Lubricant Oil Degradation, Condition Monitoring, and Service Life Estimation: A Review

Lubricant oil is often changed on fixed time schedules, even though its real condition depends on load, temperature, and contamination, so good oil is discarded in some cases and degraded oil stays in service in others, which adds cost and waste and raises the risk of gearbox and hydraulic system failures. Degradation, monitoring, and life prediction are usually reviewed separately, so the links between them remain unclear. This review summarizes how oil type, including mineral, synthetic, bio‐based, nano‐, and ionic‐liquid lubricants, and operating loads cause degradation, including thermal‐oxidative degradation, additive depletion, viscosity breakdown, acid number increase, and water and particle contamination. It compares offline monitoring methods such as viscometry, Fourier‐transform infrared spectroscopy (FTIR), ferrography, particle counting, elemental analysis, and online methods such as dielectric, capacitive, inductive, optical, and multi‐sensor fusion by what they measure and where they can be used. It also assesses health index and RUL models, including machine learning, deep learning, fuzzy logic, stochastic degradation models, and digital twins, regarding how they support oil change decisions within one life cycle framework. The literature, which comprises about 230 sources from 2017 to the present, was collected from Scopus, IEEE Xplore, and SpringerLink. No single method, sensor, or model captures lubricant degradation under real operating conditions. Condition monitoring is shifting from laboratory analysis to integrated online multi‐sensor systems, and predictive models must balance accuracy, interpretability, data demand, and industrial readiness. The main gap is the limited integration across degradation, sensing, and prediction, together with the lack of standardized benchmarking and laboratory‐to‐field validation. Hybrid physics‐informed and data‐driven models, virtual sensors, and digital twins are promising routes to condition‐based and predictive maintenance and to optimizing oil change intervals, additives, and formulations, with potential environmental and economic benefits.