A Wireless Sensor Network for Prognostics and Health Management
Abstract
Equipment maintenance mode is changing from Breakdown Maintenance and Periodic Maintenance to Condition-based Maintenance. Fault Prognostic (Remaining Useful Life, RUL) is the key to Condition-Based Maintenance. This article first introduces the basic principles of Dynamic Bayesian Network and the Fault Tree, and then uses the Fault Tree and Dynamic Bayesian Network for Wireless Sensor Network for fault prediction methods and procedures. Dynamic Bayesian networks can integrate information from various sources and give a probabilistic representation of the system. DBNs provide a platform suited for seamless integration of diagnosis and prediction. ly used in order to anticipate the failure At last, with the methods above, we predict the life cycle and the health status of the Wireless Sensor Networks. We use MATLAB to simulate and the result showed that the methodology proposed could have been successful of the wireless sensor network.
Keywords
Wireless sensor network, Prognostics and health management, Dynamic bayesian network
DOI
10.12783/dteees/peem2016/5031
10.12783/dteees/peem2016/5031
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