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). However, the thermal images require significant storage space, a high transfer rate and high-speed hardware. Thermal imaging is a promising technique in the field of machine condition monitoring via the variation detection of heat distribution. ![]() 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. ![]() 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.
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