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Asset integrity and predictive maintenance models require data for an accurate assessment of an asset’s condition. Historically this data is collected periodically in the field by technicians using hand-held units. Significant investment in the area of Smart Sensors (SmS), as well as Wireless Sensor Networks (WSNs), is changing the way organisations collect asset data. Permanently installed inexpensive SS units will enable near real-time data collection and reduce the safety exposure of technicians by eliminating the need to obtain field data manually.

The proposed project will attempt to address a subset of the problems presented by the deployment of widescale deployment of SmS in a WSN. The first problem is assuring the validity of the data generated by the SmS. Many of the SmS which will soon see service in industry utilise microelectromechanical systems (MEMS). While MEMS sensors have shown considerable benefit in commercial consumer applications there use in the offshore sector has not yet been proven. This project will develop a framework to quantify the uncertainty surrounding a sensor measurement to allow for the data to be validated.

The second problem this project will attempt to address is resource optimisation. WSNs have a variety of resource constraints including energy, bandwidth and others. Therefore, it is of great benefit to maximise the information utility of individual sensor measurements to allow for a minimum number of samples to be taken while allowing for a certain level of uncertainty.

The project intends to use Bayesian statistical methods in conjunction with Soft Sensors (SS). SS usage is widespread in the process industry as demonstrated by Kadlec et al. Hines, Coble and others show extensive research and application of a Bayesian framework in the nuclear industry.

While the research to date shows the suitability of the proposed method, there is a lack of research for the application of SS in the civil sphere. The project intends to examine the impact and integration of MEMS-based systems into validation frameworks.

The project requires an interdisciplinary approach, knowledge of mechanics, electronics and statistics are needed. Successful outcomes in the proposed project will allow for the offshore sector to deploy WSNs with confidence. Ultimately leading to safer working conditions, lower costs and a reduced environmental impact.