Results 201 to 210 of about 3,123 (260)
Fault Diagnosis Method for Reciprocating Compressors Based on Spatio-Temporal Feature Fusion. [PDF]
Sensors (Basel)Xu H +6 more
europepmc +1 more source
Comparative analysis of deep learning algorithms for rolling element bearing fault classification under variable loads and speeds. [PDF]
Sci RepVishal P +4 more
europepmc +1 more source
A machine learning-based classification method for SynRM faults. [PDF]
Sci RepRajini V +6 more
europepmc +1 more source
Gear fault diagnosis of wind turbine drivetrains using multi-source information fusion and ensemble learning in a simulation bench study. [PDF]
Sci RepKang X, Shao L, Zhao B.
europepmc +1 more source
Infrared Imaging for Autonomous Power Inspection: A Review from Detector to System Integration. [PDF]
Sensors (Basel)Guo Y, Du Y, Mao R, Zhao Y, Guo J.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
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
Compound-Fault Diagnosis of Rotating Machinery: A Fused Imbalance Learning Method
IEEE Transactions on Control Systems Technology, 2021 Rotating 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