Realtime Lubricating Oil Analysis to Predict Equipment Failure

Abstract

Oil condition monitoring for rotating and reciprocating equipment has typically been laboratory based. A technician or engineer collects a sample of lubricating oil and sends this to a laboratory for chemical analysis. After the laboratory has performed the analysis the results are sent to the engineer to make decisions on the health and/or condition of the machinery. This process can take up to 6 weeks, and consequently analysis may end up being performed only quarterly with little likelihood of critical failures being pre-empted. The slowness of oil condition monitoring analyses performed in laboratories has led engineers to substitute for real-time monitoring methods such as vibration analysis and thermography. Nevertheless, the chemical composition of the lubricating oil remains the gold standard for the diagnosis of machine health. The automation of methods for analysing the chemical composition of lubricating oil in real-time would provide engineers with data on the immediate condition of a particular piece of machinery, allowing the early diagnosis of incipient faults. In this paper, we present a microfluidic technique that can perform real-time continuous monitoring of the chemical composition of lubricating fluid from rotating and reciprocating equipment. Results from this technique both in laboratory and field environments are comparable to conventional laboratory measurements. The microfluidic technique exploits the flow of fluids within micrometre-dimensioned channel, permitting liquid-liquid diffusive separation between otherwise miscible non-aqueous fluids. It can be shown that several fluids e.g. methanol, hexane etc. can selectively extract target components in lubricating oil. Following an extraction, these components can be quantified using a combination of optical techniques, e.g. UV/Vis, Infrared etc. This microfluidic technique has been demonstrated for a range of lubricating oils with several acid, alkaline detergent, asphaltene/insoluble content. This technology can potentially revolutionise the way oil analysis is carried out, automating and making the process rapid and in real-time.

Online Monitoring and Analysis of Lube Oil Degradation for Gas Turbine Engine using Recurrent Neural Network (RNN)

“…Based on results from prediction, it needs more work to get better method than current research by combination of method and or modify layer of the method. Beside on the lube oil temperature prediction, next research should consider also to add other oil properties measurement system such as LOAC (Lab-On-A-Chip) [15] for more accurate analysis. In the future, prediction system will be embedded in to Turbine control and monitoring system as added value of artificial neural network instead only automatic control system.…”