Feasibility of Diagnostics, Prognostics and Hybrid Prognostics across Multiple Platforms

Abstract

Maintenance practices within the military have historically 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 CBM measures in accordance with the policy is required of the program managers through requirements of the Secretaries of the Military Departments and the Directors of the Defense Agencies. The implementation of the program 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 based 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 DoD intends to implement CBM+ to reduce maintenance costs and improve the readiness of their assets. Figure 1 provides a visual representation of the planned DoD transition.

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Document Details

Document Type
Technical Report
Publication Date
May 08, 2019
Accession Number
AD1076028

Entities

People

  • Chris J. Valant
  • Michael G. Thurston
  • Nenad G. Nenadic
  • Sean P. Mcconky

Organizations

  • Rochester Institute of Technology

Tags

Communities of Interest

  • Advanced Electronics
  • Autonomy
  • Cyber
  • Energy and Power Technologies
  • Engineered Resilient Systems
  • Ground and Sea Platforms
  • Weapons Technologies

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Bayesian Networks
  • Big Data
  • Computational Science
  • Computer Programming
  • Computers
  • Data Analysis
  • Data Mining
  • Data Science
  • Dimensionality Reduction
  • Failure Mode And Effect Analysis
  • Information Science
  • Machine Learning
  • Neural Networks
  • Supervised Machine Learning

Fields of Study

  • Engineering

Readers

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  • Systems Analysis and Design