Ramanujan Fourier mode decomposition and its application in gear fault diagnosis

2021 ◽  
pp. 1-1
Author(s):  
Jian Cheng ◽  
Yu Yang ◽  
Zhantao Wu ◽  
Haidong Shao ◽  
Haiyang Pan ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Fourier Mode ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis

scholarly journals Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Jiakai Ding ◽  
Dongming Xiao ◽  
Liangpei Huang ◽  
Xuejun Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Hopfield Neural Network ◽  
Sample Entropy ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

The gear fault signal has some defects such as nonstationary nonlinearity. In order to increase the operating life of the gear, the gear operation is monitored. A gear fault diagnosis method based on variational mode decomposition (VMD) sample entropy and discrete Hopfield neural network (DHNN) is proposed. Firstly, the optimal VMD decomposition number is selected by the instantaneous frequency mean value. Then, the sample entropy value of each intrinsic mode function (IMF) is extracted to form the gear feature vectors. The gear feature vectors are coded and used as the memory prototype and memory starting point of DHNN, respectively. Finally, the coding vector is input into DHNN to realize fault pattern recognition. The newly defined coding rules have a significant impact on the accuracy of gear fault diagnosis. Driven by self-associative memory, the coding of gear fault is accurately classified by DHNN. The superiority of the VMD-DHNN method in gear fault diagnosis is verified by comparing with an advanced signal processing algorithm. The results show that the accuracy based on VMD sample entropy and DHNN is 91.67% of the gear fault diagnosis method. The experimental results show that the VMD method is better than the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical mode decomposition (EMD), and the effect of it in the diagnosis of gear fault diagnosis is emphasized.

Gear Fault Diagnosis Method Using EEMD Sample Entropy and Grey Incidence

2013 ◽  
Vol 694-697 ◽  
pp. 1151-1154
Author(s):  
Wen Bin Zhang ◽  
Ya Song Pu ◽  
Jia Xing Zhu ◽  
Yan Ping Su
Keyword(s):  
Fault Diagnosis ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Morphological Filter ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

In this paper, a novel fault diagnosis method for gear was approached based on morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey incidence. Firstly, in order to eliminate the influence of noises, the line structure element was selected for morphological filter to denoise the original signal. Secondly, denoised vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some containing the most dominant fault information were calculated the sample entropy. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

Gear Fault Diagnosis Method Based on the Integration of Empirical Mode Decomposition and Cepstrum Analysis

2013 ◽  
Vol 310 ◽  
pp. 328-333 ◽  
Author(s):  
Bing Luo ◽  
Wen Tong Yang ◽  
Zhi Feng Liu ◽  
Yong Sheng Zhao ◽  
Li Gang Cai
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Mechanical Transmission ◽  
Intrinsic Mode Functions ◽  
Cepstrum Analysis ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

Gear is the most common mechanical transmission equipment. Therefore, gear fault diagnosis is of much significance. In this article, a gear fault diagnosis method based on the integration of empirical mode decomposition and cepstrum is proposed by introducing empirical mode decomposition and cepstrum into gear fault analysis. Firstly EMD is used to decompose the gear vibration signal finite number of intrinsic mode functions and a residual error item. To do gear fault diagnosis, cepstrum analysis is carried upon those intrinsic mode functions to extract feature information from the vibration signal. The results of the study on simulated and experimental signals show that this method is better than the cepstrum method and it can precisely locate the site of gear failure.

Symplectic quaternion singular mode decomposition with application in gear fault diagnosis

2021 ◽  
Vol 160 ◽  
pp. 104266
Author(s):  
Yanli Ma ◽  
Junsheng Cheng ◽  
Niaoqing Hu ◽  
Zhe Cheng ◽  
Yu Yang
Keyword(s):  
Fault Diagnosis ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis

Gear Fault Diagnosis Using Synchro-Squeezing Transform Based Feature Analysis

2013 ◽  
Vol 569-570 ◽  
pp. 449-456 ◽  
Author(s):  
Budhaditya Hazra ◽  
Sriram Narasimhan
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Mathematical Structure ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
The Rich ◽  
The Individual ◽  
Non Stationary Signal

Synchro-squeezing transform has recently emerged as a powerful signal processing tool in non-stationary signal processing. Premised upon the concept of time-frequency (TF) reassignment, its basic objective is to provide a sharper representation of signals in the TF plane and extract the individual components of a non-stationary multi-component signal, akin to empirical mode decomposition (EMD). The rich mathematical structure based on continuous wavelet transform (CWT) makes synchro-squeezing powerful for gear fault diagnosis, as faulty gear signal is frequently constituted out of multiple amplitude-modulated and frequency-modulated signals embedded in noise. This work utilizes the decomposing power of synchro-squeezing transform to extract the IMFs from a gear signal followed by the application of standard gearbox condition indicators which promises greater prognostic power than that can be achieved by applying condition indictors directly to the inherently complex gear signals. The efficacy and the robustness of the algorithm are demonstrated with the aid of practical experimental data obtained from a helicopter gear box.

Envelope demodulation based on variational mode decomposition for gear fault diagnosis

2016 ◽  
Vol 231 (4) ◽  
pp. 864-870 ◽  
Author(s):  
Xueli An ◽  
Hongtao Zeng ◽  
Chaoshun Li
Keyword(s):  
Fault Diagnosis ◽  
Amplitude Modulation ◽  
Decomposition Method ◽  
Modulation Frequency ◽  
Vibration Signal ◽  
Variational Mode Decomposition ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
Component Amplitude

A new time–frequency analysis method, based on variational mode decomposition, was investigated. When a gear fault occurs, its vibration signal is nonstationary, nonlinear, and exhibits complex modulation performance. According to the modulation characteristics of the gear vibration signal arising from faults therein, a gear fault diagnosis method based on variational mode decomposition and envelope analysis was proposed. The variational mode decomposition method can decompose a complex signal into several stable components. The obtained components were analyzed by envelope demodulation. According to the envelope spectrum, gear faults can be diagnosed. In essence, the variational mode decomposition method can decompose a multi-component signal into a number of single component amplitude modulation–frequency modulation signals. The method is suited to the handling of multi-component amplitude modulation–frequency modulation signals. The simulated signal and the actual gear fault vibration signals were analyzed. The results showed that the method can be effectively applied to gear fault diagnosis.

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