Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A fault diagnosis framework for rotating machinery of marine equipment: A semi-supervised learning framework based on contractive stacked autoencoder
https://orcid.org/0000-0002-1614-0302
Rotating machinery is one of the key components of marine equipment. Due to the complex and harsh offshore environment, the health status of rotating machinery is more likely to be affected. Therefore, fault diagnosis is of great significance to normal operation and maintenance of rotating machinery in marine equipment. Traditional data-driven fault diagnosis tasks require massive label data for training, and it takes time and manpower to obtain enough label samples. At the same time, it is considered that the noise can interfere with the performance of the fault diagnosis framework. To overcome the above two defects, this paper proposes a fault diagnosis framework based on semi-supervised learning, where the contractive stacked autoencoder (CSA) and the classifier multilayer perceptron (MLP) extract features from unlabeled data and realize fault classification respectively. Compared with the Stacked Autoencoder (SAE)-MLP and Stacked Denoising Autoencoder (SDAE)-MLP frameworks, the proposed learning framework has better fault diagnosis accuracy and robustness.
A fault diagnosis framework for rotating machinery of marine equipment: A semi-supervised learning framework based on contractive stacked autoencoder
https://orcid.org/0000-0002-1614-0302
Rotating machinery is one of the key components of marine equipment. Due to the complex and harsh offshore environment, the health status of rotating machinery is more likely to be affected. Therefore, fault diagnosis is of great significance to normal operation and maintenance of rotating machinery in marine equipment. Traditional data-driven fault diagnosis tasks require massive label data for training, and it takes time and manpower to obtain enough label samples. At the same time, it is considered that the noise can interfere with the performance of the fault diagnosis framework. To overcome the above two defects, this paper proposes a fault diagnosis framework based on semi-supervised learning, where the contractive stacked autoencoder (CSA) and the classifier multilayer perceptron (MLP) extract features from unlabeled data and realize fault classification respectively. Compared with the Stacked Autoencoder (SAE)-MLP and Stacked Denoising Autoencoder (SDAE)-MLP frameworks, the proposed learning framework has better fault diagnosis accuracy and robustness.
A fault diagnosis framework for rotating machinery of marine equipment: A semi-supervised learning framework based on contractive stacked autoencoder
https://orcid.org/0000-0002-1614-0302
Rotating machinery is one of the key components of marine equipment. Due to the complex and harsh offshore environment, the health status of rotating machinery is more likely to be affected. Therefore, fault diagnosis is of great significance to normal operation and maintenance of rotating machinery in marine equipment. Traditional data-driven fault diagnosis tasks require massive label data for training, and it takes time and manpower to obtain enough label samples. At the same time, it is considered that the noise can interfere with the performance of the fault diagnosis framework. To overcome the above two defects, this paper proposes a fault diagnosis framework based on semi-supervised learning, where the contractive stacked autoencoder (CSA) and the classifier multilayer perceptron (MLP) extract features from unlabeled data and realize fault classification respectively. Compared with the Stacked Autoencoder (SAE)-MLP and Stacked Denoising Autoencoder (SDAE)-MLP frameworks, the proposed learning framework has better fault diagnosis accuracy and robustness.
A fault diagnosis framework for rotating machinery of marine equipment: A semi-supervised learning framework based on contractive stacked autoencoder
Pan, Penghao (Autor:in) / Zhao, Dong (Autor:in) / Li, Yueyang (Autor:in)
01.08.2023
12 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Data Mining for Fault Diagnosis and Machine Learning for Rotating Machinery
| British Library Online Contents | 2005
Multiscale singular value manifold for rotating machinery fault diagnosis
| British Library Online Contents | 2017
| DOAJ | 2023
ROTATING MACHINERY FAULT DIAGNOSIS BASED ON TWO-DIMENSIONAL CONVOLUTION NEURAL NETWORK
| DOAJ | 2020
A rule-based classifier ensemble for fault diagnosis of rotating machinery
| British Library Online Contents | 2018