A NON-GAUSSIAN CONTINUOUS STATE SPACE MODEL FOR ASSET DEGRADATION
The degradation model plays an essential role in asset life prediction and condition based maintenance. Various degradation models have been proposed. Within these models, the state space model has the ability to combine degradation data and failure event data. The state space model is also an effective approach to deal with the multiple observations and missing data issues. Using the state space degradation model, the deterioration process of assets is presented by a system state process which can be revealed by a sequence of observations. Current research largely assumes that the underlying system development process is discrete in time or states. Although some models have been developed to consider continuous time and space, these state space models are based on the Wiener process with the Gaussian assumption. This paper proposes a Gamma-based state space degradation model in order to remove the Gaussian assumption. Both condition monitoring observations and failure events are considered in the model so as to improve the accuracy of asset life prediction. A simulation study is carried out to illustrate the application procedure of the proposed model.
State space model Degradation model Gamma process Particle smoother
Yifan Zhou Lin Ma Yong Sun Joseph Mathew
CRC of Integrated Engineering Asset Management (CIEAM), School of Engineering Systems, Faculty of Built Environment and Engineering, Queensland University of Technology, Brisbane, Australia
国际会议
北京
英文
1981-1992
2008-10-27（万方平台首次上网日期，不代表论文的发表时间）