Experts call for advanced diagnostics, AI tools to improve industrial asset life assessment

At a recent symposium in Jamshedpur, industry experts highlighted the critical role of AI-driven predictive modeling and advanced diagnostics to enhance the safety, reliability, and cost-effectiveness of industrial assets. These tools are increasingly seen as essential for accurately assessing the remaining life of engineering components, enabling better maintenance and operational decision-making. Experts emphasized that traditional maintenance approaches, often reactive or time-based, can lead to premature equipment replacement or unexpected failures, resulting in higher costs and downtime. The integration of AI and machine learning allows organizations to transition to predictive maintenance by analyzing real-time data from sensors and using predictive analytics to forecast asset health and failure points. This approach enables precise timing of maintenance activities, extending asset lifespans by 20 to 40 percent while reducing unplanned downtime and capital expenditure. Companies leveraging AI-driven asset performance management platforms can optimize manufacturing processes, improve energy efficiency, and reduce operational risks. Such technologies help transform maintenance functions from cost centers into strategic value drivers by informing acquisition, repair, and replacement decisions across an asset’s lifecycle. The symposium underscored the importance of adopting AI-based diagnostic and modeling solutions to improve industrial asset management’s overall effectiveness, highlighting the growing trend of digital transformation in asset-intensive industries aiming for smarter, more sustainable operations.