Sohar University

About: Sohar University is a education organization based out in Sohar, Oman. It is known for research contribution in the topics: Photovoltaic system & Fiber laser. The organization has 279 authors who have published 730 publications receiving 8658 citations.

Photovoltaic/Thermal (PV/T) systems: Status and future prospects

Abstract: In the last four decades, greater attention has been paid to PV/T systems due to their advantages compared with PV or solar thermal systems alone. This paper aims to study various aspects of PV/T systems through the existing literature in order to highlight key points as future work in this field as well as illustrate different techniques used for such systems. In addition, PV/T systems are reviewed in terms of thermal and electrical side views. Furthermore, the analysis of solar thermal systems, various system applications such as air, water, air/water, phase change material PCM and Nanofluid systems are summarized. In light of most attempts to improve the PV/T system, more focus has been paid to the thermal rather than the electrical side. Furthermore, comparisons between PV/T systems in terms of performance parameters and efficiencies are presented. A critical review of many findings of previously conducted research is also discussed. It is found that the PV/T air heater system is promising for future preheating air applications. Moreover, it is suggested that the use of nanoparticles and water as base fluid improves the overall system efficiency. Furthermore, the PV side views require more attention in technical and cost terms. However, more research is essential to reduce the cost and, improve the effectiveness and technical design of such systems.

Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN)

Abstract: An efficient predictive plan is needed for any industry because it can optimize the resources management and improve the economy plant, by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in the productive processes are caused for gear box, they began its deterioration from early stages, also called incipient level. The extracted features from the DWT are used as inputs in a neural network for classification purposes. The results show that the developed method can reliably diagnose different conditions of the gear box. The wavelet transform is used to represent all possible types of transients in vibration signals generated by faults in a gear box. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal of a spur bevel gear box in different conditions is used to demonstrate the application of various wavelets in feature extraction. In this paper fault diagnostics of spur bevel gear box is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, and classification. This paper investigates the use of discrete wavelets for feature extraction and artificial neural network for classification.