Muhammad Ammar Akram

Bio: Muhammad Ammar Akram is an academic researcher from HITEC University. The author has contributed to research in topics: Fault (power engineering) & Fault detection and isolation. The author has an hindex of 4, co-authored 6 publications receiving 115 citations. Previous affiliations of Muhammad Ammar Akram include University of Engineering and Technology.

Experimental investigation on graphene based nanoparticles enhanced phase change materials (GbNePCMs) for thermal management of electronic equipment

Abstract: The present study focuses on the thermal management of electronic components using nanoparticles enhanced phase change materials (NePCMs) based heat sinks for the reduction in the baseline temperature, to heighten the operational time and ensure the enhanced functionality and reliability of the working system. RT-44HC and RT-64HC are used as the base PCMs along with the incorporation of GNPs (0.002wt%, 0.005wt% and 0.008wt%) for different heating loads i.e. 0.86 KW/m2, 1.44 KW/m2 and 2.40 KW/m2. Results revealed that after 90 min of charging phase, RT-44HC/GNPs (0.008wt%) has depicted best performance with the maximum reduction of 25% in the baseline temperature for 0.86 KW/m2. Subsequently, at 1.44 KW/m2 RT-44HC/GNPs (0.005wt%) and RT-64HC/GNPs (0.008wt%) have shown maximum reduction of 13.6% and 11.8% respectively. For higher heating load i.e. 2.40 KW/m2 RT-64HC/GNPs has revealed best performance with maximum reduction of 16.37% in the temperature. Hence, for low heating loads, RT-44HC composites are best recommended whereas, RT-64HC composites are suitable for higher power levels and high heating loads.

System Design for Early Fault Diagnosis of Machines using Vibration Features

Abstract: The faults diagnosis of machines consists of feature extraction and classification of faults. The fault diagnosis process is based on the fact that each fault in a machine has a unique vibrational feature. Every industry wants a compact device or embedded system of low cost for the early faults diagnosis of machinery. This paper presents the platform that is focused at the embedded system design for the early faults diagnosis of machine(s) and classification of faults. We performed our experiment on the test rig apparatus and collected the vibration signals of four states of machine, those were: normal state, cracking state, offset pulley state and wear state. In segmentation, we use the empirical mode decomposition (EMD) technique. For classification purpose, we are using k-nearest neighbors (K-NN). Achievement of this research is that embedded system design for the classification of different faults in the machines. The overall accuracy of our experiment is 91.5%.

Vibration Based Gear Fault Diagnosis under Empirical Mode Decomposition and Power Spectrum Density Analysis

Abstract: Rotating machinery plays a significant role in industrial applications and covers a wide range of mechanical equipment. A vibration analysis using signal processing techniques is generally conducted for condition monitoring of rotary machinery and engineering structures in order to prevent failure, reduce maintenance cost and to enhance the reliability of the system. Empirical mode decomposition (EMD) is amongst the most substantial non-linear and non-stationary signal processing techniques and it has been widely utilized for fault detection in rotary machinery. This paper presents the EMD, time waveform and power spectrum density (PSD) analysis for localized spur gear fault detection. Initially, the test model was developed for the vibration analysis of single tooth breakage of spur gear at different RPMs and then specific fault was introduced in driven gear under different damage conditions. The data, recorded by means of a wireless tri-axial accelerometer, was then analyzed using EMD and PSD techniques and the results were plotted. The results depicted that EMD algorithms are found to be more functional than the ordinarily used PSD and time waveform techniques.