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

Le résumé fourni par la source

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.

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