An Adaptive Frequency Window Empirical Wavelet Transform Method for Fault Diagnosis of Wheelset Bearing

2018 ◽  
Author(s):  
Feiyue Deng ◽  
Yongqiang Liu
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Transform Method ◽  
Empirical Wavelet Transform ◽  
Frequency Window

An improved empirical wavelet transform method for rolling bearing fault diagnosis

2020 ◽  
Vol 63 (11) ◽  
pp. 2231-2240
Author(s):  
HaiRun Huang ◽  
Ke Li ◽  
WenSheng Su ◽  
JianYi Bai ◽  
ZhiGang Xue ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Transform Method ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Empirical Wavelet Transform

scholarly journals Low-Pass Filtering Empirical Wavelet Transform Machine Learning Based Fault Diagnosis for Combined Fault of Wind Turbines

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 975
Author(s):  
Yancai Xiao ◽  
Jinyu Xue ◽  
Mengdi Li ◽  
Wei Yang
Keyword(s):  
Machine Learning ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Gear Tooth ◽  
Support Vector ◽  
Grey Wolf Optimizer ◽  
Empirical Wavelet Transform ◽  
Low Pass

Fault diagnosis of wind turbines is of great importance to reduce operating and maintenance costs of wind farms. At present, most wind turbine fault diagnosis methods are focused on single faults, and the methods for combined faults usually depend on inefficient manual analysis. Filling the gap, this paper proposes a low-pass filtering empirical wavelet transform (LPFEWT) machine learning based fault diagnosis method for combined fault of wind turbines, which can identify the fault type of wind turbines simply and efficiently without human experience and with low computation costs. In this method, low-pass filtering empirical wavelet transform is proposed to extract fault features from vibration signals, LPFEWT energies are selected to be the inputs of the fault diagnosis model, a grey wolf optimizer hyperparameter tuned support vector machine (SVM) is employed for fault diagnosis. The method is verified on a wind turbine test rig that can simulate shaft misalignment and broken gear tooth faulty conditions. Compared with other models, the proposed model has superiority for this classification problem.

scholarly journals Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

2019 ◽  
Vol 9 (8) ◽  
pp. 1696 ◽  
Author(s):  
Wang ◽  
Lee
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Background Noise ◽  
Vibration Signal ◽  
Spectral Subtraction ◽  
Accurate Identification ◽  
Side Band ◽  
Empirical Wavelet Transform ◽  
Gear Fault ◽  
Fault Characteristic

Fault characteristic extraction is attracting a great deal of attention from researchers for the fault diagnosis of rotating machinery. Generally, when a gearbox is damaged, accurate identification of the side-band features can be used to detect the condition of the machinery equipment to reduce financial losses. However, the side-band feature of damaged gears that are constantly disturbed by strong jamming is embedded in the background noise. In this paper, a hybrid signal-processing method is proposed based on a spectral subtraction (SS) denoising algorithm combined with an empirical wavelet transform (EWT) to extract the side-band feature of gear faults. Firstly, SS is used to estimate the real-time noise information, which is used to enhance the fault signal of the helical gearbox from a vibration signal with strong noise disturbance. The empirical wavelet transform can extract amplitude-modulated/frequency-modulated (AM-FM) components of a signal using different filter bands that are designed in accordance with the signal properties. The fault signal is obtained by building a flexible gear for a helical gearbox with ADAMS software. The experiment shows the feasibility and availability of the multi-body dynamics model. The spectral subtraction-based adaptive empirical wavelet transform (SS-AEWT) method was applied to estimate the gear side-band feature for different tooth breakages and the strong background noise. The verification results show that the proposed method gives a clearer indication of gear fault characteristics with different tooth breakages and the different signal-noise ratio (SNR) than the conventional EMD and LMD methods. Finally, the fault characteristic frequency of a damaged gear suggests that the proposed SS-AEWT method can accurately and reliably diagnose faults of a gearbox.

scholarly journals An Adaptive Spectrum Segmentation Method to Optimize Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 30437-30456 ◽  
Author(s):  
Yonggang Xu ◽  
Kun Zhang ◽  
Chaoyong Ma ◽  
Zhipeng Sheng ◽  
Hongchen Shen
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Segmentation Method ◽  
Rolling Bearings ◽  
Empirical Wavelet Transform

scholarly journals Frequency Phase Space Empirical Wavelet Transform for Rolling Bearings Fault Diagnosis

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 86306-86318 ◽  
Author(s):  
Xin Huang ◽  
Guangrui Wen ◽  
Lin Liang ◽  
Zhifen Zhang ◽  
Yuan Tan
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Phase Space ◽  
Rolling Bearings ◽  
Empirical Wavelet Transform ◽  
Frequency Phase

The Study of the Nonstationary Signal in Fault Diagnosis

2012 ◽  
Vol 182-183 ◽  
pp. 1489-1493
Author(s):  
Zhao Yan Xuan ◽  
Miao Ge
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Space Time ◽  
Signal Spectrum ◽  
Nonstationary Signal ◽  
Index Method ◽  
Transform Method ◽  
Time Index ◽  
Nonstationary Stage

In this paper, the space time-index plots were introduced and used to determine the nonstationarity of the fault diagnosis in order to handle faults in the nonstationary stage. By using the decomposition and reconstruction of wavelet transform for nonstationary signal, the author targeted on the bandwidth and select information with pertinence, and then analysed the reconstruct signal spectrum for extracting the typical character of fault. The results show that the space time-index method is applicable to judge the nonstationary and the wavelet transform method in fault diagnosis is effective for the nonstationary signal.

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