Fault diagnosis of wind turbine bearings based on CNN and SSA–ELM

X Liu, Z Zhang, F Meng, Y Zhang - Journal of Vibration Engineering & …, 2023‏ - Springer
Purpose As a critical component of the wind turbine drive train, the bearings are easy to fail
under the complex environment of variable working conditions and loads in long-term …

A neural network compression method based on knowledge-distillation and parameter quantization for the bearing fault diagnosis

M Ji, G Peng, S Li, F Cheng, Z Chen, Z Li, H Du - Applied Soft Computing, 2022‏ - Elsevier
Condition monitoring and fault diagnosis have been critical for the optimal scheduling of
machines, improving the system reliability and the reducing maintenance cost. In recent …

Rolling bearing fault diagnosis based on multiple wavelet coefficient dimensionality reduction and improved residual network

X Zheng, P Yang, K Yan, Y He, Q Yu, M Li - Engineering Applications of …, 2024‏ - Elsevier
In response to limitations in traditional intelligent fault diagnosis methods, such as accuracy,
robustness, generality, and susceptibility to noise, this article proposes Multiple Wavelet …

Condition monitoring and fault diagnosis of rotating machinery towards intelligent manufacturing: Review and prospect

H Zhang, W Che, Y Cao, Z Guan, C Zhu - Iranian Journal of Science and …, 2024‏ - Springer
Rotating machinery is advancing in the direction of high efficiency, high rotary speed,
enhanced automation, and widespread application with the quickening growth of intelligent …

Spectral proper orthogonal decomposition and machine learning algorithms for bearing fault diagnosis

A Afia, F Gougam, W Touzout, C Rahmoune… - Journal of the Brazilian …, 2023‏ - Springer
Vibration analysis has been extensively exploited for bearing fault diagnosis. However,
signal acquisition is quite expensive since external hardware is required. Moreover, for …

Real-time damage analysis of 2D C/SiC composite based on spectral characters of acoustic emission signals using pattern recognition

X Zeng, H Shao, R Pan, B Wang, Q Deng, C Zhang… - Acta Mechanica …, 2022‏ - Springer
In this study, unsupervised and supervised pattern recognition were implemented in
combination to achieve real-time health monitoring. Unsupervised recognition (k-means++) …

A deep convolutional neural network model with two-stream feature fusion and cross-load adaptive characteristics for fault diagnosis

W Pan, H Qu, Y Sun, M Wang - Measurement Science and …, 2023‏ - iopscience.iop.org
Research aimed at diagnosing rolling bearing faults is of great significance to the health
management of equipment. In order to solve the problem that rolling bearings are faced with …

A multi fault classification in a rotor-bearing system using machine learning approach

PV Shinde, RG Desavale, PM Jadhav… - Journal of the Brazilian …, 2023‏ - Springer
Modern condition monitoring of rotating machinery became intelligent for enhanced
reliability, productivity, and safety. Signal processing has been collaboratively implemented …

Bearing fault diagnosis based on artificial intelligence methods: machine learning and deep learning

A Ghorbel, S Eddai, B Limam, N Feki… - Arabian Journal for …, 2024‏ - Springer
This paper presents a comprehensive study on the application of Artificial Intelligence (AI)
methods, specifically machine learning and deep learning, for the diagnosis of bearing …

A case study of wind turbine rotor over-speed fault diagnosis using combination of SCADA data, vibration analyses and field inspection

M Morshedizadeh, M Rodgers, A Doucette… - Engineering Failure …, 2023‏ - Elsevier
Reduction of wind turbines down times is paramount in wind energy develop-ment and cost
effectiveness. Such reductions require early fault detection and more importantly, a …