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(3) Condition-Based Predictive Maintenance for Mission Critical Systems with Probabilistic Knowledge Graph and Deep Learning

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-20-F-0147
Agency Tracking Number: N193-A01-0280
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N193-A01
Solicitation Number: 19.3
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2019-11-21
Award End Date (Contract End Date): 2020-04-20
Small Business Information
20271 Goldenrod Lane Suite 2066
Germantown, MD 20876
United States
DUNS: 967349668
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Genshe Chen
 (240) 481-5397
Business Contact
 Yingli Wu
Phone: (949) 596-0057
Research Institution

New tools and technologies are needed for modern US Navy surface and aviation fleets to augment current onboard condition monitoring and maintenance processes and help improve mission-critical systems availability, increase operational readiness, and reduce life cycle costs. The state-of-the-art condition-based predictive maintenance (CBPM) applies analytics to predict failures and recommends service only when needed. CBPM is preferred for naval assets because it provides a window into the future of each asset’s predictive performance.In this effort, IFT proposes to develop an integrated approach that includes both data-driven and physics-based modeling techniques in order to build reliable diagnostic and prognostic models. The proposed CBPM system is based on the state-of-the-art knowledge graph and deep learning framework. To explore streaming data from multiple sources, the knowledge graph will be constructed from collections of unstructured and structured data with natural language processing to extract entities, relationships, and events between them. The proposed cognitive-based decision support system is to support the operator to combine data, identify potential failures rapidly, and provide timely recommended proactive maintenance actions with increased efficiency in logistics and supply chain. This is particularly important for mission-critical systems to support sustained combat operations and readiness with minimum costs and unplanned downtime.

* Information listed above is at the time of submission. *

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