The induction motor (IM) defect diagnosis has been an important field of research in recent years. The development in control circuits for IM has piqued the interest of industrialists and researchers. This paper presents a method for detecting and quantifying broken rotor bar (BRB) faults via wavelets and energy Eigen value (EEV) estimation in voltage/frequency control-fed IM. The fast Fourier transform (FFT) extracts the signal’s amplitude and frequency components, while the discrete wavelet transform (DWT) decomposes it. In this paper, the energy estimation for each level of breakdown and the method to overcome the diagnose faults are explained. The EEV of the motor current of the signal determines the fault’s severity and provides a better method for identifying the faults. The usage of a single current sensor is a gain of this technology. With a fluctuating load, we can identify the issue and the number of broken bars via online. After processing of DWT, the faulty BRB’s stator current signal is suppressed to 91% in amplitude when compared to existing techniques. Simulation and experimental results have proved that the proposed method’s stability, durability, and resilience.

This paper presents an approach to estimate the orientation of the rectangular defect in the ferromagnetic specimen using the magnetic flux leakage technique. Three components of the magnetic flux leakage profile, such as radial, axial, and tangential component are considered to estimate the orientation of the rectangular defect. The orientation of the rectangular defect is estimated by the proposed analytical model using MATLAB software. The results calculated by the analytical model are validated by the three-dimensional finite element analysis using COMSOL Multiphysics software. Tangential component provides better performance to estimate the orientation of the rectangular defect compared with radial and axial component of the magnetic flux leakage profile.

To meet users’ expectations for speed and reliability, 5th Generation (5G) networks and other forms of mobile communication of the future will need to be highly efficient, flexible, and nimble. Because of the expected density and complexity of 5G networks, sophisticated network control across all layers is essential. In this context, self-organizing network (SON) is among the essential solutions for managing the next generation of mobile communication networks. Self-optimization, self-configuration, and self-healing (SH) are typical SON functions. This research creates a framework for analyzing SH by exploring the impact of recovery measures taken in precarious stages of health. For this reason, our suggested architecture takes into account both detection and compensating operations. The system is broken down into some faulty states and the “fuzzy c-means” (FCM) approach is used to conduct the classifying. In the compensation process, the network is characterized as the Markov decision model (MDM), and the linear programming (LP) technique is implemented to find the most effective strategy for reaching a goal. Numerical findings acquired from a variety of situations with varying objectives show that the suggested method with optimized operations in the compensation stage exceeds the approach with randomly chosen actions.

In Electric Vehicle (EV) application, the voltage conversion is significant to obtain the desired operating voltage from the source voltage. A conventional boost converter can handle such applications, but it may add losses throughout the conversion process. This work focuses on the design and implementation of a multi device Interleaved DC-DC converter with greater voltage gain, lower voltage stress across the switch, and improved efficiency when compared to the standard Boost converter and conventional Interleaved Converter. The suggested converter has three times the voltage gain of a standard Boost DC-DC converter. These converters are used in applications that demand a constant DC voltage, such as electric vehicles. The proposed converter’s mathematical modelling and modes of operation are discussed. The proposed DC-DC converter’s feasibility is validated using real-time simulation (OPAL-RT), and the results are presented in detail.

The most frequent cause of vehicle accidents (car, bike, truck, etc.) is the unexpected existence of barriers while driving. An automated braking system will assist and minimize such collisions and save the driver and other people’s lives and have a substantial influence on driver safety and comfort. An autonomous braking system is a complicated mechatronic system that incorporates a front-mounted ultrasonic wave emitter capable of creating and transmitting ultrasonic waves. In addition, a front-mounted ultrasonic receiver is attached to gather ultrasonic wave signals that are reflected. The distance between the impediment and the vehicle is determined by the reflected wave. Then, a microprocessor is utilized to control the vehicle’s speed depending on the detected pulse information, which pushes the brake pedal and applies the vehicle’s brakes extremely hard for safety. For work-energy at surprise condition for velocity 20 km/hr, the braking distance is 17.69 m, and for velocity 50 km/hr, the braking distance is 73.14.