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
Advanced Clinical Decision Support Systems significantly influence patient care, with medicine prescriptions being a vital area of research. Ontology, a growing discipline in the semantic web, enables hierarchical domain representation, thereby allowing finer data access to be achieved. Deep Learning (DL) supports pattern recognition in Electronic Health Records (EHR), which include patient demographics and diagnosis histories. Prescribing medications with minimal adverse effects is crucial, particularly for patients who require multiple drugs, as drug interactions can result in more complex conditions. This study introduces an integrated approach that combines Ontology with DL neural networks to improve prescription accuracy. This study proposes NexusOpti, a model featuring an Enhanced Gated Recurrent Unit (E-GRU) layer. To understand drug–disease interactions, hierarchical data were extracted from the International Classification of Diseases (ICD) and Anatomical Therapeutic Chemical (ATC) ontologies. These structured data were processed using a self-attention mechanism to enhance the recommendation precision. This integration not only addresses data security concerns but also improves the accuracy of the medicine recommendations. The model was evaluated using key metrics such as the hit ratio and normalised discounted cumulative gain (NDCG). The NexusOpti model, incorporating the Enhanced Gated Recurrent Unit (E-GRU) layer, outperforms the existing GRU model in terms of NDCG and Hit Ratio metrics. 13% of improvement in performance was oberved to the comparison between NexusOpti with the E-GRU and the GRAM baseline model. These findings highlight the effectiveness of the model in advancing personalised, safer, and data-driven medication prescriptions.
Harichandra Khalingarajah, A. Vasudevan, P. Abinaya et al.· Scientific Reports· 0 citations
The reliable estimation of remaining useful life (RUL) of rolling bearings plays a critical role in maintaining the reliability of modern industrial equipment and minimizing machine downtime. However, the conventional vibration-based prognostic methods tend to experience challenges in predicting the remaining useful life of rolling bearings in variable-speed operating environments due to issues with nonstationary signals and the lack of incorporation of physical degradation processes. This paper proposes a physics-informed approach for estimating the remaining useful life of rolling bearings using vibration envelope characteristics and accelerated life testing. The approach starts with the use of order tracking combined with envelope analysis to extract vibration envelope characteristics under variable speed conditions. A health index is constructed to represent the degradation process. The nonlinear degradation process is modelled using a physics-informed exponential degradation model. An ensemble prediction model is proposed for predicting RUL. The results demonstrate that the developed model was significantly more accurate in its predictions, with a maximum of 49% improvement in the RMSE compared to traditional models and consistent results under varied operational conditions. The use of physics-based modelling and envelope analysis increased the clarity and robustness of the model, and the acceleration of the life testing process contributed to better generalizability of the model. Moreover, the introduction of adaptive threshold values improved maintenance time prediction by over 50%, and uncertainty assessment confirmed the validity of the model.
Sulieman Ibrahim Mohammad, A. Vasudevan, Seif Al Bustanji et al.· Sound & Vibration· 0 citations
A bibliometric study of gamification in learning undertook to map research trends, leading authors and institutions, and emerging themes with a specific focus on their relevance to Quality Education (SDG 4) affirms the close alignment between emerging research themes on gamification and the principles of Quality Education (SDG 4).
V. Muriira, A. Vasudevan, J. Gikonyo et al.· International Journal of Lea...· 0 citations
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