Journal Article10.1016/J.YMSSP.2015.08.030
Fault diagnosis in spur gears based on genetic algorithm and random forest
Mariela Cerrada,Mariela Cerrada,Grover Zurita,Diego Cabrera,René-Vinicio Sánchez,Mariano Artés,Chuan Li,Chuan Li +7 more
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TL;DR: The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time–frequency domains, which are extracted from vibration signals.
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About: This article is published in Mechanical Systems and Signal Processing. The article was published on 01 Mar 2016. The article focuses on the topics: Fault detection and isolation & Random forest.
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Citations
Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data
TL;DR: A new intelligent method named deep convolutional transfer learning network (DCTLN) is proposed, which facilitates the 1-D CNN to learn domain-invariant features by maximizing domain recognition errors and minimizing the probability distribution distance.
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TL;DR: The results confirm that the proposed method can get rid of the dependence on manual feature extraction and overcome the limitations of individual deep learning models, which is more effective than the existing intelligent diagnosis methods.
440
Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification
Chen Lu,Zhenya Wang,Bo Zhou +2 more
TL;DR: A novel deep architecture based bearing diagnosis method is proposed using cognitive computing theory, which introduces the advantages of image recognition and visual perception to bearing fault diagnosis by simulating the cognition process of the cerebral cortex.
414
Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning
TL;DR: A nonlinear projection is applied to achieve the compressed acquisition, which not only reduces the amount of measured data that contained all the information of faults but also realizes the automatic feature extraction in transform domain.
393
Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study
TL;DR: Zhao et al. as mentioned in this paper constructed a taxonomy and performed a comprehensive review of unsupervised deep transfer learning (UDTL)-based intelligent fault diagnosis (IFD) according to different tasks.
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