Gearbox failures represent a critical problem faced by industrial machines, considering the impacts of such failures on machine reliability, efficiency, and maintenance cost. Despite the well-established use of vibration-based condition monitoring techniques for diagnosing the presence of faults, few attempts have been made in transforming information about fault progression into thresholds that could be used in making condition-based maintenance (CBM) decisions. In this work, a CBM system for analysing gearbox fault progression based on vibration envelope features is presented, alongside accelerated life testing. An experimental approach has been adopted, whereby accelerated life testing was performed to induce gearbox degradation progressively. Then, vibration data were analysed through the envelope technique, out of which six vibration envelope features, namely RMS Envelope, Kurtosis, Crest Factor, Peak Amplitude, Envelope Energy, and Sideband Energy Ratio, were derived and analysed based on their sensitivity through correlation, monotonicity, trendability, and separability tests. Thereafter, a composite health index based on the most sensitive features was formulated, and a multilevel maintenance threshold system consisting of Alert, Warning, and Critical levels was created. The findings show that Envelope Energy, Sideband Energy Ratio, and Kurtosis have the highest sensitivity to degradation and can accurately represent the evolution of gearbox faults. The composite health index shows a strong correlation with the extent of degradation (R2 = 0.962) and successfully discriminates between different health states of the gearbox. The developed framework for determining maintenance thresholds achieves an accuracy of 94.9%, which allows accurate identification of maintenance intervention phases.
A. Vasudevan, S. Mohammad, M. Hunitie et al.· Sound & Vibration· 0 citations
The Gulf Cooperation Council (GCC) is witnessing a rapid growth in artificial intelligence (AI)-enabled data center development, thanks to national digital transformation strategies and the region’s strategic location bridging global markets. Yet, no evaluation framework holistically compares the direct impacts of data center operations on the environment with the dispersed sustainability impacts of AI applications across major business sectors. To address this, this study proposes and applies a Multi-Criteria Decision Analysis (MCDA) framework to assess carbon emissions, water use, energy consumption, social impacts and governance structures across three scenarios (business-as-usual (BAU), moderate transition (MOD), and aggressive decarbonization (OPT). Using secondary structured data from peer-reviewed sources, institutional databases (IEA, UNFCCC, Uptime Institute) and independently verified case studies, the analysis applies clearly defined boundary rules and attribution principles. The findings suggest that under the BAU high-growth scenario, modelled emissions increase substantially by 2035, while AI-enabled sustainability applications in buildings, industry, and utilities provide only partial offsets under optimistic adoption assumptions. The NIS indicates that net-positive outcomes are achievable only when substantial renewable-energy procurement, best-practice cooling, and transparent verification of AI benefits occur together. The results extend socio-technical systems theory and ecological modernization theory by addressing the boundary problem in sustainability assessment of digital infrastructure, providing a scalable approach for policymakers and investors in high-carbon, water-scarce regions. The Net Impact Score (NIS) is not to be construed as an absolute causal prediction, but rather as a scenario-conditional decision-support indicator because AI-benefit attribution and scaling are both dependent on the mentioned assumptions and data limits.
Abdelrehim Awad, Bshair Alharthi, Hiyam Abdulrahim et al.· Frontiers in Environmental S...· 0 citations
