Smart Oil Management with Green Sensors for Industry 4.0

Lubricating oils are utilised in equipment and machinery to reduce friction and enhance material utilisation. The utilisation of oil leads to an increase in its thickness and density over time. Current methods for assessing oil life are slow, expensive, and complex, and often only applicable in laboratory settings and unsuitable for real-time or field use. This leads to unexpected equipment failures, unnecessary oil changes, and economic and environmental losses. A comprehensive review of the extant literature revealed no studies and no national or international patents on neural network algorithm-based oil life modelling and classification using green sensors. In order to address this research gap, this study, for the first time in the literature, provides a green conductivity sensor with high-accuracy prediction of oil life by integrating real-time field measurements and artificial neural networks. This design is based on analysing resistance change using a relatively low-cost, three-dimensional, eco-friendly sensor. The sensor is characterised by its simplicity, speed, precision, instantaneous measurement capability, and user-friendliness. The MLP and LVQ algorithms took as input the resistance values measured in two different oil types (diesel, bench oil) after 5–30 h of use. Depending on their degradation levels, they classified the oils as ‘diesel’ or ‘bench oil’ with 99.77% and 100% accuracy. This study encompasses a sensing system with a sensitivity of 50 µS/cm, demonstrating the proposed methodologies’ efficacy. A next-generation decision support system that will perform oil life determination in real time and with excellent efficiency has been introduced into the literature. The components of the sensor structure under scrutiny in this study are conducive to the creation of zero waste, in addition to being environmentally friendly and biocompatible. The developed three-dimensional green sensor simultaneously detects physical (resistance change) and chemical (oxidation-induced polar group formation) degradation by measuring oil conductivity and resistance changes. Measurements were conducted on simulated contaminated samples in a laboratory environment and on real diesel, gasoline, and industrial oil samples. Thanks to its simplicity, rapid applicability, and low cost, the proposed method enables real-time data collection and decision-making in industrial maintenance processes, contributing to the development of predictive maintenance strategies. It also supports environmental sustainability by preventing unnecessary oil changes and reducing waste.