Abstract
Condition monitoring of gearboxes is a crucial task because gearboxes are essential power transmission components whose failure can lead to a catastrophic breakdown of machines. The common faults of a gearbox system, such as tooth breakage, wear, scuffing, spalling and lubricant starvation, have a significant influence on the inside friction and heat dissipation, and consequently, it changes the temperature field distribution within the gearbox. Thermal imaging is a promising technique in the field of machine condition monitoring via the variation detection of heat distribution. However, the thermal images require significant storage space, a high transfer rate and high-speed hardware. To achieve intelligent and efficient machine condition monitoring with the advanced thermal imaging technique, this study reduces the dimensionality of thermal images of a two-stage gearbox system via compressive sensing (CS) and classifies three different lubricant shortage conditions based on the compressed features with an intelligent convolutional neural network (CNN). The experimental results demonstrate that the compressed thermal images contain sufficient fault information and are capable of diagnosing the inadequate lubrication faults for gearboxes operating at various working conditions.
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Acknowledgements
The authors sincerely appreciate the support from the China Scholarship Council (CSC) and the Centre for Efficiency and Performance Engineering (CEPE) at the University of Huddersfield.
Funding
This research was funded by the NSFC-RS joint research project under grants 11911530177 in China and IE181496 in UK.
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Tang, X. et al. (2020). Condition Monitoring of Lubricant Shortage for Gearboxes Based on Compressed Thermal Images. In: Ball, A., Gelman, L., Rao, B. (eds) Advances in Asset Management and Condition Monitoring. Smart Innovation, Systems and Technologies, vol 166. Springer, Cham. https://doi.org/10.1007/978-3-030-57745-2_76
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DOI: https://doi.org/10.1007/978-3-030-57745-2_76
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