The multi-state reliability analysis has received great attention recently in the domain of reliability and maintenance, specifically for mechanical equipment operating under stress, load, and fatigue conditions. The overall performance of this type of mechanical equipment deteriorates over time, which may result in multi-state health conditions. This deterioration can be represented by a continuous-time degradation process with multiple discrete states. In reality, due to technical problems, directly observing the actual health condition of the equipment may not be possible. In such cases, condition monitoring information may be useful to estimate the actual health condition of the equipment. In this chapter, the authors describe the application of a general stochastic process to multi-state equipment modeling. Also, an unsupervised learning method is presented to estimate the parameters of this stochastic model from condition monitoring data.
|Original language||English (US)|
|Title of host publication||Diagnostics and Prognostics of Engineering Systems|
|Subtitle of host publication||Methods and Techniques|
|Number of pages||31|
|State||Published - 2012|
ASJC Scopus subject areas