scholarly journals Gearbox Vibration Signal Amplitude and Frequency Modulation

2012 ◽  
Vol 19 (4) ◽  
pp. 635-652 ◽  
Author(s):  
Fakher Chaari ◽  
Walter Bartelmus ◽  
Radoslaw Zimroz ◽  
Tahar Fakhfakh ◽  
Mohamed Haddar
Keyword(s):  
Vibration Signal ◽  
Signal Amplitude ◽  
Spur Gear ◽  
Main Task ◽  
Time Varying ◽  
Vibration Signals ◽  
Gearbox Vibration ◽  
Load Effects ◽  
Short Time ◽  
Load Conditions

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.

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
Keyword(s):  
Planetary Gear ◽  
Vibration Signal ◽  
Spur Gear ◽  
Variational Mode Decomposition ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
The Difference ◽  
Simulation Signal ◽  
Frequency Demodulation ◽  
Frequency Modulations

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.

scholarly journals Application of Choi—Williams Reduced Interference Time Frequency Distribution to Machinery Diagnostics

1995 ◽  
Vol 2 (6) ◽  
pp. 437-444 ◽  
Author(s):  
Howard A. Gaberson
Keyword(s):  
Fourier Transform ◽  
Vibration Signal ◽  
Test Cases ◽  
Short Time Fourier Transform ◽  
Vibration Signals ◽  
Time Frequency ◽  
Machinery Diagnostics ◽  
Short Time ◽  
Amplitude And Frequency Modulation ◽  
Time Frequency Distribution

This article discusses time frequency analysis of machinery diagnostic vibration signals. The short time Fourier transform, the Wigner, and the Choi–Williams distributions are explained and illustrated with test cases. Examples of Choi—Williams analyses of machinery vibration signals are presented. The analyses detect discontinuities in the signals and their timing, amplitude and frequency modulation, and the presence of different components in a vibration signal.

Application of EEMD Energy Distribution and Grey Incidence in Gearbox Fault Diagnosis

2013 ◽  
Vol 347-350 ◽  
pp. 430-433
Author(s):  
Wen Bin Zhang ◽  
Jia Xing Zhu ◽  
Ya Song Pu ◽  
Yan Jie Zhou
Keyword(s):  
Fault Diagnosis ◽  
Energy Distribution ◽  
Rank Order ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Small Sample ◽  
Morphological Filter ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Gearbox Vibration

In this paper, a new comprehensive gearbox fault diagnosis method was proposed based on rank-order morphological filter, ensemble empirical mode decomposition (EEMD) and grey incidence. Firstly, the rank-order morphological filter was defined and the line structure element was selected for rank-order morphological filter to de-noise the original acceleration vibration signal. Secondly, de-noised gearbox vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some IMFs containing the most dominant fault information were calculated the energy distribution. Finally, due to the grey incidence has good classify capacity for small sample pattern identification; these energy distributions could serve as the feature vectors, the grey incidence of different gearbox vibration signals was calculated to identify the fault pattern and condition. Practical results show that the proposed method can be used in gear fault diagnosis effectively.

Features of Early Cracks in a Gear Based on a 16DOF Nonlinear Time-Varying Stiffness Dynamic Model

2009 ◽  
Author(s):  
Xiaojun Zhou ◽  
Yimin Shao ◽  
Ming J. Zuo
Keyword(s):  
Crack Growth ◽  
Dynamic Model ◽  
Digital Filter ◽  
Energy Method ◽  
Vibration Signal ◽  
Spur Gear ◽  
Time Varying ◽  
Meshing Stiffness ◽  
One Stage ◽  
The Relationship

A 16DOF nonlinear time-varying stiffness dynamic model of a one-stage spur gear system is studied when there is a crack growth on the pinion; the energy method is then used for calculating the meshing stiffness of the gear pairs. A Hybrid Digital Filter is used to detect the feature signal which is induced by the tooth crack when the vibration signal contains heavy noise. The relationship between the indicators and the growth of the crack is given.

