Remote Health Monitoring for Offshore Machines, using Fully Automated Vibration Monitoring and Diagnostics

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Mustapha Mjit Pierre-Philippe J. Beaujean David Vendittis

Abstract

This paper describes the use of vibration analysis with a fully automated diagnostics system to detect common machine faults such as imbalance and misalignment as well as bearing and gearbox faults of offshore machines. Other faults types, e.g. when a large object hits the propeller blades may be detected using the STFT. As the mechanical properties of the structure can change because of changes of temperature and oil quality, these (and other) state data are also stored. The data fusion process is currently under work. Experiments were performed on a home cooling fan system to demonstrate and illustrate the faults detection capability of the vibration and diagnostics system. Vibration data were also acquired from selected equipment on a small boat with different combinations (on/off status) of the engine, generator and hydraulic pump. The trending and alarm features were demonstrated for the different types of data. The vibration and diagnostics system is implemented with threshold limits for the alerts and alarms corresponding to each technique. The model also allows for automatic storing of raw data periodically and after any deviations from normal conditions; i.e., when alerts are on.

How to Cite

Mjit, M. ., J. Beaujean, P.-P. ., & Vendittis, D. . (2010). Remote Health Monitoring for Offshore Machines, using Fully Automated Vibration Monitoring and Diagnostics. Annual Conference of the PHM Society, 2(1). https://doi.org/10.36001/phmconf.2010.v2i1.1757
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Keywords

condition monitoring, signal processing

References
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Section
Technical Papers