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Published in Heinz P. Bloch, Kenneth E. Bannister, Practical Lubrication for Industrial Facilities, 2020
Heinz P. Bloch, Kenneth E. Bannister
Oil analysis is a highly effective and inexpensive method used to determine when to change oil based on its condition, to predict incipient bearing failure so that relevant action can be taken in a timely manner to avert failure and to diagnose bearing failure should it occur. Despite being around since the 1940’s and its proven track record for effectiveness, oil analysis is still misunderstood and overlooked as a proactive / predictive maintenance strategy in many of today’s industrial plants! Setting up and implementing an industrial oil analysis program is relatively easy and should be an included strategy in any industrial plant that purchases, stores, dispenses, changes, uses, or recycles lubricants as part of its manufacturing or maintenance process.
Wind Turbine Lubrication
Published in Leslie R. Rudnick, Synthetics, Mineral Oils, and Bio-Based Lubricants, 2020
Many performance parameters of fresh wind turbine lubricants will change along with usage time. Each wind turbine is different in gearbox installation process, contamination, wind turbine location, environment, usage, etc. Used oil analysis is used to monitor the lubricant condition against the established oil condemning limit for each parameter. Certain parameters change over time. Wear metal level may increase due to wear; TAN may increase due to oxidation; oil cleanliness may become poorer due to wear, oxidation, and contamination; oil viscosity may rise due to oxidation or fall due to shear loss; P or S levels may decrease from depletion of EP/AW additives. All parameters should be within the condemning limits before the expected oil change time (Figure 48.6).
Principles of Energy Conversion
Published in Hamid A. Toliyat, Gerald B. Kliman, Handbook of Electric Motors, 2018
Hamid A. Toliyat, Gerald B. Kliman
The analysis of lubricant samples taken from the scavenge line ahead of the filter in a circulating oil system or from a static sump can provide valuable information on the bearing and lubricant condition. Analysis should include physical and chemical properties of the oil, that is, viscosity, acid number, water, coolant, fuel and solids contamination, and wear particle analysis. The results of oil sample analysis should be plotted versus time; the trend analysis provides advance warning of bearing damage before failure occurs. Considerable savings in maintenance costs can be achieved by scheduling inspections and replacement of damaged bearings before failure occurs, with possible damage to related components beyond the bearing. In new machinery, oil analysis can be used to prevent costly premature failures by detection of damage due to errors in design, manufacture, and assembly of components.
An improved control-limit-based principal component analysis method for condition monitoring of marine turbine generators
Published in Journal of Marine Engineering & Technology, 2020
Kun Yang, Biao Hu, Reza Malekian, Zhixiong Li
The widespread deployment of turbine generators raises increasing requirement on a high-standard condition monitoring strategy to guarantee the safe operation of the turbines (Katnam et al. 2015). This is particularly true for marine power plants, where the condition monitoring and fault diagnosis will magnificently benefit the industries, national security and people's livelihood. The recent development of lubrication oil-based condition monitoring and degradation detection techniques can greatly improve the operational availability and reducing the maintenance cost of marine turbine generators. In the past decades, scientists and experts have developed considerable sensors and systems to measure one or more physical and chemical indicators of oil in order to effectively monitor the oil condition of turbine lubrication (Cheng et al. 2012; Katnam et al. 2015). Lubricating oil analysis has been proved in many applications to be a more advanced indicator technique than vibration analysis identification of early-stage failures in a mechanical system (Roylance et al. 2000; Gresham 2008; Ahmadi and Mollazade 2009). The main limitation of lubricating oil analysis is that the failure indicators produce inconsistent/contradictory conclusions in the failure detection/prediction. Consequently, the analysis result of the lubricating oil analysis may confuse the users. As a result, how to extract useful features and eliminate redundancy from lubricant monitoring indicators is a challenge for lubricating oil analysis (Zhao et al. 2011).