Infrared thermography technology is one of the most effective non-destructive testing techniques for predictive faults diagnosis of electrical components. Faults in electrical system show overheating of components which is a common indicator of poor connection, overloading, load imbalance or any defect. Thermographic inspection is employed for finding such heat related problems before eventual failure of the system. However, an automatic diagnostic system based on artificial neural network reduces operating time, human efforts and also increases the reliability of system. In the present study, statistical features and artificial neural network (ANN) with confidence level analysis are utilized for inspection of electrical components and their thermal conditions are classified into two classes namely normal and overheated. All the features extracted from images do not produce good performance. Features having low performance reduce the diagnostic performance. The study reveals the performance of each feature individually for selecting the suitable feature set. In order to find the individual feature performance, each feature of thermal image was used as input for neural network and the classification of condition types were used as output target. The multilayered perceptron network using Levenberg-Marquardt training algorithm was used as classifier. The performances were determined in terms of percentage of accuracy, specificity, sensitivity, false positive and false negative. After selecting the suitable features, the study introduces the intelligent diagnosis system using suitable features as inputs of neural network. Finally, confidence percentage and confidence level were used to find out the strength of the network outputs for condition monitoring. The experimental result shows that multilayered perceptron network produced 79.4% of testing accuracy with 43.60%, 12.60%, 21.40, 9.20% and 13.40% highest, high, moderate, low and lowest confidence level respectively.
Time-lapse resistivity measurements and groundwater geochemistry were used to study salinity effect on groundwater aquifer at the ex-promontory-land of Carey Island in Malaysia. Resistivity was measured by ABEM Terrameter SAS4000 and ES10-64 electrode selector. Relationship between earth resistivity and total dissolved solids (TDS) was derived, and with resistivity images, used to identify water types: fresh (ρ ( e ) > 6.5 Ω m), brackish (3 Ω m < ρ ( e ) < 6.5 Ω m), or saline (ρ ( e ) < 3 Ω m). Long-term monitoring of the studied area's groundwater quality via measurements of its time-lapse resistivity showed salinity changes in the island's groundwater aquifers not conforming to seawater-freshwater hydraulic gradient. In some aquifers far from the coast, saline water was dominant, while in some others, freshwater 30 m thick showed groundwater potential. Land transformation is believed to have changed the island's hydrogeology, which receives saltwater pressure all the time, limiting freshwater recharge to the groundwater system. The time-lapse resistivity measurements showed active salinity changes at resistivity-image bottom moving up the image for two seasons' (wet and dry) conditions. The salinity changes are believed to have been caused by incremental tide passing through highly porous material in the active-salinity-change area. The study's results were used to plan a strategy for sustainable groundwater exploration of the island.
Microplastics (MPs) are generated from plastic and have negative impact to our environment due to high level of fragmentation. They can be originated from various sources in different forms such as fragment, fiber, foam and so on. For detection of MPs, many techniques have been developed with different functions such as microscopic observation, density separation, Raman and FTIR analysis. Besides, due to ingestion of MPs by wide range of marine species, research on the effect of this pollution on biota as well as human is vital. Therefore, we comprehensively reviewed the occurrence and distribution of MPs pollution in both marine and freshwater environments, including rivers, lakes and wastewater treatment plants (WWTPs). For future studies, we propose the development of new techniques for sampling MPs in aquatic environments and biota and recommend more research regarding MPs release by WWTPs.