Augmenting the Existing CBPM Maintenance Philosophy Currently in Use in the Marine Sector with Intelligent Predictive—CBPM Philosophy

Gautam, Minakshi; Ramu, Vaishnavi S.; Sinha, Sachin; Reddy, Pranay Kumar; Kondur, Monica; Kumar, S. Suresh

doi:10.1007/978-981-15-7234-0_51

Cited by 1 publication

(1 citation statement)

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“…The other PdM data-based corrosion prediction models include the 25 years of monitoring of historical data of ship hulls [140], the condition-based predictive maintenance of customized self-healing systems [141], the spatial dependence of material, geometric and corrosion growth properties [142], and the synthesized life-cycle risk analysis framework [143].…”

Section: Pdm With Data-based Modelmentioning

confidence: 99%

Application of Artificial Intelligence in Marine Corrosion Prediction and Detection

Imran

Jamaludin

Ayob

et al. 2023

JMSE

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One of the biggest problems the maritime industry is currently experiencing is corrosion, resulting in short and long-term damages. Early prediction and proper corrosion monitoring can reduce economic losses. Traditional approaches used in corrosion prediction and detection are time-consuming and challenging to execute in inaccessible areas. Due to these reasons, artificial intelligence-based algorithms have become the most popular tools for researchers. This study discusses state-of-the-art artificial intelligence (AI) methods for marine-related corrosion prediction and detection: (1) predictive maintenance approaches and (2) computer vision and image processing approaches. Furthermore, a brief description of AI is described. The outcomes of this review will bring forward new knowledge about AI and the development of prediction models which can avoid unexpected failures during corrosion detection and maintenance. Moreover, it will expand the understanding of computer vision and image processing approaches for accurately detecting corrosion in images and videos.

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