AI-Enabled Condition Monitoring (CM) of Propulsion, Power, and Auxiliary Systems

Introduction
Condition monitoring is the “pulse” of a vessel’s mechanical health. CM is part of the “Digital Workplace” trend where AI analytics provide continuous oversight of high-complexity power management systems. This ensures “zero-downtime” for sophisticated assets like DP3 drillships or luxury cruise liners, where power failure is not an option.

In emerging markets, AI-enabled CM is a strategic tool for extending the “economic life” of assets. By monitoring propulsion and auxiliary systems on older vessels, operators can prevent catastrophic failures that would otherwise lead to the total loss of the asset. This course provides the reliability and resilience strategies needed to manage a diverse fleet across multiple economic zones.

Managers will learn how to design dashboards that reduce “Digital Friction” for their teams, ensuring that the right alerts reach the right people at the right time

Learning Outcomes

1. Implement AI condition monitoring (CM) frameworks for propulsion and power systems.
2. Develop reliability and resilience strategies based on real-time health indicators.
3. Integrate CM data into “Digital Workplace” workflows for shore-based engineers.
4. Design AI alerts that distinguish between critical failures and sensor noise.
5. Evaluate the impact of CM on insurance premiums and classification standards.
6. Lead teams in interpreting system health dashboards for faster engineering decisions.

Learning Objectives

1. Apply AI analytics to propulsion, power distribution, and auxiliary systems.
2. Interpret system health indicators to prevent catastrophic mechanical failures.
3. Develop reliability strategies that mitigate “Digital Burnout” for monitoring staff.
4. Identify the most critical sensor points for propulsion monitoring.
5. Compare AI condition monitoring vs. traditional vibration analysis.
6. Construct a CM implementation plan for a heterogeneous fleet.