Abstract
Online monitoring of multiple lubricant properties is critical in maintaining and extending the health of high-speed rotating and reciprocating machinery used in many of the nation’s key industries including aerospace, manufacturing, and energy. There have been many efforts on the development of sensors focused on measuring specific chemical/physical properties of lubricant oil. One long-standing challenge for these property sensors is the overlapping output problem (cross-sensitivity), meaning they cannot provide accurate measurements. Here we demonstrated a capacitive oil property sensor array based on a new general regression neural network (GRNN) for measuring acid, base, and water content in lubricant oil. Results showed that the GRNN can pinpoint individual oil properties from the overlapped sensor array’s responses with high accuracy and speed.
Published Version
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