NCMS Blog

There are five basic steps for companies looking to start applying artificial intelligence (AI) and machine learning (ML) in their predictive maintenances. It starts with having a defined maintenance strategy in place and concludes with finding the most appropriate predictive maintenance software to apply. By applying these steps to a company’s needs, AI and ML can optimize machine maintenance, increase output, and lower costs. These case studies are examples of integrating AI and ML into business processes.

• Asset histories and sensor data are compiled and analyzed to optimize operations through powerful predictive maintenance tools.
• Case Study 1: Integrated Repair Data. A predictive maintenance software program was implemented to integrate all repair data for a fleet of F-35s to create for primary capabilities: an Artificial Intelligence Prognostic Steering Tool to prioritize maintenance solutions, an Equipment Manager to provide asset life-cycle management, a Modification Manager to create a centralized environment for supporting data, and a Supply Module to generate insight on current and predicted supply chain performance.
• Caste Study 2: Predictive Asset Readiness Solution. Using AI software program, Predictive Asset Readiness, four weapon systems data were assessed to create predictions of the asset’s future condition and initiate corrective actions to prevent asset failure. Three models were created and used in this project: Asset Status Prediction, Non-Mission Capable Duration Prediction, and Non-Mission Capable Duration Driving Factors.