Start with the failure mode
Choose equipment where failure is costly, data is available and an action is possible. Define the degradation mechanism, useful signals, lead time, false-alarm tolerance and responsible engineer.
Use more than sensor data
Temperature, pressure, vibration, load, fuel, electrical and alarm data become more useful when combined with running hours, maintenance history, operating mode, ambient conditions and engineer observations.
Rules, models and AI
Threshold rules are understandable and often effective. Statistical baselines can identify drift. Machine-learning models may discover complex patterns but require representative data and monitoring. Generative AI can summarise context; it should not fabricate a diagnosis.
Shipboard realities
Connectivity is intermittent, sensors fail, equipment is modified and vessels operate across changing loads and climates. Edge processing, data buffering, compression and clear quality flags are important. Alerts must be prioritised to avoid alarm fatigue.
A staged Zea IoT roadmap
Begin with data ingestion and equipment context, then descriptive health views, rule-based exceptions, engineer feedback and finally validated predictive models. Integrate work orders with the existing planned-maintenance system rather than creating a second uncontrolled maintenance record.
Frequently asked questions
What is predictive maintenance on a ship?
It uses condition and operational data to estimate developing equipment problems and support maintenance before functional failure.
Does predictive maintenance replace a PMS?
No. It should inform and integrate with the planned-maintenance and engineering process.
What is the biggest implementation risk?
Poor data context and excessive false alarms can destroy trust even when the underlying technology is sophisticated.