TY  - CONF
EP  - 5
A1  - Muhammad, M.
A1  - Mokhtar, A.A.
A1  - Abdul Majid, M.A.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84858971102&doi=10.1109%2fCHUSER.2011.6163716&partnerID=40&md5=cce061418d3bbabd13d50abb46b47548
SN  - 9781467300193
Y1  - 2011///
SP  - 1
ID  - scholars1531
TI  - Reliability assessment of multi-state repairable system subject to minimal repairs and constant demand
KW  - Adverse effect; Continuous time Markov process; Data clustering; Discrete levels; Discrete time Markov chains; Discrete time markov process; Equipment failures; Equipment replacement analysis; General repair; Maintenance management; Minimal repair; Multi state; Multi-state system; Performance data; Random failures; Reliability assessments; Repairable systems; Replacement analysis; System degradation; System reliability; System state; System's performance; Transition probabilities
KW  -  Clustering algorithms; Continuous time systems; Equipment; Obsolescence; Reliability analysis; Safety engineering
KW  -  Markov processes
N1  - cited By 1; Conference of 2011 IEEE Colloquium on Humanities, Science and Engineering, CHUSER 2011 ; Conference Date: 5 December 2011 Through 6 December 2011; Conference Code:89137
N2  - Effective maintenance management is essential to reduce the adverse effect of equipment failure to operation. This can be accomplished by accurately predicting the equipment failure such that appropriate actions can be planned and taken in order to minimize the impact of equipment failure to operation. This paper presents a model to assess system reliability for a degraded multi-state system based on discrete time Markov process and continuous time Markov process. The selection of which model to use is based on the type of available data. The system degradation was quantified by discrete level of system's performance rate with system states ranging from perfect functioning state to complete failure. At any point in time, the system can experience random failures from any degraded state upon which general repair will be performed. This research also explored a method of estimating of transition probabilities as well as definition of states for the Markov process by utilizing system performance data and data clustering method. The results proved the applicability of both discrete time Markov chain and continuous time Markov process in assessing the reliability of multi-state systems using the system's performance data. The results are then utilized to perform equipment replacement analysis due to deterioration based on the expected demand. © 2011 IEEE.
AV  - none
CY  - Penang
ER  -