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- W4286631385 abstract "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." @default.
- W4286631385 created "2022-07-23" @default.
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- W4286631385 date "2022-08-24" @default.
- W4286631385 modified "2023-09-26" @default.
- W4286631385 title "Separation of aliasing signals from inductive oil debris monitors based on fully convolutional neural networks" @default.
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- W4286631385 doi "https://doi.org/10.1088/1361-6501/ac7f1c" @default.
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