Abstract: Artificial neural network has achieved the state-of-art performance in fault detection on the Tennessee Eastman process, but it often requires ...
Jan 18, 2021 · Section IV discusses the details of deep learning architecture and deep compression results for fault detection of TN Eastman chemical processes ...
This work extensively studied 7 different combinations of compression techniques and concludes that applying all three techniques, which reduces the model ...
In order to implement online real-time fault detection, three deep compression techniques (pruning, clustering, and quantization) are applied to reduce the ...
We have extensively studied 7 different combinations of compression techniques, all methods achieve high model compression rates over 64 detection accuracy. The ...
Jul 12, 2018 · In this paper, a fault diagnosis method based on a DCNN model consisting of convolutional layers, pooling layers, dropout, fully connected layers is proposed ...
This paper proposes a deep learning neural network structure, called Deep Autoencoder (DAE) algorithm, to detect faults without tedious feature selection. The.
Missing: Compression | Show results with:Compression
Artificial neural network has achieved the state-of-art performance in fault detection on the Tennessee Eastman process, but it often requires enormous memory ...
This research offers a probabilistic neural network (PNN) based on feature selection and a bio-heuristic optimizer as a fault diagnostic approach for chemical ...
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When applied to the benchmark Tennessee Eastman Process, this method revealed clear sets of decision rules which corresponded to the root cause of some faults.
Missing: Compression | Show results with:Compression