Academic Research Papers

Unlock the power of digital twins and system-level condition monitoring to accelerate fault diagnosis and drive proactive maintenance.

See how energy-efficient AI and advanced algorithms can enhance fault diagnosis accuracy while minimising computational costs in industrial settings.

Learn how code-driven structured knowledge reasoning can unlock hidden maintenance insights and improve fault resolution with minimal disruption.

Discover how iMaintain’s explainable AI and multimodal knowledge graph delivers transparent decision support to eliminate repeat faults and boost asset reliability.

Discover how iMaintain’s AI platform decouples and continually integrates maintenance insights into structured knowledge for ongoing reliability improvements.

Learn how iMaintain’s AI-driven platform transforms code-driven research into unified, structured maintenance knowledge for faster, data-backed troubleshooting.

Uncover how manufacturing teams leverage AI-driven multivariate time series analysis to forecast equipment failures and optimize spare parts planning.

Learn how LSTM and time-series analysis power AI-driven predictive maintenance in medical equipment, capturing repair history and expert knowledge to prevent unexpected failures.

Summarize leading research on electric bus predictive maintenance and see how iMaintain’s AI platform transforms experimental data into actionable insights.

Explore how path analysis improves transit maintenance performance and learn how iMaintain’s AI transforms these insights into actionable fleet reliability improvements.