Commander’s Decision Analysis Tool for Maintenance: Applications of Unsupervised Learning with Pattern Characterization (ULPC) in Vehicle Health Assessment

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.