Results 261 to 270 of about 31,713 (311)
Some of the next articles are maybe not open access.
Zero-shot learning for compound fault diagnosis of bearings
Expert Systems With Applications, 2022Abstract Due to the concurrency and coupling of various types of faults, and the number of possible fault modes grows exponentially, thereby compound fault diagnosis is a difficult problem in bearing fault diagnosis. The existing deep learning models can extract fault features when there are a large number of labeled compound fault samples.
Juan Xu, Long Zhou, Yuqi Fan
exaly +2 more sources
A zero-shot fault semantics learning model for compound fault diagnosis
Expert Systems With Applications, 2023Chenchu Xu, Xu Ding
exaly +2 more sources
WavCapsNet: An Interpretable Intelligent Compound Fault Diagnosis Method by Backward Tracking
IEEE Transactions on Instrumentation and Measurement, 2023Weihua Li, Hao Lan, Junbin Chen
exaly +2 more sources
Zero-shot learning compound fault diagnosis of bearings
2021 International Joint Conference on Neural Networks (IJCNN), 2021The compound fault signal of bearings is coupled and complex, thereby compound fault diagnosis is a difficult problem in bearing fault diagnosis. The existing deep learning models can extract fault features when there are a large number of labeled compound fault samples.
Juan Xu 0002 +4 more
openaire +1 more source
Compound-Fault Diagnosis of Rotating Machinery: A Fused Imbalance Learning Method
IEEE Transactions on Control Systems Technology, 2021Rotating machinery plays an important role in large-scale equipment. The fault diagnosis of rotating machinery is of great significance and can increase industrial safety. Up until now, most existing fault diagnosis techniques have been proposed under the condition that only a single fault will occur at the same time.
Jingfei Zhang +4 more
openaire +2 more sources
Multiple Enhanced Sparse Decomposition for Gearbox Compound Fault Diagnosis
IEEE Transactions on Instrumentation and Measurement, 2020The vibration monitoring of gearboxes is an effective means of ensuring the long-term safe operation of rotating machinery. A gearbox may have more than one fault in actual applications. Therefore, gearbox compound fault diagnosis should be investigated.
Ning Li 0020 +4 more
openaire +1 more source
A Novel Compound Neural Network for Fault Diagnosis
2006 2nd IEEE/ASME International Conference on Mechatronics and Embedded Systems and Applications, 2006Independent Component Analysis (ICA) is a powerful tool for redundancy reduction and nongaussian data analysis. And, Artificial Neural Network (ANN), especially the Self-Organizing Map (SOM) based on unsupervised learning is a kind of excellent method for pattern clustering and recognition. By combining ICA with ANN, we proposed a novel compound neural
Jiao Weidong, Yang Shixi, Yan Gongbiao
openaire +1 more source
Compound fault diagnosis based on probability box theory
2017 9th International Conference on Modelling, Identification and Control (ICMIC), 2017In view of compound fault of rolling bearing in mechanical transmission system, the fault signal was decomposed by probability box modeling method to obtain the different probability box models. The original data with abundant statistical information was taken as the research object.
Tang Hong +3 more
openaire +1 more source
The application of compound networks in fault diagnosis of power transformer
2008 China International Conference on Electricity Distribution, 2008Using the concepts of typical gas's concentration and cumulative frequency in analysis of the reliability data for dealing with the pretreatment of data of DGA, two new normalized methods which named characteristic normalization and mix normalization are presented in this paper. The Fisher rule to evaluate the results of the two pretreatment methods is
Wei-zheng Zhang +4 more
openaire +1 more source