Incipient Fault Diagnosis of the Planetary Gearbox Based on Improved Variational Mode Decomposition and Frequency-Weighted Energy Operator

Author(s):  
Hongkun Li ◽  
Chaoge Wang ◽  
Jiayu Ou

Abstract Planetary gearbox is widely used in large and complex mechanical equipment such as wind power generation, helicopters and petrochemical industry. Gear failures occur frequently in working conditions at low speeds, high service load and harsh operating environments. Incipient fault diagnosis can avoid the occurrence of major accidents and loss of personnel property. Aiming at the problems that the incipient fault of planetary gearbox is difficult to recognize and the number of intrinsic mode functions (IMFs) decomposed by variational mode decomposition (VMD) must be set in advance and can not be adaptively selected, a improved VMD algorithm based on energy difference as an evaluation parameter to automatically determine the decomposition level k is proposed. On this basis, a new method for early fault feature extraction of planetary gearbox based on the improved VMD and frequency-weighted energy operator is proposed. Firstly, the vibration signal is pre-decomposed by VMD, and the energy difference between the component signal and the original signal under different K-values is calculated respectively. The optimal decomposition level k is determined according to the energy difference curve. Then, according to kurtosis criterion, sensitive components are selected from the k modal components obtained by the decomposition to reconstruct. Finally, a new frequency-weighted energy operator is used to demodulate the reconstructed signal. The fault characteristic frequency information of the planetary gearbox can be accurately extracted from the energy spectrum. The method is applied to the simulation fault data and actual data of planetary gearbox, and the weak fault characteristics of planetary gearbox are extracted effectively, and the early fault characteristics are distinguished. The results show that the new method has certain application value and practical significance.

Entropy ◽  
2017 ◽  
Vol 19 (8) ◽  
pp. 421 ◽  
Author(s):  
Qing Li ◽  
Steven Liang

The periodical transient impulses caused by localized faults are sensitive and important characteristic information for rotating machinery fault diagnosis. However, it is very difficult to accurately extract transient impulses at the incipient fault stage because the fault impulse features are rather weak and always corrupted by heavy background noise. In this paper, a new transient impulse extraction methodology is proposed based on impulse-step dictionary and re-weighted minimizing nonconvex penalty Lq regular (R-WMNPLq, q = 0.5) for the incipient fault diagnosis of rolling bearings. Prior to the sparse representation, the original vibration signal is preprocessed by the variational mode decomposition (VMD) technique. Due to the physical mechanism of periodic double impacts, including step-like and impulse-like impacts, an impulse-step impact dictionary atom could be designed to match the natural waveform structure of vibration signals. On the other hand, the traditional sparse reconstruction approaches such as orthogonal matching pursuit (OMP), L1-norm regularization treat all vibration signal values equally and thus ignore the fact that the vibration peak value may have more useful information about periodical transient impulses and should be preserved at a larger weight value. Therefore, penalty and smoothing parameters are introduced on the reconstructed model to guarantee the reasonable distribution consistence of peak vibration values. Lastly, the proposed technique is applied to accelerated lifetime testing of rolling bearings, where it achieves a more noticeable and higher diagnostic accuracy compared with OMP, L1-norm regularization and traditional spectral Kurtogram (SK) method.


2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Shangkun Liu ◽  
Guiji Tang ◽  
Xiaolong Wang ◽  
Yuling He

A time-frequency analysis method based on improved variational mode decomposition and Teager energy operator (IVMD-TEO) is proposed for fault diagnosis of turbine rotor. Variational mode decomposition (VMD) can decompose a multicomponent signal into a number of band-limited monocomponent signals and can effectively avoid model mixing. To determine the number of monocomponents adaptively, VMD is improved using the correlation coefficient criterion. Firstly, IVMD algorithm is used to decompose a multicomponent signal into a number of monocompositions adaptively. Second, all the monocomponent signals’ instantaneous amplitude and instantaneous frequency are demodulated via TEO, respectively, because TEO has fast and high precision demodulation advantages to monocomponent signal. Finally, the time-frequency representation of original signal is exhibited by superposing the time-frequency representations of all the monocomponents. The analysis results of simulation signal and experimental turbine rotor in rising speed condition demonstrate that the proposed method has perfect multicomponent signal decomposition capacity and good time-frequency expression. It is a promising time-frequency analysis method for rotor fault diagnosis.


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