Application of machine learning for inter turn fault detection in pumping system.

Pump fault diagnosis is essential for the maintenance and safety of the device as it is an important appliance used in various major sectors. Fault diagnosis at the proper time can reduce maintenance costs and save energy. This article uses a Simulink model based on mathematical equations to analyze the effects of parameter estimation of three-phase induction motor-based centrifugal pumps in inter-turn fault conditions. The inter-turn fault causes a massive in, a massive increase in current, which severely affects the parameters of both motor and pump. These have been analyzed by simulation through the Matlab Simulink model. Later, the results are verified by a hardware in loop (HIL) based simulator. In this paper, machine learning (ML) based artificial neural network (ANN) and ANFIS (ANN and Fuzzy) models have been applied for fault detection. ANN and ANFIS-based models provide a satisfactory level of accuracy. These models provide accurate training and testing results. Based on root mean square error (RMSE), R2, prediction accuracy, and mean validation value, these models are compared to find out which is more suitable for this experiment. Various supervised algorithms are compared with ANN, ANFIS, and lastly, found which is the most suitable for this experiment.

Printable Gel Polymer Electrolytes for Solid-State Printed Supercapacitors.

Supercapacitors prepared by printing allow a simple manufacturing process, easy customization, high material efficiency and wide substrate compatibility. While printable active layers have been widely studied, printable electrolytes have not been thoroughly investigated despite their importance. A printable electrolyte should not only have high ionic conductivity, but also proper viscosity, small particle size and chemical stability. Here, gel-polymer electrolytes (GPE) that are compatible with printing were developed and their electrochemical performance was analyzed. Five GPE formulations based on various polymer-conductive substance combinations were investigated. Among them, GPE made of polyvinylidene difluoride (PVDF) polymer matrix and LiClO4 conductive substance exhibited the best electrochemical performance, with a gravimetric capacitance of 176.4 F/g and areal capacitance of 152.7 mF/cm2 at a potential scan rate of 10 mV/s. The in-depth study of the in-plane solid-state supercapacitors based on various printed GPEs suggests that printable electrolytes provide desirable attributes for high-performance printed energy devices such as supercapacitors, batteries, fuel cells and dye-sensitized solar cells.