Separation of aliasing signals from inductive oil debris monitors based on fully convolutional neural networks

Abstract

Inductive oil debris monitors can detect wear debris in lubricating oil in real-time, which has great potential for monitoring the working conditions of mechanical systems. However, the superimposition of the induced voltages when multiple debris particles pass through a sensor at a close distance may lead to an erroneous estimation of the peak-to-peak value of the wear debris waveforms. A complete implementation framework is proposed to separate the aliasing signals based on fully convolutional neural networks, which includes a segmented fractional calculus filtering technique and a semi-simulated training dataset generation method. The results of physical experiments indicate that the proposed method can reduce the average error rate of the peak-to-peak value from 15.36% to 3.96% and the maximum error rate from 56.33% to 9.27% compared with those before separation. The stability and computing time of this method are also evaluated through physical experiments.