Rahul Kumar

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• Ph.D in Industrial Engineering
• Senior Lecturer in Electrical & Electronics Engineering at University of the South Pacific

Fault detection of shorted turns in the stator windings of Induction Motors (IMs) is possible in a variety of ways. As current sensors are usually installed together with the IMs for control and protection purposes, using stator current for fault detection has become a common practice nowadays, as it is much cheaper than installing additional senso...

Accurate gear defect detection in induction machine-based systems is a fundamental issue in several industrial applications. At this aim, shallow neural networks, i.e. architectures with only one hidden layer, have been used after a feature extraction step from vibration, torque, acoustic pressure and electrical signals. Their additional complexity...

This paper presents a two-stage fault detection and classification scheme specifically designed for rotating electrical machines. The approach involves the use of new condition indicators that are specific to the frequency domain. The paper proposes two distinct features: one based on the extraction of peaks by using the prominence measure, a techn...

This paper presents a data driven approach for the classification of faults in induction machines. The designed scheme involves newly engineered features extracted from the line current signals, which provides an improved fault discrimination. For this purpose, a topological based fast projection technique (Curvilinear Component Analysis) is used a...

This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visi...

The rapid advancement of industrial technologies has underscored the importance of effective diagnosis and prognosis in equipment maintenance to ensure safe operations. This is particularly critical in rotating machinery (RM), where bearing components play a pivotal role in determining the health and lifespan of systems such as wind turbines and hi...

This paper presents an experimental investigation into the detection and classification of broken rotor bar (BRB) faults in a 1.1 kW squirrel cage induction motor (IM) across various load conditions and fault severities: 1.5 BRBs, 2 BRBs, 2.5 BRBs, and 3 BRBs. Motor current signature analysis (MCSA), fast Fourier transform (FFT), and the extended P...

This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particul...

Proton Exchange Membrane Fuel Cell (PEMFC) systems represent a crucial clean energy component, offering a sustainable alternative to traditional power sources. However, ensuring the dependable and optimal performance of PEMFC systems is essential for their widespread adoption and integration into various applications, ranging from transportation to...

This paper introduces a novel fault diagnosis approach for two-level Voltage Source Inverters in Motor Drives, using Shallow Neural Networks. A key feature of this study is the implementation of three different Classifiers: two are designed to both detect and diagnose faults, while the third is specialized in fault detection and localization. The s...

Ground simulation methods have gained significant research attention due to their effectiveness. We propose a ground simulation method for space laser communication with a transmission distance of 53,000 km in free space using the laser as a means for information transmission. The simulation is verified using artificial intelligence and pattern rec...

Gears are widely recognized as crucial components for power connections and transmission owing to their substantial loading capacity, transmission efficiency, and consistent power output. Given their complexity, they are prone to faults and require continuous condition monitoring (CM). The study proposes an innovative approach that combines signal...

This study introduces a novel approach for fault classification in bearing components utilizing raw accelerometer data. By employing various neural network models, including deep learning architectures, we bypass the traditional preprocessing and feature-extraction stages, streamlining the classification process. Utilizing the Case Western Reserve...

The purpose of an Automatic Parking system is to make it easier for drivers to find an available parking space and provide a fee generation-based exit system. Ideally, heavy labor is required for this type of job thus the paper focuses on using license plate recognition to detect vehicles and its number plate and develop a simple algorithm for park...

This paper presents an evaluation and testing of state-of-the-art object detectors, including Fast RCNN with ResNet18, Faster RCNN with VGG16 and YOLOv4 with ResNet101. They were trained using the same hyperparameters, with slight adjustments made if needed. YOLOv4 emerged as the top performer, achieving an AP0.5 of 90% and mAP of 55%, while Faster...

There can be serious and harmful effects on people following a fall event due to its severity. This paper presents a real-time fall detection using sensor fusion to improve the overall accuracy of the system when subjected to continuous operation. The system combines data from accelerometer and an ultrasonic sensor to detect falls in real time. The...

With the rapid development of robotics, autonomous rovers are finding numerous applications in the automation industry. This research paper aims to present an approach to designing an autonomous rover that can detect and avoid obstacles through advanced image-processing techniques. The proposed rover operates in real time, utilizing the Raspberry P...

This chapter is based on the prediction of MoRF regions within the intrinsically disordered protein sequence. Disordered proteins have molecular recognition regions (MoRF) making them highly attractive to bind with protein pairs. Thus, as they combine with other protein pairs, they undergo disorder-to-order transition making them essential for vari...

Ensuring reliability and safety in multilevel power converter topologies requires effective fault diagnostics, especially considering the greater number of components involved compared to traditional converters. Recent research has recognized the potential of Artificial Neural Networks (ANN) in enhancing diagnostic strategies. In this context, this...

This study proposes a neural-based approach to classify ECG signals acquired from a low-cost wearable device as an early warning system for possible heart diseases. The aim is to recognize ECG signals into respective classes, including normal sinus rhythm and different types of arrhythmia, while adhering to FDA approved IEC standards for wearable m...

