scholarly journals Joint Amplitude and Frequency Demodulation Analysis Based on Variational Mode Decomposition for Multifault Diagnosis of a Multistage Reducer

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Feng Li ◽  
Xinyu Pang ◽  
Zhaojian Yang

Multistage reducer vibration signals have complicated spectral structures owing to the amplitude and frequency modulations of gear damage-induced vibrations and the multiplicative amplitude modulation effect caused by time-varying vibration transfer paths (in the case of local gear damage) when the multistage reducer contains both planetary and spur gears. Moreover, the difference between the vibration energies of these gears increases the difficulty of fault feature extraction when multiple failures occur in the reducer. As the meshing frequency of each gear group often varies significantly, variational mode decomposition can be performed to decompose the vibration signal according to frequency, enabling separation of the vibration signals of the spur and planetary gears. The common fault features of these gears can be extracted from the spectrum of the amplitude demodulation envelope. To verify the effectiveness of this method, we first analyzed a simulation signal, and then utilized the experimental signals from a laboratory multistage reducer for verification. In the multistage reducer simulation, we considered the amplitude and frequency modulation of the gear damage and transfer paths. In the experimental verification, we processed local faults (broken teeth) and uniform faults (uniform wear) on the sun gear and the spur gear of the planetary gear separately.

Author(s):  
Gang Ren ◽  
Jide Jia ◽  
Xiangyu Jia ◽  
Jiajia Han

Variational mode decomposition (VMD) is a recently introduced adaptive signal decomposition algorithm with a solid theoretical foundation and good noise robustness compared with empirical mode decomposition (EMD). There is a lot of background noise in the vibration signal of diesel engine. To solve the problem, a denoising algorithm based on VMD and Euclidean Distance is proposed. Firstly, a multi-component, non-Gauss, and noisy simulation signal is established, and decomposed into a given number K of band-limited intrinsic mode functions by VMD. Then the Euclidean distance between the probability density function of each mode and that of the simulation signal are calculated. The signal is reconstructed using the relevant modes, which are selected on the basis of noticeable similarities between the probability density function of the simulation signal and that of each mode. Finally, the vibration signals of diesel engine connecting rod bearing faults are analyzed by the proposed method. The results show that compared with other denoising algorithms, the proposed method has better denoising effect, and the fault characteristics of vibration signals of diesel engine connecting rod bearings can be effectively enhanced.


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Madhurjya Dev Choudhury ◽  
Liu Hong ◽  
Jaspreet Singh Dhupia

Fault detection in gearboxes plays a significant role in ensuring their reliability. Vibration signals collected during gearbox operation contain a wealth of valuable condition information that can be exploited for fault detection. However, in an industrial environment machine operating speed always fluctuates around its nominal value, which causes smearing of the gearbox vibration spectrum. Considering operating speed fluctuation and multi-component nature of measured gearbox vibration signals, an order-tracking method combining the variational mode decomposition (VMD) and the fast dynamic time warping (FDTW) is proposed in this paper. Firstly, the multi-component vibration signal is decomposed into several intrinsic mode functions (IMFs) using VMD in order to extract a signal component with higher signal-to-noise ratio (SNR). Then, the sensitive fault information carrying IMF is exploited to estimate the instantaneous speed profile in order to construct the shaft rotational vibration signal. The measured vibration signal is then resampled based on the optimal warping path obtained by FDTW, which performs an “elastic” stretching and compression along the time axis of the extracted shaft vibration signal with respect to a sinusoidal reference signal of constant shaft rotational frequency. Finally, the gear fault is detected by constructing the order spectrum of the resampled vibration signal. The effectiveness of the proposed algorithm is demonstrated using simulation results.


2019 ◽  
Vol 68 (8) ◽  
pp. 2755-2767 ◽  
Author(s):  
Shiqian Chen ◽  
Yang Yang ◽  
Xingjian Dong ◽  
Guanpei Xing ◽  
Zhike Peng ◽  
...  

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Jianfeng Zhang ◽  
Mingliang Liu ◽  
Keqi Wang ◽  
Laijun Sun

During the operation process of the high voltage circuit breaker, the changes of vibration signals can reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition (EEMD). Firstly, the original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, calculating the envelope of each IMF and separating the envelope by equal-time segment and then forming equal-time segment energy entropy to reflect the change of vibration signal are performed. At last, the energy entropies could serve as input vectors of support vector machine (SVM) to identify the working state and fault pattern of the circuit breaker. Practical examples show that this diagnosis approach can identify effectively fault patterns of HV circuit breaker.


Author(s):  
Xueli An ◽  
Junjie Yang

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.


2011 ◽  
Vol 58-60 ◽  
pp. 636-641
Author(s):  
Yan Chen Shin ◽  
Yi Cheng Huang ◽  
Jen Ai Chao

This paper proposes a diagnosis method of ball screw preload loss through the Hilbert-Huang Transform (HHT) and Multiscale entropy (MSE) process when machine tool is in operation. Maximum dynamic preload of 2% and 4% ball screws are predesigned, manufactured and conducted experimentally. Vibration signal patterns are examined and revealed by Empirical Mode Decomposition (EMD) with Hilbert Spectrum. Different preload features are extracted and discriminated by using HHT. The irregularity development of ball screw with preload loss is determined and abstracting via MSE based on complexity perception. The experiment results successfully show preload loss can be envisaged by the proposed methodology.


2012 ◽  
Vol 19 (4) ◽  
pp. 635-652 ◽  
Author(s):  
Fakher Chaari ◽  
Walter Bartelmus ◽  
Radoslaw Zimroz ◽  
Tahar Fakhfakh ◽  
Mohamed Haddar

Gearboxes usually run under fluctuating load conditions during service, however most of papers available in the literature describe models of gearboxes under stationary load conditions. Main task of published papers is fault modeling for their detection. Considering real situation from industry, the assumption of stationarity of load conditions cannot be longer kept. Vibration signals issued from monitoring in maintenance operations differ from mentioned models (due to load non-stationarity) and may be difficult to analyze which lead to erroneous diagnosis of the system. The objective of this paper is to study the influence of time varying load conditions on a gearbox dynamic behavior. To investigate this, a simple spur gear system without defects is modeled. It is subjected to a time varying load. The speed-torque characteristic of the driving motor is considered. The load variation induces speed variation, which causes a variation in the gearmesh stiffness period. Computer simulation shows deep amplitude modulations with sidebands that don't differ from those obtained when there is a defective tooth. In order to put in evidence the time varying load effects, Short Time Fourier Transform and then Smoothed Wigner-Ville distribution are used. Results show that the last one is well suited for the studied case.The experimental validation presented at the end of the paper confirms the obtained results. Such results offer useful information when diagnosing gear transmissions by avoiding confusing conclusions from vibration signals.


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