scholarly journals Fault Diagnosis of Planet Gear Using Continuous Vibration Separation and Minimum Entropy Deconvolution

2020 ◽  
Vol 10 (22) ◽  
pp. 8062
Author(s):  
Jian Shen ◽  
Lun Zhang ◽  
Niaoqing Hu
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Dynamic Responses ◽  
Minimum Entropy ◽  
Modulation Effect ◽  
Dynamic Component ◽  
Planetary Gearbox ◽  
Gear Fault ◽  
Vibration Responses ◽  
Planet Gear

Planet gear is the most unique dynamic component in planetary gearbox. It rotates around sun gear while rotating around its own central axis, causing modulation effect in monitoring signal. Planetary gear is usually connected to heavy external loads and other transmissions, fault feature of planet gear may be overwhelmed by noises and other signals. Focused on planet gear inside planetary gearbox, a method for fault diagnosis is proposed in this paper based on continuous vibration separation (CVS) and minimum entropy deconvolution (MED). In this method, CVS is designed to separate dynamic responses of planet gear from overall vibration responses of planetary gearbox by overcoming the modulation effect and depressing noises. MED is used for enhancement detection of fault-related impulses. Simulations and experiments are conducted to collect signals for analysis. The proposed method is also compared with vibration separation method (VS). Both simulation and experiment analysis indicate that the proposed planet gear fault diagnosis method is effective. Comparative study indicates that CVS-MED method improves VS by keeping signal periodicity while overcoming modulation effect and depressing noises.

scholarly journals Planetary Gearbox Fault Diagnosis Using Envelope Manifold Demodulation

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Weigang Wen ◽  
Robert X. Gao ◽  
Weidong Cheng
Keyword(s):  
Fault Diagnosis ◽  
Frequency Band ◽  
Manifold Learning ◽  
Planetary Gear ◽  
Vibration Signal ◽  
Planetary Gearbox ◽  
Characteristic Extraction ◽  
Gear Fault ◽  
Planet Gear ◽  
Fault Characteristic

The important issue in planetary gear fault diagnosis is to extract the dependable fault characteristics from the noisy vibration signal of planetary gearbox. To address this critical problem, an envelope manifold demodulation method is proposed for planetary gear fault detection in the paper. This method combines complex wavelet, manifold learning, and frequency spectrogram to implement planetary gear fault characteristic extraction. The vibration signal of planetary gear is demodulated by wavelet enveloping. The envelope energy is adopted as an indicator to select meshing frequency band. Manifold learning is utilized to reduce the effect of noise within meshing frequency band. The fault characteristic frequency of the planetary gear is shown by spectrogram. The planetary gearbox model and test rig are established and experiments with planet gear faults are conducted for verification. All results of experiment analysis demonstrate its effectiveness and reliability.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

scholarly journals Planetary Gearbox Dynamic Modeling Considering Bearing Clearance and Sun Gear Tooth Crack

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2638
Author(s):  
Xianhua Chen ◽  
Xingkai Yang ◽  
Ming J. Zuo ◽  
Zhigang Tian
Keyword(s):  
Dynamic Model ◽  
Dynamic Modeling ◽  
Failure Modes ◽  
Gear Tooth ◽  
Working Environment ◽  
The Sun ◽  
Planetary Gearbox ◽  
Bearing Clearance ◽  
Vibration Responses ◽  
Planet Gear

Planetary gearbox systems are critical mechanical components in heavy machinery such as wind turbines. They may suffer from various failure modes, due to the harsh working environment. Dynamic modeling is a useful method to support early fault detection for enhancing reliability and reducing maintenance costs. However, reported studies have not considered the sun gear tooth crack and bearing clearance simultaneously to analyze their combined effect on vibration characteristics of planetary gearboxes. In this paper, a dynamic model is developed for planetary gearboxes considering the clearance of planet gear, sun gear, and carrier bearings, as well as sun gear tooth crack levels. Bearing forces are calculated considering bearing clearance, and the dynamic model equations are updated accordingly. The results reveal that the combination of bearing clearances can affect the vibration response with sun gear tooth crack by increasing the kurtosis. It is found that the effect of planet gear bearing clearance is very small, while the sun gear and carrier bearing clearance has clear impact on the vibration responses. These findings suggest that the incorporation of bearing clearance is important for planetary gearbox dynamic modeling.

