Alert Lifecycle and Best Practices for Predictive Maintenance Alert Lifecycle Management
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Abstract
The predictive maintenance alert lifecycle is a critical topic in the aviation industry. Stakeholders, including operators, suppliers, and Original Equipment Manufacturers (OEMs), require effective frameworks to support the value proposition of predictive maintenance products and services. However, defining alert effectiveness is challenging due to the lack of industry standards for the end-to-end lifecycle of predictive maintenance alerts. Adding to the challenge, different stakeholders may want to optimize on different objectives. Often, alert performance is measured prematurely or not at all. To ensure high-quality alerts, all alerts should be managed through their entire lifecycle until obsolescence. This whitepaper outlines a clear conceptual framework for the predictive maintenance alert lifecycle and best practices alert lifecycle management. Future work will expand upon the qualitative benefits discussed herein with quantitative results achieved from applying this framework to assess alert effectiveness at scale within the Boeing alert catalog.
How to Cite
##plugins.themes.bootstrap3.article.details##
Life Cycle, Predictive Maintenance
International Organization for Standardization (ISO) (2003). Condition monitoring and diagnostics of machines — Data processing, communication and presentation — Part 1: General guidelines. Geneva, Switzerland: International Organization for Standardization (ISO).
Society of Automotive Engineers (SAE) (2020). Diagnostic and Prognostic Metrics for Aerospace Propulsion Health Management Systems. In SAE, AIR 7999. Warrendale, PA: Society of Automotive Engineers.
Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., & Schwabacher, M. (2008). Metrics for Evaluating Performance of Prognostic Techniques. In 2008 International Conference on Prognostics and Health Management Conferences Proceedings.
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.