu

Read more

A Mining Model of Network Log Data based on Hadoop

Yun Wu, Xin Ma, Guangqian Kong, Bin Wang, and Xinwei Niu

Read more
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com