scholarly journals Research on fault diagnosis of planetary gearbox based on variable multi-scale morphological filtering and improved symbol dynamic entropy

2022 ◽  
Author(s):  
Tongtong Liu ◽  
Lingli Cui ◽  
Jianyu Zhang ◽  
Chao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gearbox ◽  
Multi Scale ◽  
Morphological Filtering

scholarly journals Study on Fault Diagnosis Method of Planetary Gearbox Based on Turn Domain Resampling and Variable Multi-Scale Morphological Filtering

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 52
Author(s):  
Tongtong Liu ◽  
Lingli Cui ◽  
Chao Zhang
Keyword(s):  
Frequency Conversion ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Operating Efficiency ◽  
Planetary Gearbox ◽  
Noise Interference ◽  
Multi Scale ◽  
Morphological Filtering ◽  
Diagnosis Method ◽  
Simulation Signal

The turn domain resampling (TDR) method is proposed in the paper on the basis of the existing angle domain resampling for solving the problem of non-fixed fault frequency under variable working conditions. TDR can select the appropriate sampling order according to the influence of frequency conversion, which avoided the error caused by the spline interpolation method. It can provide accurate parameters for the subsequent calculation of the equivalent frequency order. Variable multi-scale morphological filtering (VMSMF) method is proposed for the purpose of further reducing the interference of noise in resampling signal to feature extraction. VMSMF adaptively selects structural elements according to the parameter change of impact signal to make its scale more targeted. It only needs to calculate once using the optimal structural unit for a particular impact, and the filtering accuracy and operating efficiency have been greatly improved. The main steps of this article are as follows. First, the TDR is used to resample the original signal as to get the resampling signal which is still submerged by the strong noise. In the second step, VMSMF is used to filter the resampling signal to obtain the signal with less noise interference. Finally, the fault characteristics of the filtering signal was extracted and compared with the possible fault frequency calculated by the sampling parameters provided by resampling, so as to determine the fault type of the planetary gearbox. By analyzing the simulation signal and the experimental signal respectively, this method can find out the corresponding fault characteristics effectively.

scholarly journals Research on fault diagnosis of planetary gearbox based on Variable Multi-Scale Morphological Filtering and improved Symbol Dynamic Entropy

2021 ◽  
Author(s):  
Tongtong Liu ◽  
Lingli Cui ◽  
Jianyu Zhang ◽  
Chao Zhang
Keyword(s):  
Working Conditions ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
High Signal ◽  
Structural Element ◽  
Planetary Gearbox ◽  
Noise Interference ◽  
Multi Scale ◽  
Time Domain Signal ◽  
Morphological Filtering

Abstract Under complex working conditions with noise interference, the fault feature of planetary gearbox is difficult to be extracted and the fault mode is difficult to be identified. To tackle this problem, the technologies of Variable Multi-scale Morphological Filtering (VMSMF) and Average Multi-scale Double Symbolic Dynamic Entropy (AMDSDE) are proposed in this paper. VMSMF selects Chebyshev Window as the structural element and automatically selects the optimal scale parameters according to the signal characteristics of the planetary gearbox, which improves the filtering accuracy and calculation efficiency. AMDSDE fully considers the correlation between various state modes. Once combined with relevant knowledge of Mathematical statistics, the algorithm can effectively reduce misjudgment. Firstly, the Turn Domain Resampling (TDR) is used to transform the time domain signal of variable speed into the angle domain signal that is not affected by speed change. Secondly, the proposed VMSMF is used to de-noise the vibration signal, and the fault signal with a high signal-to-noise ratio is obtained. Finally, AMDSDE is used to extract the entropy value of the fault signal and judge the fault type. The proposed technology is verified by four kinds of signals collected from the sun gear of the planetary gearbox under non-stationary working conditions.

