2023
DOI: 10.1109/access.2023.3244795
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Fault Diagnosis of Rotating Machinery Using Denoising-Integrated Sparse Autoencoder Based Health State Classification

Abstract: The diagnostic study on single-fault with distinguishing features based on monitoring data analysis is mature and fruitful in recent years. However, the early fault signals collected by practical monitoring systems often possess the following characteristics: 1) Fairly weak signal strength; 2) Submerged in powerful background noise; 3) Coupling of different fault data. These features not only increase the diagnostic difficulty, but also make the existing methods hardly to get the desired results. Consequently,… Show more

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Cited by 5 publications
(2 citation statements)
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“…where ρ is named sparse parameter with values approaching 0. Models incorporating the sparseness condition are called the SAE models, which have already been popularly promoted in multiple sectors [26,27]. Finally, the training of the SAE model is transformed to solve the optimization problem of formula (6),…”
Section: Decodermentioning
confidence: 99%
“…where ρ is named sparse parameter with values approaching 0. Models incorporating the sparseness condition are called the SAE models, which have already been popularly promoted in multiple sectors [26,27]. Finally, the training of the SAE model is transformed to solve the optimization problem of formula (6),…”
Section: Decodermentioning
confidence: 99%
“…The Backpropagation algorithm is employed to minimize the loss function and uncover more profound and representative features. Fault classification is ultimately conducted through the Softmax classification layer, which is connected at the topmost layer of the neural network [30][31][32][33]. Fig.…”
Section: Stacked Sparse Autoencodermentioning
confidence: 99%