Application of the Wavelet Multi-resolution Analysis and Hilbert transform for the prediction of gear tooth defects

Djebala, Abderrazek; Ouelaa, Nouredine; Benchaabane, Chaouki; Laefer, Debra F.

doi:10.1007/s11012-012-9538-1

Cited by 43 publications

(16 citation statements)

References 19 publications

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“…In case of a localized fault on tooth surface, the amplitude and phase modulation of the meshing frequency can be visible in frequency spectrum of the vibration signal. In the other words, sidebands will appear around the meshing frequency and its harmonics, the spacing of sidebands corresponds to the rotational frequency of the shaft carrying the defective gear (Djebala, et al, 2012, Dalpiaz, 1998. However, the signals acquired from accelerometer mounted on gear box are often inevitably contaminated by the interference signal, which is caused by vibrations from shafts, bearings, and other components on the testing machine.…”

Section: Experimental Results and Discussion 51 Analysis Of Vibratimentioning

confidence: 99%

Gear damage diagnosis and classification based on support vector machines

Fan

Ikejo

Nagamura

et al. 2014

JAMDSM

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The operation status of gear device can directly affect the working conditions of the whole machine system. Thus, it is crucial to detect the gear damage as early as possible to prevent the system from malfunction. This paper proposes an intelligent diagnosis method for gear damage using multiple classifiers of support vector machines with extracted failure feature vector. The vibration signal of gear box is employed as the analytical data in this paper. In order to illustrate the representative characters of gear conditions, statistical parameters and characteristic amplitude ratios of frequency bands are extracted from the vibration signals in time-domain and frequency-domain respectively, which are served as failure feature vector for the following diagnosis. Moreover, to reduce the dimensions of the failure feature vector, the technology of principal component analysis is adopted to transform the original failure feature vector into a new smaller set of variables as inputs to classifiers of support vector machines. In order to classify different types of gears, multiple classifiers of support vector machines based on the binary tree are designed. The validity of this approach is investigated by the experiment. Three kinds of gears, namely normal gear, spot damaged gear and pitted gear, are tested on the power circulating type gear testing machine. The vibration accelerations of gear box are measured as original data. Most of the samples are correctly classified by the provided method, which demonstrates the effectiveness of the proposed method on the application of gear damage diagnosis. IntroductionSince minor gear damage may cause serious failures of the entire equipment, early detection of gear damage is one of the important measures to prevent the machine system from malfunction. Analyzing the vibration signal adopted from the gear or gear box is one of the effective methods to diagnose gear failures. Researchers have done countless studies in this respect and have developed many methods based on the analysis of vibration signal in time domain, frequency domain and time-frequency domain. In frequency domain, it is well known that the fault condition of gears can be observed at the meshing frequency and its harmonics, together with sidebands by spectra analysis of vibration signal (Dalpiaz, et al., 2000). However, the acquired signals are always inevitably interfered by the vibration of other components in the system, or the environment disturbances. Therefore, in order to strengthen the characteristics of the useful signal, researchers have developed many other signal processing techniques for gear fault detection, such as time synchronous average method in time domain, wavelet transform and Hilber transform techniques in time-frequency domain, advanced statistical approaches and so on. These techniques have been satisfactorily applied to both fault detection and identification of the damaged gear (Houjoh, et al., 2007, Tanaka, et al., 2012, Wang, et al., 2010 Fan, Ikejo, Nagamura, Kawada and Has...

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Section: Experimental Results and Discussion 51 Analysis Of Vibratimentioning

confidence: 99%

Gear damage diagnosis and classification based on support vector machines

Fan

Ikejo

Nagamura

et al. 2014

JAMDSM

View full text Add to dashboard Cite

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“…The most common signal processing methods include time-domain analysis and time-frequency analysis [5] such as amplitude spectrum analysis, order analysis [6], cepstrum analysis [7,8], envelope spectrum analysis [6], Hilbert transform demodulation analysis [9], wavelet analysis, autocorrelation analysis [10], and empirical mode decomposition (EMD) [11]. Among the available vibration analysis methods, EMD is an effective signal analysis method which is suited for dealing with nonlinear and nonstationary signals [12].…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

Chen

Zhou

Fang

et al. 2017

International Journal of Rotating Machinery

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A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.

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“…An optimized discrete version especially adapted for shock signals' analysis is proposed. It has been successfully applied for the diagnosis of bearing [11] and gear defects [15]. A good review with applications of the wavelets for fault diagnosis of rotary machines is proposed in [16].…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault detection using a hybrid method based on Empirical Mode Decomposition and optimized wavelet multi-resolution analysis

Djebala

Babouri

Ouelaa

2015

Int J Adv Manuf Technol

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In this paper, a hybrid method based on the combination of Empirical Mode Decomposition (EMD) and an optimized wavelet multi-resolution analysis (WMRA) is proposed. The pairing of these two time-frequency techniques is well adapted to analyze transient signals generated by rolling bearing defects. First, an optimal intrinsic mode function (IMF), having the most important kurtosis and covering the significant natural frequency, is selected using the classical EMD analysis. An envelope signal of the selected IMF's energy is calculated from Hilbert transform. This envelope is then analyzed by an optimized WMRA especially adapted to shock signals. A reconstructed signal is obtained and an envelope spectrum is performed to highlight the fault characteristic frequency. The results show that the proposed method can effectively get better time and frequency domain visualization of the fault occurrence compared to the application of WMRA or EMD alone.

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Application of the Wavelet Multi-resolution Analysis and Hilbert transform for the prediction of gear tooth defects

Cited by 43 publications

References 19 publications

Gear damage diagnosis and classification based on support vector machines

Gear damage diagnosis and classification based on support vector machines

Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

Rolling bearing fault detection using a hybrid method based on Empirical Mode Decomposition and optimized wavelet multi-resolution analysis

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