The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give features extraction and pattern grouping of the issue to distinguish the failure in an induction motor using classification models. In this paper, the open-source dataset of the rotor with the broken bars in a three-phase induction motor available on the IEEE data port is used for fault classification. The study aims at fault identification under various loading conditions on the rotor of an induction motor by performing time, frequency, and time-frequency domain feature extraction. The extracted features are provided to the models to classify between the healthy and faulty rotors. The extracted features from the time and frequency domain give an accuracy of up to 87.52% and 88.58%, respectively, using the Random-Forest (RF) model. Whereas, in time-frequency, the Short Time Fourier Transform (STFT) based spectrograms provide reasonably high accuracy, around 97.67%, using a Convolutional Neural Network (CNN) based fine-tuned transfer learning framework for diagnosing induction motor rotor bar severity under various loading conditions.

Studies on wear properties of Aluminium based hybrid nano composite materials, processed through powder metallurgy technique, are reported in the present study. Silicon Carbide nano particles and E-glass fibre are reinforced in pure aluminium matrix to fabricate hybrid nano composite material samples. Pin-on-Disc wear testing equipment is used to evaluate dry sliding wear properties of the composite samples. The tests were conducted following the Taguchi's Design of Experiments method. Signal-to-Noise ratio analysis and Analysis of Variance are carried out on the test data to find out the influence of test parameters on the wear rate. Scanning Electron Microscopic analysis and Energy Dispersive X-Ray analysis are conducted on the worn surfaces to find out the wear mechanisms responsible for wear of the composites. Multiple linear regression analysis and Genetic Algorithm techniques are employed for optimization of wear test parameters to yield minimum wear of the composite samples. Finally, a wear model is built by the application of Artificial Neural Networks to predict the wear rate of the composite material, under different testing conditions. The predicted values of wear rate are found to be very close to the experimental values with a deviation in the range of 0.15 % to 8.09%.

The Plasma Electrolytic Method of Oxidation (PEMO ) is proved powerful for producing the wear and corrosion-resistant coating of the oxide layer for exclusive applications. This technique is having a better capability for industrial use for growing proactive coatings on material like Aluminium, Magnesium, Titanium for diverse application of lightweight and high temperature requirements applications. The paper consists of review of papers primarily based on the surface modification amendment method of plasma arc oxidation of aluminium and its alloy. Because the process characterized by the discharge generated by electrode, electrolyte composition and oxide coating morphology which might be crucial component for effectiveness of the technique. The purpose of the paper is to write the systematic review for electrodes, electrolyte compositions, and its effect in improving the characteristics of aluminium alloys. There is a large scope for enhancing the method and monitoring the electric parameters of PEMO for massive applications in regions of automotive wherein the modification in surfaces can replace the components by the lightweight aluminium alloy material. The attempt is made to pick the key elements inside the technique. The scope of designing numerous optimization algorithms and analytical modelling is a key area for further research which can help to screen the technique extra precisely to get an optimized coating microstructure.

The plasma electrolytic method of oxidation (PEO) is a technique that is increasingly being used in industry to produce protective coatings and other surface treatments. The emphasis is then placed on the dielectric breakdown that repeatedly occurs across the surface of the workpiece. There is significant potential for more effective process control. Key study areas include the interrelation of electrical conditions, electrolyte composition, coating microstructure, and growth rates. With specific goals in terms of coating performance and energy efficiency, an attempt is made to identify key points that are likely to simplify this. The research aims to identify the optimized range of input parameters when the oxide layer is formed using the DC supply in the silicate-based electrolyte on Al 6061 substrate. For developing an oxide layer on Al 6061, a silicate-based electrolyte containing Na ₂ SiO ₃ (10g/L), KOH (2g/L), and KMnO ₄ 3 (g/lit) is applied. At the optimal parameter of DC supply with 190 V and 1.5 A, the maximal adoption of Mn particles was observed to be 34 percent.