An Improved High-Throughput Imaging Sensor for Macroscale and Microscale Features of Wear Particles in Online Condition Monitoring

The online oil monitoring sensor is widely used in evaluating the health stage of various equipment through analyzing the macroscale (concentration) and microscale feature (size and shape) of wear debris. However, existing sensors face limitations in acquiring those multiple features effectively with high throughput (100 mL/min). In light of this challenge, an imaging sensor is developed to capture both the macroscale and microscale features, which employs a magnetic field to manipulate particle sampling within a fixed period. In the sampling processes, the wear particles were absorbed and dispersed in turn, which was recorded by two complementary metal-oxide-semiconductor (CMOS) cameras to capture images of the macro and micro features, respectively. The optimal parameters of sensor were calibrated by various oil throughput and magnetic currents (100 mL/min and 0.9 A), which showed wide and accurate capability for measurement of particle shapes and size distribution from 10 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$150~\mu $ </tex-math></inline-formula>m. Finally, this sensor was tested for a gearbox wear experiment with 4000 min at a high throughput of 100 mL/min. It effectively identified changes across three different health stages, which showed a promising online condition monitoring application with multifeatures of wear debris at high throughput.