Gear Crack Detection by Adaptive Amplitude and Phase Demodulation

Brie, David; Tomczak, Marc; Oehlmann, Harald; Richard, Alain

doi:10.1006/mssp.1996.0068

Cited by 32 publications

(19 citation statements)

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“…, m), m are the model parameters and model order of the autoregressive model AR(m) of c i (t), respectively; e i (t) is the remnant of the model and is a white noises sequence whose mean value is zero and variance is σ 2 i . Since the parameters ϕ ik can reflect the inherent characteristics of a gear vibration system and the variance of the remnant σ 2 i is tightly related with the output characteristics of the system, ϕ ik and σ 2 i can be chosen as feature vectors A i = [ϕ i1 , ϕ i2 , . .…”

Section: Diagnosis Approach For Gears Based On Imf Ar Model and Svmmentioning

confidence: 99%

“…(3) In order to eliminate the effect of the signal amplitude to the variance of the remnant σ 2 i , normalize each IMF component to achieve a new component:…”

Section: Diagnosis Approach For Gears Based On Imf Ar Model and Svmmentioning

confidence: 99%

“…Signal processing methods have been widely used to extract fault feature of gear vibration signals [1,2]. Fourier transform (FT), which has been the dominating analysis tool for feature extraction of stationary signals, could produce the statistical average characteristics over the entire duration of the data.…”

Section: Introductionmentioning

confidence: 99%

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A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

Cheng

2008

EURASIP J. Adv. Signal Process.

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An accurate autoregressive (AR) model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD), which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs), is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM) is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.

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Section: Diagnosis Approach For Gears Based On Imf Ar Model and Svmmentioning

confidence: 99%

“…(3) In order to eliminate the effect of the signal amplitude to the variance of the remnant σ 2 i , normalize each IMF component to achieve a new component:…”

Section: Diagnosis Approach For Gears Based On Imf Ar Model and Svmmentioning

confidence: 99%

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A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

Cheng

2008

EURASIP J. Adv. Signal Process.

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“…McFadden [2] proposed a technique for extracting the amplitude and phase modulations of a tooth meshing vibration from the vibration average and applied it to the early detection of local defects such as fatigue cracks. Brie et al [3] used an adaptive algorithm to estimate the slow time-varying amplitude and phase, and a sequential abrupt change detection algorithm coupled to the adaptive algorithm to decide whether a crack is present or not. Amabili et al [4] identified modal parameters (natural frequency and damping) and the equivalent gear error of a spur gear pair using the measurement of gear torsional vibrations.…”

Section: Introductionmentioning

confidence: 99%

Estimating Size of Gear Tooth Root Crack Using Embedded Modelling

Lee

Choi

2002

Mechanical Systems and Signal Processing

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“…The vibration demodulation analysis has been widely investigated [1][2][3][4][5][6][7][8][9][10][11][12][13][14] detecting local tooth damage, such as cracks, pitting, etc. However, previous works have not considered how the technique works with the time synchronous average (TSA) [15] of vibration signals under conditions of variable amplitudes of the mesh harmonics and their surrounding sidebands [1].…”

Section: Introductionmentioning

confidence: 99%

Novel adaptation of the demodulation technology for gear damage detection to variable amplitudes of mesh harmonics

Combet

Gelman

2011

Mechanical Systems and Signal Processing

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a b s t r a c tIn this paper, a novel adaptive demodulation technique including a new diagnostic feature is proposed for gear diagnosis in conditions of variable amplitudes of the mesh harmonics. This vibration technique employs the time synchronous average (TSA) of vibration signals. The new adaptive diagnostic feature is defined as the ratio of the sum of the sideband components of the envelope spectrum of a mesh harmonic to the measured power of the mesh harmonic. The proposed adaptation of the technique is justified theoretically and experimentally by the high level of the positive covariance between amplitudes of the mesh harmonics and the sidebands in conditions of variable amplitudes of the mesh harmonics. It is shown that the adaptive demodulation technique preserves effectiveness of local fault detection of gears operating in conditions of variable mesh amplitudes.

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Gear Crack Detection by Adaptive Amplitude and Phase Demodulation

Cited by 32 publications

References 0 publications

A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM

Estimating Size of Gear Tooth Root Crack Using Embedded Modelling

Novel adaptation of the demodulation technology for gear damage detection to variable amplitudes of mesh harmonics

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