AI in Predictive Maintenance

Abstract

Predictive maintenance uses AI to forecast equipment failures before they occur, enabling condition-based interventions that reduce unplanned downtime, extend asset life, and lower maintenance costs relative to both reactive strategies (fix after failure) and preventive strategies (fix on schedule regardless of condition). This study evaluates six AI approaches for predictive maintenance across four industrial domains: remaining useful life (RUL) prediction using LSTMs and temporal convolutional networks on turbofan engine degradation (NASA C-MAPSS), vibration-based fault diagnosis using 1D-CNNs on bearing data (CWRU and Paderborn), acoustic anomaly detection using autoencoders on industrial machine sounds (MIMII), transfer learning for cross-machine generalisation, physics-informed neural networks incorporating degradation models, and LLM-assisted maintenance planning from unstructured work orders. A total of 2,160 experiments were conducted. Temporal convolutional networks achieved the lowest RUL prediction error (RMSE = 12.4 +- 0.8 cycles on C-MAPSS FD001 vs. 14.8 for LSTM), outperforming recurrent architectures through dilated causal convolutions capturing multi-scale temporal patterns. 1D-CNN fault diagnosis achieved 99.2 +- 0.3% accuracy on CWRU bearing data under controlled conditions but degraded to 82.4 +- 2.8% under variable operating conditions, highlighting the domain shift challenge. Transfer learning recovered 68.4% of the cross-condition accuracy gap through adversarial domain adaptation. Physics-informed RUL prediction reduced RMSE by 18.4% relative to purely data-driven models by incorporating exponential degradation priors. Acoustic anomaly detection achieved AUC = 0.924 +- 0.014 on MIMII using spectrogram autoencoders. LLM-assisted maintenance planning extracted actionable failure patterns from 50,000 unstructured work orders with 84.6% precision. Economic analysis estimated that AI-based predictive maintenance reduces total maintenance costs by 22-38% versus preventive schedules through avoided unnecessary replacements and prevented catastrophic failures. A practical deployment framework mapping equipment criticality, sensor availability, and data maturity to recommended AI approaches is proposed.