Abstract
Gearboxes, as essential connecting and transmission components in mechanical equipment, have been widely used in modern industrial development. Gearboxes are prone to malfunction or even failure due to complex structures and harsh working environments. This article takes online monitoring of gear wear and damage as the research object and studies the fault diagnosis method of gear multi-source heterogeneous parameters for oil monitoring and vibration monitoring. The Yolov5 model is used to identify multi-objective wear particles. The experimental outcomes suggest that the optimized detection method can sensitively reflect the evolution process of gear wear.
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