Adaptive power spectrum Fourier decomposition method with application in fault diagnosis for rolling bearing

Measurement ◽  
2021 ◽  
pp. 109837
Author(s):  
Jinde Zheng ◽  
Siqi Huang ◽  
Haiyang Pan ◽  
Jinyu Tong ◽  
Chengjun Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Decomposition Method ◽  
Rolling Bearing ◽  
Fourier Decomposition ◽  
Adaptive Power

Resonance-based bandwidth Fourier decomposition method for gearbox fault diagnosis

2020 ◽  
Vol 32 (3) ◽  
pp. 035003
Author(s):  
Minqiang Deng ◽  
Aidong Deng ◽  
Jing Zhu ◽  
Yaowei Shi ◽  
Yang Liu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Decomposition Method ◽  
Fourier Decomposition

scholarly journals A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing

2018 ◽  
Vol 37 (4) ◽  
pp. 928-954 ◽  
Author(s):  
Jun Ma ◽  
Jiande Wu ◽  
Xiaodong Wang
Keyword(s):  
Fault Diagnosis ◽  
Decomposition Method ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Difference Spectrum ◽  
Singular Value ◽  
Local Mean Decomposition ◽  
Diagnosis Method ◽  
Local Mean ◽  
Teager Energy

Rolling bearing is one of the most crucial components in rotating machinery and due to their critical role, it is of great importance to monitor their operation conditions. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. Therefore, signal denoising preprocessing has become an essential part of condition monitoring and fault diagnosis. In the present study, a hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing is proposed. First, as a denoising preprocessing method, singular value difference spectrum denoising is applied to reduce the noise of the bearing vibration signal and improve the signal-to-noise ratio. Then, local mean decomposition method is used to decompose the denoised signals into several product functions. And product functions corresponding to the fault feature are selected according to the correlation coefficient criterion. Finally, Teager energy spectrum is analyzed by applying the Teager energy operator to the constructed amplitude modulation component. The proposed method is successfully applied to analyze the vibration signals collected from an experimental motive rolling bearing and rolling bearing of the self-made rotor experimental platform. The experimental results demonstrate that the proposed singular value difference spectrum denoising and local mean decomposition method can achieve fairly or slightly better performance than the normal local mean decomposition-Teager energy operator method, fast kurtogram, and the wavelet denoising and local mean decomposition method.

scholarly journals Weak Signal Detection Method Based on the Coupled Lorenz System and Its Application in Rolling Bearing Fault Diagnosis

2020 ◽  
Vol 10 (12) ◽  
pp. 4086
Author(s):  
Guozheng Li ◽  
Nanlin Tan ◽  
Xiang Li
Keyword(s):  
Fault Diagnosis ◽  
Power Spectrum ◽  
Lorenz System ◽  
Signal To Noise Ratio ◽  
Rolling Bearing ◽  
Synchronization Error ◽  
New Method ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Rolling bearings are widely used in rotating machinery. Their fault feature signals are often submerged in strong noise and are difficult to identify. This paper presents a new method of bearing fault diagnosis that combines the coupled Lorenz system and power spectrum technology. The process is achieved in the following three steps. First, a synchronization system based on the Lorenz system is constructed using the driving-response method. Second, when the tested signal is connected to the driving end, the synchronization error between the two sub-chaotic systems is obtained. Finally, the power spectrum density of the synchronization error is calculated and compared with the corresponding fault characteristic frequency. The coupled Lorenz system makes full use of the noise immunity and nonlinear amplification of the chaotic system. The detection characteristics and feasibility of the new method are verified by simulation and actual measured vibration data. The result shows that the noise reduction effect of the coupled Lorenz system is obvious. This method can improve the signal-to-noise ratio of the tested signal and provide a new way to perform fault diagnosis of rolling bearings.

Sign in / Sign up

Export Citation Format

Share Document