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Real-time lubricating oil monitoring is crucial for assessing the health of industrial machinery, providing early warnings of machine anomalies and malfunctions, with a significant impact on both production and maintenance costs. This study presents an innovative industrial Internet-of-Things (IIoT) system for continuous and automated oil analysis, suitable for various industrial plants. At the heart of this system is Hivor(Trademarked.), a plug and play sensor whose 3D-printed design integrates several sensitive components and measuring principles, thus enabling comprehensive information on the general condition of lubricating oil and, consequently, of the machinery. The data are processed in real-time by D-Brane(Trademarked.) module to identify any anomalies, and a cloud platform provides alerts and detailed information to users. Experimental tests in the laboratory validated the sensor’s sensitivity to fine particles below $10~\mu $ m and extremely low humidity levels. A test in an industrial environment then demonstrated the robustness of the hardware and the diagnostics capabilities of the sensor, outperforming both traditional oil analysis methods and vibration sensors. This system addresses the limitations of the existing oil sensors by providing multiparameter analysis and real-time data processing, setting a new standard for industrial machinery maintenance.