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- W3083789190 abstract "Real-time acquisition of large amounts of machine operating data is now increasingly common due to recent advances in Industry 4.0 technologies. A key benefit to factory operators of this large scale data acquisition is in the ability to perform real-time condition monitoring and early-stage fault detection and diagnosis on industrial machinery—with the potential to reduce machine down-time and thus operating costs. The main contribution of this work is the development of an intelligent fault diagnosis method capable of operating on these real-time data streams to provide early detection of developing problems under variable operating conditions. We propose a novel dual-path recurrent neural network with a wide first kernel and deep convolutional neural network pathway (RNN-WDCNN) capable of operating on raw temporal signals such as vibration data to diagnose rolling element bearing faults in data acquired from electromechanical drive systems. RNN-WDCNN combines elements of recurrent neural networks (RNNs) and convolutional neural networks (CNNs) to capture distant dependencies in time series data and suppress high-frequency noise in the input signals. Experimental results on the benchmark Case Western Reserve University (CWRU) bearing fault dataset show RNN-WDCNN outperforms current state-of-the-art methods in both domain adaptation and noise rejection tasks." @default.
- W3083789190 created "2020-09-14" @default.
- W3083789190 creator A5023238883 @default.
- W3083789190 creator A5078743663 @default.
- W3083789190 date "2020-09-08" @default.
- W3083789190 modified "2023-09-29" @default.
- W3083789190 title "A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults" @default.
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- W3083789190 doi "https://doi.org/10.3390/s20185112" @default.
- W3083789190 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7570750" @default.
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