Identification and Quantitative Analysis of the Induced Voltage Signature in a High-Gradient Magnetostatic Debris Detection Sensor

Oil debris monitoring is an important and effective method for evaluating the wear conditions of mechanical equipment, therefore providing early fault warnings. In particular, inductive sensors sense the size and distribution of wear debris in oil lubrication by extracting features from induced voltages. However, in practice, induced voltages generated by wear debris are inevitably contaminated by random noise and interference, so denoising operation is usually required. In this article, the sensing model of high-gradient magnetostatic (HGM) field-based debris detection sensors was re-investigated. A new mathematical model was established, based on which a correlation-metric-based adaptive target signal localization strategy and a new indicator quantitatively analyzing the size of debris were proposed. Additionally, according to the established mathematical model, the numerical characteristics of target signal were extracted to achieve the identification, counting, and quantitative analysis of wear debris. The oil experiments validated the derived signal model and demonstrated the effectiveness and robustness of the proposed algorithm in feature extraction, identification, and quantitative characteristic analysis.