Feasibility of Diagnostics, Prognostics and Hybrid Prognostics across Multiple Platforms
Abstract
Feasibility of Diagnostics, Prognostics and Hybrid Prognostics across Multiple Platforms.See GRANT Technical Proposal under Standar"d Attachments for more detailed SOW. Abstract Maintenance practices within the military previously relied on two main practices, failure replacements and schedule or usage based preventative maintenance replacements. However, the Department of Defense (DoD) mandated Condition Based Maintenance (CBM) with DoD Instruction 4151.22[1]. These instructions indicate that CBM+ shall be used as the principal consideration for selection of proper maintenance concepts. The implementation of the CBM should be in accordance with the Condition Based Maintenance Plus DoD Guidebook [2]. The guidebook breaks down maintenance into reactive and proactive. Reactive maintenance is performed on items that are run to failure. Proactive maintenance is broken down further into preventative/scheduled maintenance or predictive maintenance. Preventative maintenance is either based on a schedule or on a trigger that may lead to failure (e.g. a visual oil leak). Predictive maintenance is either diagnostic or prognostic. Diagnostic identifies impending failure and prognostic adds a prediction of remaining useful life. The Office of Naval Research (ONR) would like to plan for the future implementation of prognostics across the armed services. To this end, the Golisano Institute for Sustainability (GIS) at Rochester Institute of Technology proposes to conduct a multi-faceted study that will address current implementations of CBM through to a roadmap for advanced prognostics models. GIS will work with ONR, Marine Corps, Army, and Navy personnel on the evaluation process across three platforms, one each from the Marine Corps, Army, and Navy. Synergy is expected between the Army and Marine Corps platforms, but the Naval platform is expected to bring a very different perspective. GIS will utilize the data collected from the three selected platforms to identify the top degraders. Additional data analysis will be performed to identify immediate opportunities for anomaly detection, diagnostics, prognostics and decision support. Next, to identify opportunities to improve collection methods, health and usage monitoring (HUMS) and maintenance data will be evaluated for quality and completeness. GIS will then perform significant research into best practices and emerging trends in prognostics across trade spaces outside of transportation. Subsequently, prognostic models will be evaluated for use in the future prognostics system, which is expected to lead to specific recommendations for implementation. The final task is to develop a roadmap for future prognostic deployment, including recommendations for frameworks, software and data interfaces. These recommendations will lay the groundwork for a future implementation program where the system is fully implemented across multiple platforms. [1] T. a. l. Under Secretary of Defense for Acquisition, "Department of Defense Instruction Number 4151.22," ed, 2012. [2] U. D. o. Defense, "Condition Based Maintenance Plus (CBM+) DoD Guidebook," ed, 2012.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Sep 25, 2017
- Source ID
- N000141712726
Entities
People
- Michael Thurston
Organizations
- Office of Naval Research
- Rochester Institute of Technology
- United States Navy