Hull Mechanical & Electrical (HM&E) Machinery Control Systems (MCS) for U.S. Navy combatant craft are highly complex systems often with hundreds of actuators and thousands of sensors spread across dozens of shipboard subsystems. As described in SBIR topic N231-028, Artificial Intelligence / Machine Learning (AI/ML) applied to HM&E MCS offers the potential for improving robustness and survivability while simultaneously reducing operator cognitive burden and reducing manning requirements. The research team proposes to develop the Shipboard Intelligent Machinery Prognostic and Learning Environment (SIMPLE) using a highly flexible foundational learning technology that is equally adept at leveraging known physics-based models when available and using a purely data-driven approach when physics-based models are not available. AI/ML technologies offer great promise for automating complex control systems whether they are HM&E for the Navy or industrial land-based systems that may be operated by energy companies/utilities or other large industrial plant operators. In addition to the existing initiatives to reduce crew workload and support reduced manning, one of the major issues the Navy is facing for its Uncrewed Surface Vessels (USVs) is achieving 30- to 90-day operations with no onboard support of the HM&E systems.