Gearbox fault detection using Hilbert and wavelet packet transform

Fan, Xianfeng; Zuo, Ming J.

doi:10.1016/j.ymssp.2005.08.032

Cited by 277 publications

(125 citation statements)

References 14 publications

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“…The wavelet transform solves the problem of fixed window size, by using short windows to analyze high frequency components (good time localization) and large windows for low frequency components (good frequency localization). An example of wavelet transforms applied for condition monitoring applications was presented in Fan and Zuo (2006). Several other frequency methods exist for monitoring applications, e.g., the Empirical Mode Decomposition, as presented in Antoniadou et al (2015), which can offer similar benefits to the wavelet transform.…”

Section: Wavelet Coefficientsmentioning

confidence: 99%

Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

Matthaiou

Khandelwal

Antoniadou

2017

Front. Built Environ.

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In this study, condition monitoring strategies are examined for gas turbine engines using vibration data. The focus is on data-driven approaches, for this reason a novelty detection framework is considered for the development of reliable data-driven models that can describe the underlying relationships of the processes taking place during an engine's operation. From a data analysis perspective, the high dimensionality of features extracted and the data complexity are two problems that need to be dealt with throughout analyses of this type. The latter refers to the fact that the healthy engine state data can be non-stationary. To address this, the implementation of the wavelet transform is examined to get a set of features from vibration signals that describe the non-stationary parts. The problem of high dimensionality of the features is addressed by "compressing" them using the kernel principal component analysis so that more meaningful, lowerdimensional features can be used to train the pattern recognition algorithms. For feature discrimination, a novelty detection scheme that is based on the one-class support vector machine (OCSVM) algorithm is chosen for investigation. The main advantage, when compared to other pattern recognition algorithms, is that the learning problem is being cast as a quadratic program. The developed condition monitoring strategy can be applied for detecting excessive vibration levels that can lead to engine component failure. Here, we demonstrate its performance on vibration data from an experimental gas turbine engine operating on different conditions. Engine vibration data that are designated as belonging to the engine's "normal" condition correspond to fuels and airto-fuel ratio combinations, in which the engine experienced low levels of vibration. Results demonstrate that such novelty detection schemes can achieve a satisfactory validation accuracy through appropriate selection of two parameters of the OCSVM, the kernel width γ and optimization penalty parameter ν. This selection was made by searching along a fixed grid space of values and choosing the combination that provided the highest cross-validation accuracy. Nevertheless, there exist challenges that are discussed along with suggestions for future work that can be used to enhance similar novelty detection schemes.

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Section: Wavelet Coefficientsmentioning

confidence: 99%

Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

Matthaiou

Khandelwal

Antoniadou

2017

Front. Built Environ.

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“…In (Fan and Zuo, 2006) a new fault detection method that combines Hilbert transform and wavelet packet transform was proposed. The wavelet packet node energy method is used as feature.…”

Section: Gearboxesmentioning

confidence: 99%

Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

Λούτας¹,

Kostopoulos²

2012

Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

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“…Some new approaches which are based on the combination of wavelet analysis and other technologies also have been developed. Fan and Zuo (2006) proposed a new failure detection method that combines both of the advantages of Hilbert transform and wavelet packet transform. The principle of this method is extracting the frequency components as failure features by wavelet packet transform based on removing carrier signals from the collected vibration signal to decrease the influence of irrelevant information by Hilbert transform.…”

Section: Introductionmentioning

confidence: 99%

Application of statistical parameters and discrete wavelet transform to gear damage diagnosis

Fan

Ikejo

Nagamura

et al. 2014

JAMDSM

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Gear is one of the most commonly used and important components in machine system. Mainly gear failure may cause serious damage of the whole equipment, even huge economic losses. Therefore, it is important to detect the gear damage as early as possible. This paper provides a method of diagnosis and location for gear damage based on statistical approach and discrete wavelet transform (DWT). The vibration signals of gear box and bearing box are measured as analytical data. To emphasize the failure features of the measured signal, gear motion residual signal is obtained from the raw signal and provides a better indication of the existence of failures. Additionally, the method of discrete wavelet transform is employed to reduce the noise from the residual signal and decompose the signal into several decomposition levels. Because of the good sensitivity to the altering of vibration signal, statistical parameters such as standard deviation, kurtosis and so on are extracted from the raw signal and the reconstructed signal by DWT as failure features for detecting the gear damage. For a comparison of the raw signal and the reconstructed signal, the variation of statistical parameters among different kinds of test gears is also discussed by the significance test. The validity of the presented method is testified by some experiments under different conditions. Three kinds of gears namely normal gear, spot damaged gear and pitted gear are tested on the power circulating type gear testing machine, and the vibration acceleration on both gear box and bearing box is obtained. The experimental results show the effectiveness of this method on the diagnosis of gear damage.

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Gearbox fault detection using Hilbert and wavelet packet transform

Cited by 277 publications

References 14 publications

Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

Vibration Monitoring of Gas Turbine Engines: Machine-Learning Approaches and Their Challenges

Utilising the Wavelet Transform in Condition-Based Maintenance: A Review with Applications

Application of statistical parameters and discrete wavelet transform to gear damage diagnosis

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