Modern Data-Driven Strategies for Enhancing Reliability and Efficiency in Predictive Maintenance of Shipyard Infrastructure

Abstract Predictive maintenance approaches to improve naval processes and streamline shipbuilding developments conducted during the inner working at Hindustan Shipyard in Visakhapatnam. Moreover, the study overcomes many problems like intricate complex operational environments and data excellence. Basically, the key parameters such as loads, speeds, torques, and vibrations were observed by using the intelligent cutting-edge sensors. To predict the performance of the equipment, various Machine Learning and Deep Learning models were enabled such as Bi Directional Long Short Terms Memory Network with Spatial Pyramid Attention Networks (BiLSTM-SPAN) and Random Forest. Consequently, BiLSTM-SPAN model outperformed well with a performance by attaining of R2 = 0.910 and significantly lower error rates. The outcomes are highlights that the developed BiLSTM-SPAN model successfully enhances the operational efficiency, accordingly, decreasing the downtime, and enhance maintenance practices, thereby making at the shipyard more competitive operations through the application of innovative intelligent predictive strategies.