scholarly journals A Novel Deep Feature Learning Method Based on the Fused-Stacked AEs for Planetary Gear Fault Diagnosis

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4522
Author(s):  
Xihui Chen ◽  
Aimin Ji ◽  
Gang Cheng
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Feature Learning ◽  
Planetary Gear ◽  
Learning Ability ◽  
Energy Industry ◽  
Deep Feature ◽  
Gear Fault ◽  
Deep Feature Learning ◽  
Electromechanical Equipment

Planetary gear is the key component of the transmission system of electromechanical equipment for energy industry, and it is easy to damage, which affects the reliability and operation efficiency of electromechanical equipment of energy industry. Therefore, it is of great significance to extract the useful fault features and diagnose faults based on raw vibration signals. In this paper, a novel deep feature learning method based on the fused-stacked autoencoders (AEs) for planetary gear fault diagnosis was proposed. First, to improve the data learning ability and the robustness of feature extraction process of AE model, the sparse autoencoder (SAE) and the contractive autoencoder (CAE) were studied, respectively. Then, the quantum ant colony algorithm (QACA) was used to optimize the specific location and key parameters of SAEs and CAEs in deep learning architecture, and multiple SAEs and multiple CAEs were stacked alternately to form a novel deep learning architecture, which gave the deep learning architecture better data learning ability and robustness of feature extraction. The experimental results show that the proposed method can address the raw vibration signals of planetary gear. Compared with other deep learning architectures and shallow learning architecture, the proposed method has better diagnosis performance, and it is an effective method of deep feature learning and fault diagnosis.

scholarly journals Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Xinghui Zhang ◽  
Jianshe Kang ◽  
Lei Xiao ◽  
Jianmin Zhao
Keyword(s):  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Stable Distribution ◽  
Planetary Gearbox ◽  
Alpha Stable Distribution ◽  
Gear Fault

Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE) of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.

scholarly journals Fault Diagnosis for a Multistage Planetary Gear Set Using Model-Based Simulation and Experimental Investigation

2016 ◽  
Vol 2016 ◽  
pp. 1-19 ◽  
Author(s):  
Guoyan Li ◽  
Fangyi Li ◽  
Yifan Wang ◽  
Dehao Dong
Keyword(s):  
Fault Diagnosis ◽  
Spectral Characteristics ◽  
Planetary Gear ◽  
Spectral Structure ◽  
Mesh Stiffness ◽  
Planetary Gearbox ◽  
Frequency Components ◽  
Close Frequency ◽  
Simulated Analysis ◽  
Potential Energy Method

The gear damage will induce modulation effects in vibration signals. A thorough analysis of modulation sidebands spectral structure is necessary for fault diagnosis of planetary gear set. However, the spectral characteristics are complicated in practice, especially for a multistage planetary gear set which contains close frequency components. In this study, a coupled lateral and torsional dynamic model is established to predict the modulation sidebands of a two-stage compound planetary gear set. An improved potential energy method is used to calculate the time-varying mesh stiffness of each gear pair, and the influence of crack propagation on the mesh stiffness is analyzed. The simulated signals of the gear set are obtained by using Runge-Kutta numerical analysis method. Meanwhile, the sidebands characteristics are summarized to exhibit the modulation effects caused by sun gear damage. At the end, the experimental signals collected from an industrial SD16 planetary gearbox are analyzed to verify the theoretical derivations. The results of experiment agree well with the simulated analysis.

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