A Comprehensive Survey of Prognostics and Health Management based on Deep Learning for Autonomous Ships

Abstract

The maritime industry widely expects to have autonomous and semiautonomous ships (autoships) in the near future. In order to operate and maintain complex and integrated systems in a safe, efficient, and cost-beneficial manner, autoships will require intelligent Prognostics and Health Management (PHM) systems. Deep learning (DL) is a potential area for this development, as it is rapidly finding applications in a variety of domains, including self-driving cars, smartphones, vision systems, and more recently in PHM applications. This paper introduces and reviews four well-established DL techniques recently applied to various practical PHM problems. The purpose is to support creativity and provide inspiration toward the PHM based on DL in autoships and the maritime industry. This paper discusses benefits, challenges, suggestions, existing problems, and future research opportunities with respect to this significant new technology.