Jul 2026· Sound & Vibration· 0 citations· 45 references
Abstract
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.
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Rolan Siregar, Didik Sugiyanto, Juan Pratama et al.· EUREKA Physics and Engineeri...· 0 citations
This study proposes a hybrid methodology for early bearing fault diagnosis in rotating
machinery, particularly asynchronous motors. The approach combines three advanced
techniques: ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition
with Adaptive Noise) to extract intrinsic mode functions from vibration s...
Kabla Aida, Asradj Zahir· ITEGAM- Journal of Engineeri...· 0 citations
The growing trend toward industrial automation has heightened the importance of production equipment reliability, especially on manufacturing lines that operate continuously. In this context, electric motors play a fundamental role in driving material handling and transportation systems. Failures in this equipment can...
E. Cisneros, Fábio de Sousa Cardoso, Raimundo Cláudio Souza Gomes et al.· Revista de Estudos Interdisc...· 0 citations
Gear fault diagnosis is a vital part of condition monitoring and predictive maintenance for rotating machinery, particularly in automotive applications where early detection of fault severity can avoid unexpected failures and cut down maintenance costs. However, the accurate classification of progressive fault levels i...
Razikha Amreen M. I., S. Kuzhalvaimozhi· Proceedings of the Instituti...· 0 citations
Bearing faults are one of the most failure mechanisms in induction motors, leading to increased vibration, reduced efficiency, and unexpected downtime. Existing diagnostic approaches are often limited by reliance on single-sensor measurements or computationally intensive learning-based models, which reduces robustness...
Vasileios I. Vlachou, Karolina Kudelina-Zhang, Dimitrios E. Efstathiou et al.· 2026 International Conferenc...· 0 citations
Rolling bearings are among the most critical components in rotating machinery, and their reliable condition assessment is essential for preventing unexpected failures and enabling predictive maintenance strategies. In this study, a lightweight and automated methodology is proposed for bearing Fault Detection (FD) and F...
C. Cosenza, M. Spirto, Francesco Melluso et al.· Journal of Physics, Conferen...· 0 citations
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