A Comparative Study of Health Monitoring Sensors based on Prognostic Performance
In the safety critical systems such as industrial plants or aircraft, failure occurs inevitably during the operation, and it is important to prevent this while maintaining high availability. Therefore, a lot of efforts are being directed toward developing advanced prognostics algorithms and sensing techniques as an enabler for predictive maintenance. The key for reliable and accurate prediction not only relies on the prognostics algorithms but also based on the collection of sensor data. However, there is not much in-dept studies toward evaluating the varying sensing techniques based on the prediction performance and inspection scheduling. It would be more reasonable for practitioner to select different cost of sensors based on the sensors’ contribution on reducing the cost on unnecessary inspection or measurement while maintaining its prognosis performance. Thus, the authors try to thoroughly evaluate the cost-effectiveness of the different sensor with respect to sensor resistance to noise. The simulation is conducted to analyze the prediction performance with varying measurement interval and different level of noise during degradation. Then real run-to-fail (RTF) dataset acquired from two different sensors are analyzed to design optimal measurement system for predictive maintenance.
How to Cite
Prognostics, Health Monitoring, Sensors, cost-effectiveness
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.