Brugada Syndrome (BrS) is an arrhythmic disorder which increases the probability of developing arrhythmic events, even life-threatening ones, in young and otherwise healthy individuals. It accounts for 5–20% of sudden deaths in people with no structural cardiac abnormality. The first clinical manifestation of this syndrome is, usually, a cardiac ar...

This paper presents an estimation of the parameters for a Double Layer Super Capacitor (DLC) that is modelled with a two-branch circuit. The estimation is achieved using a constrained minimization technique, which is developed off-line and uses a single constraint to write the matrix equation. The model is algebraically manipulated to obtain a matr...

Fiji's location makes it vulnerable to extreme weather and natural disasters, resulting in heavy rainfall, poor drainage, and deteriorating road structures. Consequently, potholes have become more common, leading to increased vehicle part replacements. The authorities require assistance in locating and quantifying potholes, as monitoring road crack...

This paper presents a cost effective design of a wearable wireless tongue drive system (TDS) for disabled individuals, particularly with spinal cord injuries. We propose a basic TDS whose language is specifically designed for issuing movement based commands, be it a gadget or a self-governing transport chair. While the overall industrial penetratio...

Hydrogen energy conversion using Fuel Cells is very promising for standalone power as well as transportation applications. Hydrogen gas production using renewable energy sources is possible through the use of electrolyzers in which DC- DC converters play an important role. This paper presents an accurate and robust method of fault diagnosis and...

Detection of stator-based faults in Induction Machines (IMs) can be carried out in numerous ways. In particular, the shorted turns in stator windings of IM are among the most common faults in the industry. As a matter of fact, most IMs come with pre-installed current sensors for the purpose of control and protection. At this aim, using only the sta...

This paper presents a new condition indicator for classifying of stator and rotor related faults in induction motors. It relies on the characteristic fault frequencies of the motor in question and can be extended to different types of motors with different magnetic structures. The proposed method, occupied band-power ratio, focuses on the power con...

The paper aims at the definition of proper indicators for the effective detection of stator inter-turn faults in line connected induction motors. The procedure uses a finite element model related to a standard medium rated motor, tuned taking into account both magnetic saturation and the actual winding scheme. After the experimental check with refe...

Fault diagnostics for electrical machines is a very difficult task because of the non-stationarity of the input information. Also, it is mandatory to recognize the pre-fault condition in order not to damage the machine. Only techniques like the principal component analysis (PCA) and its neural variants are used at this purpose, because of their sim...

Fault diagnostics for electrical machines is a very difficult task because of the non-stationarity of the input information. Also, it is mandatory to recognize the pre-fault condition in order not to damage the machine. Only techniques like the Principal Component Analysis (PCA) and its neural variants are used at this purpose, because of their sim...

The authors propose a preliminary design and development of an assistive technology, which addresses the problem for people with disabilities to communicate with learning environments. An assistive Tongue Drive System (TDS) has been proposed which permits the end user to make use of their tongue for communication. In this paper, the hardware/softwa...

This paper presents a subspace-based approach to identify and extract harmonics of interest for the diagnosis of stator and rotor related faults in induction machines. The major goal of this paper is firstly to introduce and highlight the effectiveness of prominence measure upon preparing features for classification of faults. Secondly, a new appro...

This paper presents a data driven approach where at first the most significant features of the three phase current signal are analyzed and then a Curvilinear Component based analysis (CCA), which is a nonlinear manifold learning technique, is performed to compress and interpret the feature behaviour. Finally, a multi-layer perceptron network is use...

This paper presents a fault diagnosis and classification scheme for induction machines by using motor current signature analysis together with neural networks. The adopted strategy utilizes three-phase stator current sensors and calculates appropriate features using non-parametric and a statistical approach. The feature-set is reduced by means of t...

This paper presents a simple linear control based force feedback for the gripper of a SCORBOT ER-4u robotic arm. The SCORBOT ER-4u is a 5 degree of freedom (DOF) dexterous robotic arm with a rigid 2-fingered parallel configuration gripper. A Flexi-Force Force Sensitive Resistor (FSR) is attached to one of the claws of the gripper and interfaced to...

A quick development of innovation moves us to plan the best choice for an accurate mission. Numerous independent automated innovations are intimated in the lives of individuals making their work much easier. It has been seen that automated vehicles are presented so far, with shrewd abilities after enormous measures of cash spent yearly on the exami...

This paper presents the development of a linear control based force feedback system for a scorbot robotic arm. The scorboter-4u is a 5 degree of freedom (DOF) robotic arm with a 2-fingered parallel configuration gripper. A flexi-force force sensitive Resistor (FSR) is attached to one of the claws of the gripper and interfaced to a laptop computer c...

This paper presents the kinematic analysis of the SCORBOT-ER 4u robot arm using a Multi-Layered Feed-Forward (MLFF) Neural Network. The SCORBOT-ER 4u is a 5-DOF vertical articulated educational robot with revolute joints. The Denavit-Hartenberg and Geometrical methods are the forward kinematic algorithms used to generate data and train the neural n...

Controlling a Robotic arm for applications such as object sorting with the use of vision sensors would need a robust image processing algorithm to recognize and detect the target object. This paper is directed towards the development of the image processing algorithm which is a pre-requisite for the full operation of a pick and place Robotic arm in...