Enhancement of oil particle sensor capability via resonance-based signal decomposition and fractional calculus

Oil particle sensors can provide valuable information about the conditions of mechanical devices by detecting the metallic particles falling from machine components into lubricating oil due to fatigue, corrosion and pitting. However, the vibration interferences and background noise in the signals from a particle sensor make it difficult to precisely detect the particle quantity and size, and thus lead to misleading detection results. To enhance the capabilities of oil particle sensors, resonance-based signal decomposition is employed in this study to separate the output signal of the oil particle sensor into two components, i.e., the high-resonance and the low-resonance components. Since the high-and the low-resonance components reflect the harmonic signal components and the impulse signal components contained in the original signal respectively, only the low-resonance component contains the particle signal contents. Then the fractional calculus technique is applied to extract the oil particle signature from the low-resonance component. In this way, the interferences can be eliminated and the particle signature can be reliably extracted. The performance of the proposed approach has been validated by both simulated and experimental data. (C) 2015 Elsevier Ltd. All rights reserved.