SCAE—Stacked Convolutional Autoencoder for Fault Diagnosis of a Hydraulic Piston Pump with Limited Data Samples

Deep learning (DL) models require enormous amounts of data to produce reliable diagnosis results. The superiority of DL models over traditional machine learning (ML) methods in terms of feature extraction, feature dimension reduction, and diagnosis performance has been shown in various studies of fa...

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Vydané v:Sensors (Basel, Switzerland) Ročník 24; číslo 14; s. 4661
Hlavní autori: Eraliev, Oybek, Lee, Kwang-Hee, Lee, Chul-Hee
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 18.07.2024
Predmet:
Artificial intelligence
classification
Datasets
Fault diagnosis
Fourier transforms
hydraulic piston pump
Hydraulics
limited data samples
Machinery
Methods
Neural networks
Sensors
stacked convolutional autoencoder
Wavelet transforms
hydraulic piston pump
fault diagnosis
limited data samples
classification
stacked convolutional autoencoder
ISSN:1424-8220, 1424-8220
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Shrnutí:Deep learning (DL) models require enormous amounts of data to produce reliable diagnosis results. The superiority of DL models over traditional machine learning (ML) methods in terms of feature extraction, feature dimension reduction, and diagnosis performance has been shown in various studies of fault diagnosis systems. However, data acquisition can sometimes be compromised by sensor issues, resulting in limited data samples. In this study, we propose a novel DL model based on a stacked convolutional autoencoder (SCAE) to address the challenge of limited data. The innovation of the SCAE model lies in its ability to enhance gradient information flow and extract richer hierarchical features, leading to superior diagnostic performance even with limited and noisy data samples. This article describes the development of a fault diagnosis method for a hydraulic piston pump using time–frequency visual pattern recognition. The proposed SCAE model has been evaluated on limited data samples of a hydraulic piston pump. The findings of the experiment demonstrate that the suggested approach can achieve excellent diagnostic performance with over 99.5% accuracy. Additionally, the SCAE model has outperformed traditional DL models such as deep neural networks (DNN), standard stacked sparse autoencoders (SSAE), and convolutional neural networks (CNN) in terms of diagnosis performance. Furthermore, the proposed model demonstrates robust performance under noisy data conditions, further highlighting its effectiveness and reliability.
Bibliografia:ObjectType-Article-1
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24144661