Feature Extraction of Lubricating Oil Debris Signal Based on Segmentation Entropy with an Adaptive Threshold

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 5; p. 1380
• Main Authors: Yang, Baojun, Liu, Wei, Lu, Sheng, Luo, Jiufei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 21-02-2024; MDPI
• Subjects: adaptive threshold; Algorithms; Decomposition; Entropy; Ferromagnetism; Fourier transforms; inductive sensors; Lubricating oils; Lubrication and lubricants; Magnetic fields; noise suppression; Parameter estimation; Petroleum mining; Random variables; segmentation entropy; Sensors; Signal processing; noise suppression; segmentation entropy; inductive sensors; adaptive threshold
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24051380

Ferromagnetic debris in lubricating oil, serving as an important communication carrier, can effectively reflect the wear condition of mechanical equipment and predict the remaining useful life. In practice application, the detection signals collected by using inductive sensors contain not only debris signals but also noise terms, and weak debris features are prone to be distorted, which makes it a severe challenge to debris signature identification and quantitative estimation. In this paper, a debris signature extraction method established on segmentation entropy with an adaptive threshold was proposed, based on which five identification indicators were investigated to improve detection accuracy. The results of the simulations and oil experiment show that the proposed algorithm can effectively identify wear particles and preserve debris signatures.