Vibration condition monitoring of planetary gearbox under varying external load

Bartelmus, Walter; Zimroz, Radosław

doi:10.1016/j.ymssp.2008.03.016

Cited by 217 publications

(91 citation statements)

References 10 publications

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“…Therefore, TF based condition monitoring methods can use another healthy system as a reference. This practice has been adopted by many other researchers as reported in [25], [26], [27], [5] where a healthy system was used as a reference to inspect the possible damage in a system of the same nature.…”

Section: Wind Turbine Bearing Condition Monitoringmentioning

confidence: 99%

Generalized Transmissibility Damage Indicator With Application to Wind Turbine Component Condition Monitoring

Zhang

Lang

Papaelias

2016

IEEE Trans. Ind. Electron.

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Abstract-Frequency methods such as frequency spectrum analysis, frequency spike detection, demodulation, envelope spectrum method have been widely used for condition monitoring of engineering structural systems. Different from the conventional frequency methods, the transmissibility function (TF) represents the relationship between different system output responses such as, e.g. vibration and acoustic emission sensor measurements. This paper introduces a simple and effective generalized transmissibility damage indicator (GTDI) for TF based condition monitoring. Unlike the conventional transmissibility damage indicator (TDI), the new GTDI can improve the detection sensitivity, reduces noise effects and avoid dynamic loadings effects. This is achieved by combining multiple groups of data to obtain more accurate transmissibility analysis, exploiting all the available TFs, and using multiple references. This has two advantages. First, it does not require any other priori knowledge about the system responses. Therefore the method can be used for the condition monitoring of a wide range of components or systems. Further, the method can be easily implemented using Fast Fourier transform (FFT) or power spectra density (PSD) methods and therefore is computationally efficient. These make the method very suitable for implementing online real-time condition monitoring. The method is investigated by simulation studies and then applied to analyze the vibration data of the main bearing of operating wind turbines, producing very promising results.

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Section: Wind Turbine Bearing Condition Monitoringmentioning

confidence: 99%

Generalized Transmissibility Damage Indicator With Application to Wind Turbine Component Condition Monitoring

Zhang

Lang

Papaelias

2016

IEEE Trans. Ind. Electron.

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“…The authors noticed that periodic (cyclic) variation of external load causes amplitude modulation whereas speed variation (associated with load variation) causes frequency modulation [1,20].…”

Section: Experimental Validationmentioning

confidence: 99%

“…Bartelmus and Zimroz [1,2] showed that the identification of the varying external load is crucial to the evaluation of a planetary gearbox condition. Randall [3] states that the amplitude of a gearbox casing vibration, caused by the meshing of the gears, is modulated by the fluctuation of torque load.…”

Section: Introductionmentioning

confidence: 99%

Gearbox Vibration Signal Amplitude and Frequency Modulation

Chaari

Bartelmus

Zimroz

et al. 2012

Shock and Vibration

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Abstract. 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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“…Choy et al [10] have shown the capacity of WVD in analyzing the effects of surface pitting and wear on the vibrations of a gear transmission system. Thereafter, several studies have exploit the technique like the work done by [11].…”

Section: Introductionmentioning

confidence: 99%

Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

Rahmoune¹,

Merainani²,

Benazzouz³

et al. 2018

J. vibroeng.

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Abstract. Vibration signal of gearbox systems carries the important dynamic information for fault diagnosis. However, vibration signals always show non stationary behavior and overwhelmed by a large amount of noise make this task challenging in many cases. Thus, a new fault diagnosis method combining the Hilbert empirical wavelet transform (HEWT), the singular value decomposition (SVD) and Elman neural network is proposed in this paper. Vibration signals of normal gear, gear with tooth root crack, gear with chipped tooth in width, gear with chipped tooth in length, gear with missing tooth and gear with general surface wear are collected in different speed and load conditions. HEWT, a new self-adaptive time-frequency analysis, was applied to the vibration signals to obtain the instantaneous amplitude matrices. Singular value vectors, as the fault feature vectors were then acquired by applying the SVD. Last, the Elman neural network was used for automatic gearbox fault identification and classification. Through experimental results, it was concluded that the proposed method can accurately extract and classify the gear fault features under variable conditions. Moreover, the performance of the proposed HEWT-SVD method has an advantage over that of Hilbert-Huang transform (HHT)-SVD, local mean decomposition (LMD)-SVD or wavelet packet transform (WPT)-PCA for feature extraction.

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Vibration condition monitoring of planetary gearbox under varying external load

Cited by 217 publications

References 10 publications

Generalized Transmissibility Damage Indicator With Application to Wind Turbine Component Condition Monitoring

Generalized Transmissibility Damage Indicator With Application to Wind Turbine Component Condition Monitoring

Gearbox Vibration Signal Amplitude and Frequency Modulation

Gear fault feature extraction and classification of singular value decomposition based on Hilbert empirical wavelet transform

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