Predictive Maintenance &
Remaining Life Assessment
Predictive maintenance has moved from offline FFT-spectrum analysis on quarterly routes into continuous, ML-driven anomaly detection on connected assets — what the ARC Advisory Group calls 'PdM 4.0'. The discipline now integrates vibration analysis per ISO 10816 / ISO 7919 / API RP 670 (machinery protection), oil-condition monitoring per ASTM D7720 (wear-metal trends, ferrography, viscosity, TAN), infrared thermography per ASTM E1934, ultrasonics for valve and steam-trap leakage, motor-current signature analysis (MCSA) for electrical-driven equipment, and acoustic emission for pressure-vessel and storage-tank monitoring. Modern programmes layer this with cloud / edge analytics platforms (GE / Aveva APM, AspenTech Mtell, Siemens MindSphere, AWS IoT, Microsoft Azure IoT, OSIsoft PI Asset Framework) and increasingly LLM-assisted root-cause synthesis. RUL (Remaining Useful Life) prediction has become tractable through Bayesian degradation models, hidden-Markov methods, and recurrent neural networks trained on years of historical failure data — though the discipline remains highly asset-specific and demands physics-of-failure understanding alongside data science.
