Demonstrate broadband over power line network in Malaysia

Osman, Wan Rozaini Sheik; Nisar, Kashif; Altrad, Abdallah Mahmoud Mousa

doi:10.1109/icce-china.2014.7029864

Cited by 6 publications

(1 citation statement)

References 10 publications

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“…During normal operation, these motors are exposed to many kinds of mechanical and electrical stresses that can cause severe failures. Examples of major mechanical faults include such events as broken rotor bars [1][2][3] and bearing faults [4][5][6][7]. In general, electrical faults include insulation failure, short circuits, and supply problems and are influenced by the quality of the power supply, which might exhibit voltage unbalances and variations in supply frequency under varying load conditions [8,9].…”

Section: Introductionmentioning

confidence: 99%

Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

Hussain¹,

Soother²,

Kalwar³

et al. 2021

IASC

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Induction motors (IMs) are the workhorse of the industry and are subjected to a harsh environment. Due to their operating conditions, they are exposed to different kinds of unavoidable faults that lead to unscheduled downtimes and losses. These faults may be detected early through predictive maintenance (i.e., deployment of condition monitoring systems). Motor current signature analysis (MCSA) is the most widely used technique to detect various faults in industrial motors. The stator winding faults (SWF) are one of the major faults. In this paper, we present an induction motor fault detection and identification system using signal processing techniques such as fast Fourier transform (FFT), short-time Fourier transform (STFT), and continuous wavelet transform (CWT). A three-phase motor model is developed in MATLAB Simulink and simulated under various fault conditions. The current signature is observed using FFT, spectrogram, and scalogram to detect the faults. It is observed that under some fault conditions, the current signature analysis remains indistinguishable from the non-fault case. Therefore, deep learning (DL) methods are adopted here to identify these faults. The time-series data of healthy and unhealthy conditions are obtained from the simulation results. The comparative investigation among DL models confirmed the superiority of the long short-term memory (LSTM) model, which achieved 97.87% accuracy in identifying the individual faults.

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Section: Introductionmentioning

confidence: 99%

Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

Hussain¹,

Soother²,

Kalwar³

et al. 2021

IASC

Self Cite

View full text Add to dashboard Cite

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Blind Signal Modulation Classification Using Constellation Pattern Analysis with Oversampling Factor Alteration

Kumar

Jajoo

Kumar

et al. 2022

Wireless Pers Commun

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Designing obstacle’s map of an unknown place using autonomous drone navigation and web services

Tanwar

Sharma

Mittal

2022

IJPCC

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Purpose Drones are used in several purposes including examining areas, mapping surroundings and rescue mission operations. During these tasks, they could encounter compound surroundings having multiple obstacles, acute edges and deadlocks. The purpose of this paper is to propose an obstacle dodging technique required to move the drones autonomously and generate the obstacle's map of an unknown place dynamically. Design/methodology/approach Therefore, an obstacle dodging technique is essentially required to move autonomously. The automaton of drones requires complicated vision sensors and a high computing force. During this research, a methodology that uses two basic ultrasonic-oriented proximity sensors placed at the center of the drone and applies neural control using synaptic plasticity for dynamic obstacle avoidance is proposed. The two-neuron intermittent system has been established by neural control. The synaptic plasticity is used to find turning angles from different viewpoints with immediate remembrance, so it helps in decision-making for a drone. Hence, the automaton will be able to travel around and modify its angle of turning for escaping objects during the route in unknown surroundings with narrow junctions and dead ends. Furthermore, wherever an obstacle is detected during the route, the coordinate information is communicated using RESTful Web service to an android app and an obstacle map is generated according to the information sent by the drone. In this research, the drone is successfully designed and automated and an obstacle map using the V-REP simulation environment is generated. Findings Simulation results show that the drone effectively moves and turns around the obstacles and the experiment of using web services with the drone is also successful in generating the obstacle's map dynamically. Originality/value The obstacle map generated by autonomous drone is useful in many applications such as examining fields, mapping surroundings and rescue mission operations.

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Demonstrate broadband over power line network in Malaysia

Cited by 6 publications

References 10 publications

Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

Stator Winding Fault Detection and Classification in Three-Phase Induction Motor

Blind Signal Modulation Classification Using Constellation Pattern Analysis with Oversampling Factor Alteration

Designing obstacle’s map of an unknown place using autonomous drone navigation and web services

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