Gearbox Fault Diagnosis Based on EMD and Coefficient-Energy Value

2012 ◽  
Vol 542-543 ◽  
pp. 234-237
Author(s):  
Ping Wang ◽  
De Xiang Zhang ◽  
Yan Li Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Intrinsic Mode Function ◽  
Vibration Signals ◽  
Energy Value ◽  
Mode Function ◽  
Mode Decomposition ◽  
Gearbox Vibration ◽  
Vibration Signal Analysis ◽  
Fault Information

This paper applies the empirical mode decomposition (EMD) methods to gearbox vibration signal analysis capture from vibrating acceleration sensor for gearbox fault diagnosis. The original modulation fault vibration signals are firstly decomposed into a number of intrinsic mode function (IMF) by the EMD method. Then the fault information diagnosis of the gearbox vibration signals can be extracted from the coefficient-energy value of intrinsic mode function. Experiment result has shown the feasibility and efficiency of the EMD algorithms and energy characteristic method in fault diagnosis and fault message abstraction. It is significant for the monitor operating state of gearbox and detects incipient faults as soon as possible.

The Application and Research of Order Analysis in Gearbox Fault Diagnosis

2014 ◽  
Vol 635-637 ◽  
pp. 844-850
Author(s):  
Xiao Feng Yue ◽  
Cheng Wei Zhu
Keyword(s):  
Vibration Signal ◽  
Signal Amplitude ◽  
Shaft Speed ◽  
Angular Domain ◽  
Order Analysis ◽  
Time Domain Signal ◽  
Gearbox Vibration ◽  
The Time Domain ◽  
Fault Mechanism ◽  
Fault Characteristic

This paper researched the failure mechanism of the gearbox and resample algorithm in order analysis. For diagnosing the types of gearbox’s fault by analyzing the acquisition of vibration signal and extracting the fault characteristics of the gear. A function that relation gearbox vibration signal amplitude to order and shaft speed and transform from the time domain signal to angular domain signal was deduced. The MQ250 gearbox model was applied for the experimental analysis in this paper, the fault mechanism function of this gearbox has been successfully verified and the gearbox fault characteristic information has been extracted.

Short-Time Kurtogram for Bearing Fault Feature Extraction Under Time-Varying Speed Conditions

2018 ◽  
Author(s):  
Huan Huang ◽  
Natalie Baddour ◽  
Ming Liang
Keyword(s):  
Experimental Data ◽  
Feature Extraction ◽  
Vibration Signal ◽  
Analysis Tool ◽  
Time Varying ◽  
Bearing Fault ◽  
Fault Features ◽  
Induced Signal ◽  
Short Time ◽  
Fault Feature Extraction

The kurtogram is a spectral analysis tool used to detect non-stationarities in a signal. It can be effectively used to determine the optimal filter for bearing fault feature extraction from a blurred vibration signal, since the transients of the bearing fault-induced signal can be regarded as non-stationary. However, the effectiveness of the kurtogram is diminished when the signal is collected from a bearing operating under time-varying speed conditions. There is a need to improve the performance of the kurtogram under time-varying speed conditions. In this paper, a short-time kurtogram method is proposed for bearing fault feature extraction under time-varying speed conditions. The performance of the short-time kurtogram is examined with experimental data. The results demonstrate that the short-time kurtogram can effectively be used to extract bearing fault features under time-varying speed conditions.

scholarly journals Order Tracking Using Variational Mode Decomposition to Detect Gear Faults under Speed Fluctuations

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Madhurjya Dev Choudhury ◽  
Liu Hong ◽  
Jaspreet Singh Dhupia
Keyword(s):  
Fault Detection ◽  
Reference Signal ◽  
Vibration Signal ◽  
Operating Speed ◽  
Variational Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Order Tracking ◽  
Mode Decomposition ◽  
Gearbox Vibration

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.

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