000011149 001__ 11149
000011149 005__ 20241101121432.0
000011149 0247_ $$2doi$$a10.24868/11149
000011149 037__ $$aGENERAL
000011149 245__ $$aEnhancing Predictive Maintenance in the Maritime Industry with Unsupervised Learning
000011149 269__ $$a2024-11-05
000011149 336__ $$aConference Proceedings
000011149 520__ $$aThis paper introduces a novel approach centred on unsupervised learning, specifically leveraging state-of-the-art recurrent neural networks (RNNs), particularly Long Short-Term Memory (LSTM). The study aligns with the domain of predictive maintenance (PdM) and time series analysis for evaluating the health status of devices.
The proposed methodology seeks to enhance current PdM practices by integrating machine learning (ML) into the conventional statistical framework. ML techniques are increasingly applied in the industrial sector, demonstrating their capacity to capture complex correlations that may elude human operators.
Indeed, the management of scheduled maintenance in the naval sector is a complex challenge, primarily due to the diversity of devices and systems on board ships. These systems vary in nature, complexity, and criticality, making it challenging to adopt a standardized maintenance strategy.
Solutions based on Condition-Based Maintenance (CBM) aim to perform maintenance based on the actual operational conditions of a system, rather than following fixed schedules. This approach relies on the use of sensors, continuous monitoring, and advanced diagnostics to assess the health status of components and predict failure times.
However, despite advancements in this direction, there are still significant challenges to address. One of the main challenges is the heterogeneity of systems on board ships.
Furthermore, approaching the predictive maintenance task for onboard ship equipment, a key challenge emerges when attempting to estimate the Remaining Useful Life (RUL) of a component given the scarcity of run-to-failure data. For such a reason, the developed model adopts a fully data-driven approach, where the failure characteristics (run-to-failure data) of the equipment are not pre-defined.
Moreover, the framework encompasses data acquisition, data preprocessing, Health Index (HI) construction, and the prediction of remaining useful life grounded in the examination of a documented failure case related to maritime equipment. Indeed, the framework has been tested on data corresponding to an actual equipment failure, enabling a direct comparison with its application to data from normally functioning equipment without anomalies.
The presented unsupervised approach serves a dual purpose: enabling real-time detection of potential failures and facilitating trend analysis of the device's health index. This dual functionality proves valuable in estimating the projected failure period.
000011149 7001_ $$aFaggioni, N$$uFincantieri NexTech
000011149 7001_ $$aCaviglia, A$$uFincantieri NexTech
000011149 7001_ $$aGuarnera, N$$uArgo IT
000011149 7001_ $$aSchininà, E$$uArgo IT
000011149 7001_ $$aSansebastiano, E$$uFincantieri NexTech
000011149 7001_ $$aChiti, R$$uFincantieri NexTech
000011149 773__ $$tConference Proceedings of iSCSS
000011149 773__ $$jiSCSS 2024
000011149 8564_ $$uhttps://library.imarest.org/record/11149/files/iSCSS_19_paper_19_v2.pdf$$9b99fc721-11ea-47d9-925f-333811465229$$s4301650