Reliability analysis of digital feedwater regulating valve controller system using a semi-Markov process model

2011 ◽  
Vol 6 (4) ◽  
pp. 298 ◽  
Author(s):  
Arun Veeramany ◽  
Mahesh D. Pandey
Keyword(s):  
Markov Process ◽  
Reliability Analysis ◽  
Process Model ◽  
Markov Process Model ◽  
Semi Markov Process

scholarly journals Evaluation of a method for fitting a semi-Markov process model in the presence of left-censored spells using the Cardiovascular Health Study

2008 ◽  
Vol 27 (26) ◽  
pp. 5509-5524 ◽  
Author(s):  
Liming Cai ◽  
Nathaniel Schenker ◽  
James Lubitz ◽  
Paula Diehr ◽  
Alice Arnold ◽  
...  
Keyword(s):  
Markov Process ◽  
Process Model ◽  
Cardiovascular Health ◽  
Health Study ◽  
Cardiovascular Health Study ◽  
Markov Process Model ◽  
Semi Markov Process

scholarly journals Developing A Semi-Markov Process Model for Bridge Deterioration Prediction in Shanghai

2019 ◽  
Vol 11 (19) ◽  
pp. 5524 ◽  
Author(s):  
Yu Fang ◽  
Lijun Sun
Keyword(s):  
Markov Process ◽  
Service Life ◽  
Life Expectancy ◽  
Weibull Distribution ◽  
Process Model ◽  
Transition Probabilities ◽  
Bridge Management ◽  
Markov Process Model ◽  
The Future ◽  
Semi Markov Process

The performance of urban bridges will deteriorate gradually throughout service life. Bridge deterioration prediction is essential for bridge management, especially for maintenance planning and decision-making. By considering the time-dependent reliability in the bridge deterioration process, a Weibull distribution based semi-Markov process model for urban bridge deterioration prediction was proposed in this paper. Historical inspection records stored in the Bridge Manage System (BMS) database in Shanghai since 2004 were investigated. The Weibull distribution was used to characterize the bridge deterioration behavior within each condition rating (CR), and the semi-Markov process was used to calculate the bridge transition probabilities between adjacent CRs. After that, the service life expectancy of urban bridges, the transition probabilities of the deck system and the substructure, and the future CR proportion change caused by deterioration was predicted. The prediction results indicate that the life expectancy of concrete beam bridges is about 77 years. The decay rate of the deck system is the fastest among three major parts, and the substructure has a much longer life expectancy. It suggests that the overall prediction accuracy of the semi-Markov model in network-level is better than the regression analysis method. Furthermore, the proportion of bridges in intact condition will gradually decrease in the next few decades, while the percentage of bridges in the qualified and bad state will increase rapidly. The prediction results show a good agreement with the actual deterioration trend of the urban bridges in Shanghai. In order to alleviate the pressure of bridge maintenance in the future, it is necessary to adopt a more targeted preventive maintenance strategy.

A semi-Markov process model for migration of marine shrimp

1992 ◽  
Vol 60 (3-4) ◽  
pp. 167-184 ◽  
Author(s):  
J.H. Matis ◽  
W.E. Grant ◽  
T.H. Miller
Keyword(s):  
Markov Process ◽  
Process Model ◽  
Markov Process Model ◽  
Marine Shrimp ◽  
Semi Markov Process

Redundancy Allocation for Multistate Systems With Component Dependencies and Load Sharing

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Jing Wang ◽  
Mian Li
Keyword(s):  
Markov Process ◽  
Process Model ◽  
Load Sharing ◽  
Two Component System ◽  
System Availability ◽  
Redundancy Allocation ◽  
Markov Process Model ◽  
Dependent Failures ◽  
Binary State ◽  
Semi Markov Process

Binary-state and component independent assumptions will lead to doubtful and misleading redundancy allocation schemes which may not satisfy the reliability requirements for real engineering applications. Most published works proposed methods to remove the first assumption by studying the degradation cases where multiple states of a component are from the best state to the degradation states then to the completely failed state. Fewer works focused on removing the second assumption and they only discussed dependent failures which are only a special case of component dependency. This work uses the Semi-Markov process to describe a two-component system for redundancy allocation. In this work, multiple states of a component are represented by multiple output levels, which are beyond the scope of degradation, and the component dependency is not limited to failure dependency only. The load sharing is also taken care of in the proposed work. The optimal redundancy allocation scheme is obtained by solving the corresponding redundancy allocation optimization problem with the reliability measure, the system availability, obtained through the Semi-Markov process model being constraint. Two case studies are presented, demonstrating the applicability of the propose method.

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