Artificial intelligence has become a core enabling factor for raising the standard of sustainable project management, and it can effectively strengthen organizational capacity, innovation levels, and the quality of environmental decision-making. Against this backdrop, this study is an original empirical study focused on organizations implementing green projects in Malaysia. Its core research objective is to explore the impact of AI-driven green project management on sustainable project performance. The study incorporates the following variables: antecedent variable MADANI sustainability orientation, core independent variable AI-driven green project management, mediating variable green innovation capability, moderating variables organizational green agility and green tax incentives, and dependent variable sustainable project performance. This study adopts a quantitative cross-sectional research design, and collected valid samples from 341 practitioners. It uses partial least squares structural equation modeling (PLS-SEM) to analyze the core conceptual model. The results show that the path coefficients and p-values between all core variables reach the threshold for statistical significance. The model’s explained variance for green innovation capability is 41.7%, and its explained variance for sustainable project performance is 49.2%. At the academic level, this study fills the literature gap in the research field of AI-enabled sustainable project management; at the practical level, it provides clear actionable guidance for local Malaysian organizations and policymakers to advance digital transformation and sustainable development initiatives.
S. Mohammad, Attallah Hassan Mohamed Al-Taani, A. Vasudevan et al.· Journal of Project Managemen...· 0 citations
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
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