Commander's Decision Analysis Tool for Maintenance: Applications of Unsupervised Learning with Pattern Characterization (ULPC) in Vehicle Health Assessment Trident Systems LLC, Lightridge Solutions Company

Format:

Book

Conference/Event

Author/Creator:

Bailey, Jeffrey, author.

Contributor:

Cabrey, Connor

Hsu, Charles

Conference Name:

2025 NDIA Michigan Chapter Ground Vehicle Systems Engineering and Technology Symposium (2025-08-12 : Novi, Michigan, United States)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2025

Summary:

Within the military maintenance cycle, commanders and units struggle with understanding the operational readiness of their fleets from a data driven perspective. Many unsupervised learning techniques have been developed with applications for vehicle maintenance with pattern classification. In this paper, Predictive Maintenance using Unsupervised Learning with Pattern Characterization (ULPC) is proposed to classify the overall health of the platform system and subsystems. In this model, the key features are selected using an intelligent pre-processing system for signal classification for each subsystem. Next the data is processed and compared to a normalcy baseline dataset using the unsupervised machine learning (ML) model. Operational data collected post-baseline is then processed through a Recurrent Neural Network (RNN) and clustered. An overall "normalcy" metric is calculated to show the difference in operation when compared to the baseline patterns. This normalcy servers as an analog for the platform's overall readiness level for operational commander decision making

Notes:

Vendor supplied data

Publisher Number:

2025-01-0490

Access Restriction:

Restricted for use by site license