By Topic

Cost-optimal condition-monitoring for predictive maintenance of 2-phase systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
L. M. Maillart ; IOE Building, Ann Arbor, MI, USA ; S. M. Pollock

The deterioration processes of many industrial systems can be modeled in 2-phases. A 2-phase system begins its life in a new condition where it resides for a random amount of time before progressing to a worn condition where it resides for a random amount of time preceding system failure. If monitoring takes place while the system is in the worn condition, preventive maintenance is performed. This paper analyzes predictive maintenance policies for systems exhibiting 2-phase behavior, and presents cost-minimizing policies, as well as satisfying policies, to determine when monitoring should take place, and for allocating monitoring resources to multiple systems. The solution approach is based on decomposing the expected cost (per unit time) into 2 components: the expected cost due to maintenance actions, and the expected cost due to monitoring actions. This decomposition facilitates the construction of operating-characteristic curves that represent policy performance, and allows evaluation of the policy tradeoffs in many situations including those with constrained or unconstrained monitoring resources, multiple or single systems, and fixed or nonfixed monitoring intervals

Published in:

IEEE Transactions on Reliability  (Volume:51 ,  Issue: 3 )