Implementation of multisource sensors combined with data analysis systems (e.g. machine learning) might provide new solutions for predictive maintenance to improve sociotechnical system reliability. The Seanatic project aims to develop a decision support tool to increase maintenance processes in the maritime field, considering limits and benefits of human factor expertise. Under this perspective, this paper describes the Cognitive Work Analysis (CWA) approach for investigating new key functions that emerge in future maintenance sociotechnical systems. After phase one of the CWA was completed (WDA -Work Domain Analysis), the functions identified were used in the subsequent phases (ConTA -Control Task Analysis and SOCA -Social Organization and Cooperation Analysis) to highlight different implications for human cognitive activities. Realtime and prediction of machine breakdown of a vessel could be significantly reduced by assisting the chief engineer for supervision and planning activities. Based on a CWA approach, ecological design interfaces could support those activities.