scholarly journals Intelligent Fault Diagnosis Method of Wind Turbines Planetary Gearboxes Based on a Multi-Scale Dense Fusion Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Xinghua Huang ◽  
Yuanyuan Li ◽  
Yi Chai
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbines ◽  
Feature Fusion ◽  
Positive Role ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Multi Scale ◽  
Average Accuracy ◽  
Fault Features ◽  
Ablation Study

Due to the powerful capability of feature extraction, convolutional neural network (CNN) is increasingly applied to the fault diagnosis of key components of rotating machineries. Due to the shortcomings of traditional CNN-based fault diagnosis methods, the continuous convolution and pooling operations result in the constant decrease of feature resolution, which may cause the loss of some subtle fault information in the samples. This paper proposes a CNN-based model with improved structure multi-scale dense fusion network (MSDFN) to realize the fault diagnosis of wind turbines planetary gearboxes under complicated working conditions. First, the continuous wavelet transform is applied to preprocess the vibration signals, and the two-dimensional wavelet time-frequency diagrams are used as the network input. Then, the multi-scale feature fusion (MSFF) module and a feature of maximum (FoM) module are used in the extraction and classification stages of fault features, respectively. Next, the multi-scale features of each network layer are fused to enhance the fault features. Finally, the high fault diagnosis accuracy is achieved by extracting the separable fusion result of fault features. The proposed method achieves more than 99% fault diagnosis average accuracy on a planetary gearbox dataset. The comparative experimental results verify the effectiveness of the proposed method and its superiority to some mainstream approaches. The ablation study further confirms that MSFF module and FoM module play the positive role in fault diagnosis.

scholarly journals Multi-Scale Demodulation for Fault Diagnosis Based on a Weighted-EMD De-Noising Technique and Time–Frequency Envelope Analysis

2020 ◽  
Vol 10 (21) ◽  
pp. 7796 ◽  
Author(s):  
Wei-tao Du ◽  
Qiang Zeng ◽  
Yi-min Shao ◽  
Li-ming Wang ◽  
Xiao-xi Ding
Keyword(s):  
Fault Diagnosis ◽  
Signal To Noise Ratio ◽  
Fast Fourier Transformation ◽  
Intrinsic Mode Functions ◽  
Envelope Analysis ◽  
Planetary Gearbox ◽  
Time Frequency ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Envelope Signal

Demodulation is one of the most useful techniques for the fault diagnosis of rotating machinery. The commonly used demodulation methods try to select one sensitive sub-band signal that contains the most fault-related components for further analysis. However, a large number of the fault-related components that exist in other sub-bands are ignored in the commonly used envelope demodulation methods. Based on a weighted-empirical mode decomposition (EMD) de-noising technique and time–frequency (TF) impulse envelope analysis, a multi-scale demodulation method is proposed for fault diagnosis. In the proposed method, EMD is first employed to divide the signal into some IMFs (intrinsic mode functions). Then, a new weighted-EMD de-noising technique is presented, and different weights are assigned to IMFs for construction according to their fault-related degrees; thus, the fault-unrelated components are suppressed to improve the signal-to-noise ratio (SNR). After that, continuous wavelet transformation (CWT) is adopted to obtain the time–frequency representation (TFR) of the de-noised signal. Subsequently, the fault-related components in the entire frequency range scale are calculated together, referring to the TF impulse envelope signal. Finally, a fault diagnosis result can be obtained after the fast Fourier transformation of the TF impulse envelope signal. The proposed method and three commonly used methods are applied to the fault diagnosis of a planetary gearbox with a sun gear spalling fault and a fixed shaft gearbox with a crack fault. The results show that the proposed method can effectively detect gear faults and yields better performance than other methods.

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

scholarly journals Composite multi-scale basic scale entropy based on CEEMDAN and its application in hydraulic pump fault diagnosis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Xiaolin Liu ◽  
Xiaoqiang Yang ◽  
Faming Shao ◽  
Wuqiang Liu ◽  
Fuming Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Hydraulic Pump ◽  
Multi Scale ◽  
Basic Scale
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