Rotating machinery fault diagnosis based on impact feature extraction deep neural network

A Hu, J Sun, L Xiang, Y Xu - Measurement Science and …, 2022 - iopscience.iop.org
Gears and bearings are important components in rotating machinery and are crucial for the
safety and operation of the whole mechanical system. Intelligent fault diagnosis methods …

Enhanced K-nearest neighbor for intelligent fault diagnosis of rotating machinery

J Lu, W Qian, S Li, R Cui - Applied Sciences, 2021 - mdpi.com
Case-based intelligent fault diagnosis methods of rotating machinery can deal with new
faults effectively by adding them into the case library. However, case-based methods …

An improved fault diagnosis method of rotating machinery using sensitive features and RLS-BP neural network

Q Lu, R Yang, M Zhong, Y Wang - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
An improved algorithm with feature selection and neural network classification is proposed
in this paper to investigate the fault diagnosis problem of rotating machinery. The feature …

A novel fault diagnosis method of rotating machinery via VMD, CWT and improved CNN

J Gu, Y Peng, H Lu, X Chang, G Chen - Measurement, 2022 - Elsevier
The rolling bearings play a vital role in mechanical production and transportation. However,
when it appears abnormal, the fault characteristics are weak and different to be extracted in …

Intelligent fault diagnosis of rolling bearings using variational mode decomposition and self-organizing feature map

J Zhang, J Wu, B Hu, J Tang - Journal of Vibration and …, 2020 - journals.sagepub.com
Rotating machinery contains numerous rolling bearings, which are critical for ensuring the
normal working position and accurate operation of individual shaft systems. However …

A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and Twin SVM

Z Liu, W Guo, J Hu, W Ma - ISA transactions, 2017 - Elsevier
This paper proposes a hybrid intelligent method for multi-fault detection of rotating
machinery, in which three methods, ie including the redundant second generation wavelet …

A fault diagnosis approach for rolling element bearings based on RSGWPT-LCD bilayer screening and extreme learning machine

Q Tong, J Cao, B Han, X Zhang, Z Nie, J Wang… - IEEE …, 2017 - ieeexplore.ieee.org
The faults of rolling element bearings can result in the deterioration of machine operating
conditions; how to assess the working condition and identify the fault of the rolling element …

A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine

K Li, M Xiong, F Li, L Su, J Wu - Neurocomputing, 2019 - Elsevier
This study presents a new roller bearing fault diagnosis algorithm based on a sparsity and
neighborhood preserving deep extreme learning machine (SNP-DELM) and motor current …

Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings

X Chen, J Zhou, J Xiao, X Zhang, H Xiao, W Zhu… - Applied Mathematics …, 2014 - Elsevier
Rolling element bearings (REB) are crucial mechanical parts of most rotary machineries,
and REB failures often cause terrible accidents and serious economic losses. Therefore …

An integrated deep learning method towards fault diagnosis of hydraulic axial piston pump

S Tang, S Yuan, Y Zhu, G Li - Sensors, 2020 - mdpi.com
A hydraulic axial piston pump is the essential component of a hydraulic transmission system
and plays a key role in modern industry. Considering varying working conditions and the …