Management of maritime military platform life is a real challenge for operators and maintainers. One of the major problems is how to capture cost effective information about the hull defect and predict the remaining life? This paper will focus on the automation process for calculation of the failure rate and prediction from collated hull defects information. This data is assessed for accuracy, sensitivity and correctness of defect location and failure modes. An outline of the automation techniques investigated to determine the hull failures rates is discussed. Five major failure modes are analysed and a high level approach for integrating failure data collation is presented. The paper then discusses some of the issues and challenges with obtaining reliable maintenance data and opportunities for further research in finding a better solution.
Cognitive radio network (CRN) users are inherently expected to experience widely-varied delays due to the uncertainty in wireless channel availability. Supporting delay sensitive real-time services through CRNs, so that visitors are allowed to experience full-scale networking services by opportunistically sharing the spectrum from a number of existing networks without impacting on the primary users, thus remains a challenging task. This paper presents a novel technique to provision QoS guarantee for delay-sensitive services in CRNs having secondary users equipped with multiple radio interfaces. The technique relies on modeling spectrums holes from multiple primary networks through a resultant channel to enable implementing a single server queuing model with random service interruption. Simulation results using ns-2.33 show that using multiple radio interfaces has sheer strength to reduce CRN delay with fewer number of primary channel sensing.