A gear box is widely employed in automobiles and industrial machines for transmitting power and torque. It operates under various working conditions for prolonged hours increasing the chance of gearbox failure. Major faults in gear systems are caused due to wear, scoring, pitting, tooth fracture, etc. Gear box failure leads to increases in machine downtime and maintenance costs. The nature and location of such failures can be identified with precision using condition monitoring techniques. In this study, machine condition data are acquired from the gear box using a vibration accelerometer, microphone and acoustic emission sensors under different operating conditions, such as three loading conditions (0 N, 5 N, 10 N) and three rpm variations (500, 750, 1000). The wavelet features are extracted from the acquired vibration, sound and acoustic emission signal, and prominent features are identified. To automate the process of fault diagnosis, machine learning algorithms (artificial neural network, support vector machine, and proximal support vector machine) are utilized. Dual and multi-sensor fusion is implemented with the help of prominent features, to intensify the classification accuracy. The performance of the individual signals, and dual and multi-sensor fused models in gearbox fault diagnosis are compared and discussed in detail.

This review offers a comprehensive overview of synthetic fuels as promising alternatives to conventional fossil fuels. The carbon-neutral potential of synthetic fuels, when produced using renewable energy and captured CO₂, offering significant opportunities to mitigate CO₂ emissions is discussed. Moreover, the efficiency of synthetic fuels is presented as they do not require dedicated agricultural land or substantial water resources, addressing concerns related to land use change and water scarcity associated with traditional biofuels. The economic viability of synthetic fuels is explored highlighting the advancements in technology and decreased renewable energy costs, coupled with their independence from food crops, mitigating concerns about potential impacts on food prices. Major investments by industry leaders like Porsche, HIF Global, and ExxonMobil, totalling $1 B, aimed at achieving an annual production of 550 M liters by 2026 are detailly covered. This study further extends emphasizing the scalability of synthetic fuel production through modular processes, enabling tailored facilities to meet regional demands and contribute to a decentralized and resilient energy infrastructure. Additionally, the "drop-in" nature of synthetic fuels, seamlessly compatible with existing fuel storage, pipelines, and pumps, is highlighted, facilitating a smooth transition without requiring extensive infrastructure changes. Challenges such as the current high cost of synthetic fuel production are acknowledged, necessitating supportive government policies and incentives for widespread adoption. Overall, synthetic fuels emerge as promising contenders in the pursuit of sustainable and adaptable energy solutions, with tangible benefits for the environment, economy, and existing energy infrastructure.