fuzzy logic model
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In this paper, a new fuzzy logic (FL) model is proposed for assessing the health status of power transformers. In addition, the detection of incipient faults is achieved where two or more faults exist simultaneously. The process is carried out by integrating a fuzzy logic model with the conventional International Electric Committee (IEC) ratio codes method. As transformer oil insulation deteriorates, excess percentages of dissolved gases such as hydrogen, methane, ethane, acetylene and ethylene are induced within the trasnformer. The status of oil health is generally assessed using these gas concentrations. Therefore, in the proposed model, 31 fuzzy rules are designed based on the severity levels of these gases in order to determine the health index (HI) of the oil. Similarly, any incipient faults along with their severity are also detected using the proposed fuzzy logic model with 22 expert rules. To validate the proposed fuzzy logic model, the data for dissolved gases in 50 working transformers operated by the Himachal Pradesh State Electricity Board (HPSEB), India, are collected. Over the years, calculations for the health index have been performed using conventional dissolved gas analysis (DGA) interpretation methods. The shortcomings of these methods, such as non-reliability and inaccuracy, are successfully overcome using the proposed model. The detection of incipient faults is normally performed using key gas, Rogers ratios, the Duval triangle, Dornenburg ratios, modified Rogers ratios and the IEC ratio codes methods. The shortcomings of these conventional ratio code methods in identifying incipient faults in some typical cases, ie multiple incipient fault cases, are overcome by the proposed fuzzy logic model.

From last two decades, plant fiber reinforced polymer/polyester composites have been effectively used in structural and automotive applications. Researchers and manufacturers are looking forward for an effective utilization of these composites. However, despite the outstanding properties in terms of load bearing capacity and environmental sustainability of plant fibers the uptake of these composites are limited due to its poor machinability characteristics. Hence in this paper, Taguchi based fuzzy logic model for the optimization and prediction of process output variable such as surface roughness during Abrasive Water Jet Machining (AWJM) of new class of plant fiber reinforced polyester composites i.e., Discontinuously Reinforced Caryota Urens Fiber Polyester (DRCUFP) composites has been explored. Initially machining experiments has been carried out using L27 orthogonal array obtained from Taguchi Design of Experiments (TDOE). Finally, Taguchi based fuzzy logic model has been developed for optimisation and prediction of surface roughness. From the extensive experimentation using TDOE it was observed that the optimum cutting conditions for obtaining minimum surface roughness value, water pressure (A): 300 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 12 g/s, depth of cut (C): 5 mm and Abrasive Size:200 microns. Further from FLM, it is observed that minimum water pressure (A): 100 bar, traverse speed (B): 50 mm, stand of distance: 1 mm, abrasive flow rate: 8 g/s, depth of cut (C): 5 mm and abrasive size:100 microns gave higher surface roughness values (3.47 microns) than that at maximum water pressure (A): 300 bar, traverse speed (B): 150 mm, stand of distance: 4 mm, abrasive flow rate: 12 g/s, depth of cut (C): 15 mm and abrasive size:200 microns the surface roughness values (3.25 microns).

Algal blooms are one of the most serious threats to water resources, and their early detection remains a challenge in eutrophication management worldwide. In recent years, with more widely available real-time auto-monitoring data and the advancement of computational capabilities, fuzzy logic has become a robust tool to establish early warning systems. In this study, a framework for an early warning system was constructed, aiming to accurately predict algae blooms in a river containing several water conservation areas and in which the operation of two tidal sluices has altered the tidal currents. Statistical analysis of sampled data was first conducted and suggested the utilization of dissolved oxygen, velocity, ammonia nitrogen, total phosphorus, and water temperature as inputs into the fuzzy logic model. The fuzzy logic model, which was driven by biochemical data sampled by two auto-monitoring sites and numerically simulated velocity, successfully reproduced algae bloom events over the past several years (i.e., 2011, 2012, 2013, 2017, and 2019). Considering the demands of management, several key parameters, such as onset threshold and prolongation time and subsequent threshold, were additionally applied in the warning system, which achieved a critical success index and positive hit rate values of 0.5 and 0.9, respectively. The differences in the early warning index between the two auto-monitoring sites were further illustrated in terms of tidal influence, sluice operation, and the influence of the contaminated water mass that returned from downstream during flood tides. It is highlighted that for typical tidal rivers in urban areas of South China with sufficient nutrient supply and warm temperature, dissolved oxygen and velocity are key factors for driving early warning systems. The study also suggests that some additional common pollutants should be sampled and utilized for further analysis of water mass extents and data quality control of auto-monitoring sampling.