An Inductive Sensor Based Multi-Least-Mean-Square Adaptive Weighting Filtering for Debris Feature Extraction

Monitoring the wear debris in lubricant systems is an effective method of reflecting the health of mechanical equipment. With the advantages of being simply structured, noninvasive, and insensitive to oil quality, inductive sensors are often deployed for debris detection. However, induced voltages generated by wear debris are usually contaminated by noise and other undesired components, thereby limiting the reliability and availability of the sensors. In this article, a new debris-detection framework is proposed based on an inductive sensor with parallel dual coils. With the aid of a reference signal, a multi-least-mean-square adaptive weighting filtering method was developed, and good noise suppression was achieved with little violation of the debris signatures. The algorithm is illustrated through numerical simulations, and the effectiveness of the proposed framework was verified by an oil experiment. Two traditional denoising algorithms were also analyzed for comparison. The experimental results demonstrated that the proposed strategy had an excellent capability for extracting and identifying debris features.