Currently, the U.S. Army faces an obstacle in collecting, integrating, and displaying all of the data generated on a single platform, which limits their ability to implement Condition Based Maintenance (CBM+). Any organization that depends on mechanized operations, be it automobiles, planes, trucks, or even automated systems, needs to consider the costs of maintenance. Reactive activities result in reactive productivity. Having vital assets down for repair, cost the organization valuable time, loss of productivity that translates into lost income opportunities, and possibly aggravated customers. Having a data platform that not only anticipates when maintenance is actually needed rather than scheduled due to manual suggestions but also advises on what parts to have on hand when assets do break, would save US industries and the military untold time and money, as well as employees’ redundant maintenance and sustainment efforts.
The intent of this project is to identify a process to analyze data that is off-loaded from VHMS-supported vehicles, define the model associated with vehicle analytical data, integrate the Secure Maintainer’s Support Device (MSD) for vehicle data off-loading, and define the Concept of Operations (CONOPS) associated with end-to-end CBM+ data transfer from vehicles to the Enterprise. Collectively, these tasks will allow the U.S. Army to acquire and analyze vehicle health data to improve its vehicle repair and maintenance methods.
This project will work within the Common CBM Data Warehouse (CCBMDW) maintained by LOGSA to determine what data needs to be acquired, how the data is acquired, where this data should be stored and how this data should be processed. The deliverables will be aimed at assisting Main Battle Tank Systems (and other PEO Ground Combat Systems platforms) visualize, analyze and provide feedback for vehicle performance and reliability. This specific effort has four primary aims:
- Demonstrate the ability to securely collect time-series vehicle data
- Define the process for creating diagnostic/prognostic algorithms
- Maximize predictive analytics’ capabilities
- Provide visibility into the current condition of vehicles’ and future maintenance requirement
Those interested in participating in this project should contact Lisa Stobierski, firstname.lastname@example.org or (734) 995-5636 by January 28, 2019. We encourage participation of Disadvantaged Business Enterprises (DBEs), including Minority Business Enterprises (MBEs) and Women’s Business Enterprises (WBEs).