CBM+ Data Characterization for AI-Based Predictive Maintenance Analysis

NCMS Project #: 141012

Problem: A condition where an industry is plagued with excessive down time due to low equipment reliability and sub-optimized maintenance creates a substantial negative impact on our country’s aviation operations as well as the cost and quality of air transportation services sold to the general public.  There is a critical need to benchmark current and emerging industry standards initiatives which could be utilized by the Services to support the assessment, characterization, collection and transformation of condition-based maintenance plus (CBM+) data and information, to enable enterprise-wide predictive maintenance applications using artificial intelligence (AI).

Benefit: While data generation, collection, storage and retrieval capabilities have kept pace via technology developments, the capability to develop and apply appropriate AI-based analytical methods and tools to analyze and make use of the sensor data at a commensurate rate has been severely lagging.  By focusing on developing a solution that provides a replicable, standard process that supports the assessment and characterization of sensor and maintenance event data for AI-based analysis, this project directly targets the issues that industry is facing today.

Solution/Approach: Predictive maintenance applications are essential in order to deliver optimum levels of aircraft availability and sustainment operations efficiencies.  The initial phase will provide a clear insight to assess and establish a foundation that demonstrates the value of using a standardized process specification to support the assessment and characterization of sensor and maintenance event data for a given aircraft (health-ready) component or subsystem to determine if the data is sufficient to support AI-driven analysis.  Efforts will focus on issues specific to the H-60 platform.

Impact on Warfighter:

  • Reduce forward deployed asset footprint
  • Re-prioritize maintenance activities
  • Lower costs
  • Improve warfighter readiness

DOD Participation:

  • Joint Artificial Intelligence Center (JAIC)
  • Army, Air Force, Navy, Marines and Special Operations Command (SOCOM)
  • H-60 Program Offices

Industry Participation:

  • Global Strategic Solutions LLC
  • GE Aviation
  • Carnegie Mellon University, Auton Lab
  • University of Tennessee, The Bredesen Center
  • NCMS

Benefit Area(s):

  • Cost savings
  • Repair turn-around time
  • Positive environmental impact
  • Safety
  • Maintenance management improvement

Focus Area:

  • CBM+

Final Report