Collection and Analysis of LUSV Test Data

This presentation details the evaluation of the data of a 720-hour continuous reliability demonstration for the MTU 20V 4000 M93L Main Propulsion Diesel Engine. The project served as a critical testbed for evaluating engine performance without human intervention, while simultaneously experimenting with advanced computational infrastructure for data acquisition, contextualization, and anomaly detection.

We examine the implementation of both cloud and edge computing, the use of secondary sensing technology like vibration analysis, and the challenges of data integration from disparate sources. Key findings underscore that while the mechanical equipment demonstrated high reliability, success in unmanned operations requires significant improvements in testing capabilities, data strategy, and infrastructure robustness. The session concludes with actionable lessons learned regarding data acquisition, human factors, and the deployment of AI-driven analytics to support future autonomous maritime operations.