Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study
O AlShorman, F Alkahatni, M Masadeh… - Advances in …, 2021 - journals.sagepub.com
Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating
machinery (RM) has a vital role in the modern industrial world. However, the remaining …
machinery (RM) has a vital role in the modern industrial world. However, the remaining …
Deep learning-based intelligent fault diagnosis methods toward rotating machinery
S Tang, S Yuan, Y Zhu - Ieee Access, 2019 - ieeexplore.ieee.org
Fault diagnosis of rotating machinery plays a significant role in the industrial production and
engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as …
engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as …
Convolutional neural network based bearing fault diagnosis of rotating machine using thermal images
The bearings are the crucial components of rotating machines in an industrial firm.
Unplanned failure of these components not only increases the downtime, but also leads to …
Unplanned failure of these components not only increases the downtime, but also leads to …
Fast nonlinear blind deconvolution for rotating machinery fault diagnosis
Z Zhang, J Wang, S Li, B Han, X Jiang - Mechanical Systems and Signal …, 2023 - Elsevier
Sparse optimization based early fault diagnosis method is drawing more and more attention.
In these methods, the objective function is usually a sparsity measure which can represent …
In these methods, the objective function is usually a sparsity measure which can represent …
Machine learning approach using MLP and SVM algorithms for the fault prediction of a centrifugal pump in the oil and gas industry
PF Orrù, A Zoccheddu, L Sassu, C Mattia, R Cozza… - Sustainability, 2020 - mdpi.com
The demand for cost-effective, reliable and safe machinery operation requires accurate fault
detection and classification to achieve an efficient maintenance strategy and increase …
detection and classification to achieve an efficient maintenance strategy and increase …
Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery
Despite the recent advances on intelligent data-driven machinery fault diagnostics, large
amounts of high-quality supervised data are mostly required for model training. However, it …
amounts of high-quality supervised data are mostly required for model training. However, it …
Data mining in predictive maintenance systems: A taxonomy and systematic review
Predictive maintenance is a field of study whose main objective is to optimize the timing and
type of maintenance to perform on various industrial systems. This aim involves maximizing …
type of maintenance to perform on various industrial systems. This aim involves maximizing …
Deep hypergraph autoencoder embedding: An efficient intelligent approach for rotating machinery fault diagnosis
Intelligent fault diagnosis based on deep learning (DL) has been widely used in various
engineering practices. However, when confronting massive unlabeled industrial data …
engineering practices. However, when confronting massive unlabeled industrial data …
Deep Laplacian Auto-encoder and its application into imbalanced fault diagnosis of rotating machinery
X Zhao, M Jia, M Lin - Measurement, 2020 - Elsevier
Generally, the measured health condition data from mechanical system often exhibits
imbalanced distribution in real-world cases. To enhance fault diagnostic accuracy of the …
imbalanced distribution in real-world cases. To enhance fault diagnostic accuracy of the …
Hybrid distance-guided adversarial network for intelligent fault diagnosis under different working conditions
Deep learning, especially transfer learning, has made a great deal of extraordinary
achievements in intelligent fault diagnosis. In practical situations, data shift problem is …
achievements in intelligent fault diagnosis. In practical situations, data shift problem is …