Tarique Rafique Memon^1,2,*, Tayab Din Memon^3,4, Imtiaz Hussain Kalwar⁵, Bhawani Shankar Chowdhry¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2461-2477, 2023, DOI:10.32604/cmc.2023.035413

Abstract Derailment of trains is not unusual all around the world, especially in developing countries, due to unidentified track or rolling stock faults that cause massive casualties each year. For this purpose, a proper condition monitoring system is essential to avoid accidents and heavy losses. Generally, the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy environment. Therefore, in this paper, we present the development of a novel embedded system prototype for condition monitoring of railway track. The proposed prototype system works in real-time by acquiring railway… More >

Amarjit Singh^*

Journal on Internet of Things, Vol.4, No.2, pp. 69-73, 2022, DOI:10.32604/jiot.2022.026170

Abstract Wireless sensor networks (WSNs) are created and affect our daily lives. You can find applications in various fields such as health, accident, life, manufacturing, production management, network management and many other fields. WSN now connects to the Internet of Things, connects the sensor to the Internet, and then uses it for collaboration and collaboration. However, when WSN is part of the internet we need to be able to study and analyze related terms. In this article, we’re going to look at different ways to get WSN online and identify the challenges that address in future as well. More >

Nebras M. Sobahi^1,*, Ahteshamul Haque², V S Bharath Kurukuru², Md. Mottahir Alam¹, Asif Irshad Khan³

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5757-5776, 2023, DOI:10.32604/cmc.2022.028340

Abstract Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic (PV) systems. In light of this requirement, this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment. To achieve this, different types of faults in grid-connected PV systems (GCPVs) and their impact on the energy loss associated with the electrical network are analyzed. A data-driven approach using neural networks (NNs) is proposed to achieve root cause analysis and localize the fault to the component level in the system.… More >

Sheetal A. Singh, Suresh S. Balpande^*

Sound & Vibration, Vol.56, No.3, pp. 209-220, 2022, DOI:10.32604/sv.2022.014518

Abstract As of April 2019, India has 1,42,126 kilometres of National Highways and 67,368 kilometres of railway tracks that reach even the most remote parts of the country. Bridges are critical for both passenger and freight movement in the country. Because bridges play such an important part in the transportation system, their safety and upkeep must be prioritized. Manual Condition Monitoring has the disadvantage of being sluggish, unreliable, and ineffi- cient. The Internet of Things has given structural monitoring a boost. Significant decreases in the cost of electronics and connection, together with the expansion of cloud platforms, have made it possible… More >

Majid Hussain^1,2,*, Tayab Din Memon^3,4, Imtiaz Hussain⁵, Zubair Ahmed Memon³, Dileep Kumar²

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 435-470, 2022, DOI:10.32604/cmes.2022.020583

Abstract Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown. Recently, Motor Current Signature Analysis (MCSA) is widely reported as a condition monitoring technique in the detection and identification of individual and multiple Induction Motor (IM) faults. However, checking the fault detection and classification with deep learning models and its comparison among themselves or conventional approaches is rarely reported in the literature. Therefore, in this work, we present the detection and identification of induction motor faults with MCSA and three Deep Learning (DL) models namely MLP, LSTM, and… More >

E. Akshay¹, V. Sugumaran^1,*, M. Elangovan²

Structural Durability & Health Monitoring, Vol.16, No.3, pp. 255-270, 2022, DOI:10.32604/sdhm.2022.0271

Abstract Tool wear is inevitable in daily machining process since metal cutting process involves the chip rubbing the tool surface after it has been cut by the tool edge. Tool wear dominantly influences the deterioration of surface finish, geometric and dimensional tolerances of the workpiece. Moreover, for complete utilization of cutting tools and reduction of machine downtime during the machining process, it becomes necessary to understand the development of tool wear and predict its status before happening. In this study, tool condition monitoring system was used to monitor the behavior of a single point cutting tool to predict flank wear. A… More >

Md. Mottahir Alam^1,*, Ahteshamul Haque², Mohammed Ali Khan³, Nebras M. Sobahi¹, Ibrahim Mustafa Mehedi^1,4, Asif Irshad Khan⁵

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3999-4017, 2022, DOI:10.32604/cmc.2022.026353

Abstract The shift towards the renewable energy market for carbon-neutral power generation has encouraged different governments to come up with a plan of action. But with the endorsement of renewable energy for harsh environmental conditions like sand dust and snow, monitoring and maintenance are a few of the prime concerns. These problems were addressed widely in the literature, but most of the research has drawbacks due to long detection time, and high misclassification error. Hence to overcome these drawbacks, and to develop an accurate monitoring approach, this paper is motivated toward the understanding of primary failure concerning a grid-connected photovoltaic (PV)… More >

A. D. Patange^1,2, R. Jegadeeshwaran^1,*, N. S. Bajaj², A. N. Khairnar², N. A. Gavade²

Sound & Vibration, Vol.56, No.2, pp. 127-145, 2022, DOI:10.32604/sv.2022.014910

Abstract

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In… More >

S. Narayan^1,*, M. U. Kaisan², Shitu Abubakar², Faisal O. Mahroogi³, Vipul Gupta⁴

Sound & Vibration, Vol.55, No.3, pp. 221-234, 2021, DOI:10.32604/sv.2021.016539

Abstract The health monitoring has been studied to ensure integrity of design of engine structure by detection, quantification, and prediction of damages. Early detection of faults may allow the downtime of maintenance to be rescheduled, thus preventing sudden shutdown of machines. In cylinder pressure developed, vibrations and noise emissions data provide a rich source of information about condition of engines. Monitoring of vibrations and noise emissions are novel non-intrusive methodologies for which positioning of various transducers are important issue. The presented work shows applicability of these diagnosis methodologies adopted in case of diesel engines. The effects of changing various fuel injection… More >

Dileep Kumar Soother^1,*, Imtiaz Hussain Kalwar², Tanweer Hussain¹, Bhawani Shankar Chowdhry¹, Sanaullah Mehran Ujjan¹, Tayab Din Memon³

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 393-408, 2021, DOI:10.32604/cmc.2021.014941

Abstract Reliability of rotating machines is highly dependent on the smooth rolling of bearings. Thus, it is very essential for reliable operation of rotating machines to monitor the working condition of bearings using suitable fault diagnosis and condition monitoring approach. In the recent past, Deep Learning (DL) has become applicable in condition monitoring of rotating machines owing to its performance. This paper proposes a novel bearing fault diagnosis method based on the processing and analysis of the vibration images. The proposed method is the UNET model that is a recent development in DL models. The model is applied to the 2D… More >