scholarly journals Time-Frequency Analysis of Torsional Vibration Signals in Resonance Region for Planetary Gearbox Fault Diagnosis Under Variable Speed Conditions

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 21918-21926 ◽  
Author(s):  
Xiaowang Chen ◽  
Zhipeng Feng
Author(s):  
Xiaotong Tu ◽  
Yue Hu ◽  
Fucai Li

Vibration monitoring is an effective method for mechanical fault diagnosis. Wind turbines usually operated under varying-speed condition. Time-frequency analysis (TFA) is a reliable technique to handle such kind of nonstationary signal. In this paper, a new scheme, called current-aided TFA, is proposed to diagnose the planetary gearbox. This new technique acquires necessary information required by TFA from a current signal. The current signal is firstly used to estimate the rotating speed of the shaft. These parameters are applied to the demodulation transform to obtain a rough time-frequency distribution (TFD). Finally, the synchrosqueezing method further enhances the concentration of the obtained TFD. The validation and application of the proposed method are presented by a simulated signal and a vibration signal captured from a test rig.


Author(s):  
Yue Hu ◽  
Xiaotong Tu ◽  
Fucai Li

The planetary gearbox is one of the key components in the rotating machinery. The planetary gearbox is prone to malfunction, which increases downtime and repair costs. Hence, the fault diagnosis of the planetary gearbox is an important research topic. The acquired signal from the planetary gearbox exhibit strongly time-variant and nonstationary features since the planetary gearbox usually works at time-varying speeds. In this study, a new time-frequency analysis method is proposed. This method takes the spectrum shape into account and partitions the time-frequency into several components. Then the fault feature of the planetary gearbox is detected by analyzing the decomposed components. The simulated signal and the experimental signals under nonstationary conditions are analyzed to verify the effectiveness the proposed method. Results show that the proposed method can efficiently extract the fault feature of the planet gear.


2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Kangqiang Li ◽  
Zhipeng Feng ◽  
Xihui Liang

Planetary gearbox torsional vibration signals are free from the extra amplitude modulation effect due to time-varying transmission paths and have simpler frequency structure than translational ones. Gear faults result in modulation on the torsional resonance vibration and are manifested by the modulation feature. These merits are exploited for planetary gearbox fault diagnosis in this paper. Gear fault induced torsional vibrations in resonance region are modelled as amplitude modulation and frequency modulation (AM-FM) processes, the explicit equation of Fourier spectrum is derived, and the sideband characteristics are summarized. To avoid complex sideband analysis, amplitude and frequency demodulation analysis methods are exploited. The equations of amplitude and frequency demodulated spectra are derived in closed form, and their frequency structures are revealed. For fault diagnosis based on above theoretical derivations, a resonance frequency identification approach is proposed through time-frequency analysis of torsional vibrations during variable speed processes, according to the independence nature of resonance frequency on running conditions. The theoretical derivations and proposed approach are illustrated by numerical simulated signal analysis and are further validated through dynamics modelling and lab experimental tests. Localized faults on the sun, planet, and ring gears are successfully diagnosed.


Author(s):  
M. A. AL-MANIE ◽  
W. J. WANG

The evolutionary periodogram has been introduced to mechanical fault diagnosis and relationship between the evolutionary periodogram and time-frequency spectrogram has been investigated. The evolutionary periodogram is unveiled as an especially windowed spectrogram, and is applied to gearbox fault diagnosis. It has been shown that the window used in the evolutionary periodogram is not a single function but a combination of a set of functions. Two cases of gearbox diagnosis are presented as examples of application. Vibration signals and a synchronous signal are collected for the analysis. The time synchronous averaging is used to reduce background noise or random transients to enhance the periodicity of a specific gear rotation. The performance of the evolutionary periodogram has been compared with the spectrogram for gear diagnosis, showing that the evolutionary periodogram is an alternative technique in time-frequency analysis for fault detection and better resolution can be obtained as more choices are offered by the way of constructing the window.


2021 ◽  
Author(s):  
Lingli Cui ◽  
Yuchuan Peng ◽  
Tongtong LIU

Abstract The adaptive chirp mode decomposition (ACMD) has good time-frequency representation results in analyzing chirp signals, while there is a time-frequency ambiguity problem in the analysis of variable speed planetary gearbox vibration signals. To address this problem, a planetary gearbox fault diagnosis method based on improved polynomial adaptive chirp mode decomposition wavelet is proposed (IPACMD). Using Adaptive chirp mode decomposition, the amplitude and instantaneous frequency of multiple signal components are estimated; To avoid over-decomposition to generate spurious signal components, the similarity conditional entropy is used to optimize the adaptive chirp mode decomposition threshold ;The polynomial chirp transform (PCT) using a polynomial function instead of the linear chirp kernel in the chirp transform to improve the time-frequency aggregation of the instantaneous frequency curve of each signal component and output high-resolution time-frequency representation results. Compared with the original method, the proposed method has better time-frequency aggregation and is more effective for the analysis of variable speed planetary gearbox vibration signals. The simulation and experimental study results show that the method can effectively identify the frequency components and time-frequency characteristics of the variable-speed planetary gearbox vibration signal and realize the fault diagnosis of the planetary gearbox.


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