In recent decades, there has been a constant demand for faster, longer, and safer railway networks. This also brings challenges for condition monitoring systems in modern railway vehicles. More specifically, critical parts of railway vehicles like wheels degrade over time due to various operational and environmental reasons. Different dynamic effects such as skidding/sliding over the track and the presence of contamination between wheel-rail cause various wheel defects. Faulty wheels ultimately lead to the derailment of railway vehicles. To avoid worst situations like railway derailments, various research has been conducted for developing efficient condition monitoring systems for railway wheels. In addition, there have been some commercial condition monitoring products that can be deployed with railway vehicles. These systems incorporate various sensors such as strain gauges and vision sensors to collect data for diagnosis and prognosis. Various methods have been explored but yet there is a broad research gap in terms of developing advanced onboard condition monitoring systems. With the progress in technology, advanced systems with Machine Learning/Deep Learning methods can provide more efficient and robust condition monitoring of dynamic railway systems. Considering the need for advancement in condition monitoring systems for railway vehicles, a comprehensive review of existing condition monitoring systems for railway wheels is conducted in this paper. The review is aimed at understanding the feasibility and potential of new methods for modern railways. This paper provides a detailed overview of studies on the existing wayside systems and reports their advantages and disadvantages concerning its recently emerging counterpart on-board monitoring systems. Data acquisition systems and analysis methods are critically reviewed which could assist in developing more efficient and reliable condition monitoring systems for railway wheels. This article also reviews the current progress of wayside systems and their limitations. The article is targeted at the researchers and engineers working in this domain, who can pave the way for developing advanced and cost-effective condition monitoring systems for railway wheels using modern technologies.

Plastic bottles are generally recycled by remolding them into numerous products. In this study, waste from plastic bottles was used to fabricate recycled polyethylene terephthalate (r-PET) nanofibers via the electrospinning technique, and high-performance conductive polyethylene terephthalate nanofibers (r-PET nanofibers) were prepared followed by copper deposition using the electroless deposition (ELD) method. Firstly, the electrospun r-PET nanofibers were chemically modified with silane molecules and polymerized with 2-(methacryloyloxy) ethyl trimethylammonium chloride (METAC) solution. Finally, the copper deposition was achieved on the surface of chemically modified r-PET nanofibers by simple chemical/ion attraction. The water contact angle of r-PET nanofibers, chemically modified r-PET nanofibers, and copper deposited nanofibers were 140°, 80°, and 138°, respectively. The r-PET nanofibers retained their fibrous morphology after copper deposition, and EDX results confirmed the presence of copper on the surface of r-PET nanofibers. XPS was performed to analyze chemical changes before and after copper deposition on r-PET nanofibers. The successful deposition of copper one r-PET nanofibers showed an excellent electrical resistance of 0.1 ohms/cm and good mechanical strength according to ASTM D-638.

The solar photovoltaic (PV) power forecast is crucial for steady grid operation, scheduling, and grid electricity management. In this work, numerous time series forecast methodologies, including the statistical and artificial intelligence-based methods, are studied and compared fastidiously to forecast PV electricity. Moreover, the impact of different environmental conditions for all of the algorithms is investigated. Hourly solar PV power forecasting is done to confirm the effectiveness of various models. Data used in this paper is of one entire year and is acquired from a 100 MW solar power plant, namely, Quaid-e-Azam Solar Park, Bahawalpur, Pakistan. This paper suggests recurrent neural networks (RNNs) as the best-performing forecasting model for PV power output. Furthermore, the bidirectional long-short-term memory RNN framework delivered high accuracy results in all weather conditions, especially under cloudy weather conditions where root mean square error (RMSE) was found lowest 0.0025, square stands at 0.99, and coefficient of variation of root mean square error (RMSE) Cv was observed 0.0095%.

In this article, the assembly of axi-symmetric rigid structures from imprecisely manufactured components is considered to produce a ‘straight-build’ assembly. The eccentricity of the assembly is improved by selecting the best relative orientation of each component and using four different strategies to control the assembly errors. The presentation is simplified by considering the assembly of two-dimensional structures, and the Monte Carlo simulation technique is used to quantify the propagation of random component variations on the resulting assembly. The axial and radial run-outs for each component are used to represent component variability and these quantities are represented as Gaussian random variables. The influence of component variations on the assembly is predicted using connective assembly models. Numerical results are presented for assemblies consisting of: (a) identical rectangular components; and (b) non-identical rectangular components. The results are compared with each other and those obtained using ‘direct-build’ assembly, which does not control the assembly errors. It is found that assembly variations can be reduced significantly using the proposed techniques.

The challenging task of insulator monitoring through aerial images is addressed in high voltage transmission network and highlights the limitations of traditional human patrolling with emphasize on utilization of unmanned aerial vehicles UAVs utilizing machine learning algorithms. This research has been accomplished by creating indigenous dataset of 500kV transmission network of National Transmission and Despatch Center Limited (NTDCL). 608 original images were captured in diverse lighting and topographical conditions which were then augmented to 3618 images. To improve the detection accuracy of YOLOv8s algorithm in aerial images, HSV and CLAHE image enhancement techniques were employed to improve the visual feature of insulator with suppressed noise. YOLOv8s algorithm with image enhancement has improved detection accuracy from 88% to 95% demonstrating the effectiveness of integrating image enhancement technique for insulator monitoring, offering promising improvement in maintenance practices and operational reliability of transmission lines.