Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum

Liu, B.; Riemenschneider, S. D.; Xu, Yanhe

doi:10.1016/j.ymssp.2005.02.003

Cited by 283 publications

(157 citation statements)

References 8 publications

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“…In [16], Peng Z. K. et al compared the HHT and WT, and then verified that the HHT has better resolution and computational efficiency in time-frequency domain. EMD and Hilbert spectrum were used to diagnose rotating machines faults [17,18]. Several adaptive methods based on EMD have been developed such as the LMD [19], intrinsic time-scale decomposition (ITD) [20].…”

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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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.

View full text Add to dashboard Cite

show abstract

“…Frequency-domain analysis methods include Fourier transform, cepstrum analysis and so on. As for time-frequency analysis methods such as short-time Fourier transform (STFT) and empirical mode decomposition (EMD), they have been proven effective to extract features from nonlinear and non-stationary vibration signals [5]. Among these time-frequency techniques, WPD is one of the best tools since it has particular advantages for decomposing original signals into different frequency bands.…”

Section: Introductionmentioning

confidence: 99%

A feature fusion method using WPD-SVD and t-SNE for gearbox fault diagnosis

Sun¹,

Lü²,

Ma³

2017

Vib. proced.

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Abstract.The vibration signals of a gearbox always contain the dynamic operation information, which are important for the feature extraction and further work. However, the low signal-to-noise ratio and combined multi-mode faults make it difficult to extract discriminable features of gearboxes. In this study, a feature fusion method based on wavelet packet decomposition (WPD), singular value decomposition (SVD) and -Distributed stochastic neighbor embedding ( -SNE) for gearbox fault diagnosis is proposed. First, time-frequency analysis method of WPT-SVD as well as time-domain analysis methods are utilized to extract robust feature vectors of gearboxes with different conditions. As an effective method for the visualization of high-dimensional datasets, -SNE is then introduced to realize the dimensionality reduction of feature vectors. Finally, with the fused features, a radial basis function (RBF) neural network is trained to realize the classification of gearbox fault modes. Sufficient experiments have been implemented to validate the effectiveness and superiority of the proposed method by analyzing the vibration signals of gearboxes.

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“…By contrast, Hilbert-Huang transform (HHT) is not subject to other uncertainty limitations for time and frequency resolutions. HHT is a completely self-adapting approach for the identification of damage time instant and location in civil and mechanical structures (Andrade et al 2004, Liu et al 2006, Messina & Vittal 2005, Sanchez-Gasca et al 2005, Ruiz-Vega et al 2005.…”

Section: Introductionmentioning

confidence: 99%

Time-frequency analysis of DC bias vibration of transformer core on the basis of Hilbert–Huang transform

Liu

Yang

et al. 2016

ing.inv.

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This paper presents a time-frequency analysis of the vibration of transformer under direct current (DC) bias through Hilbert-Huang transform (HHT). First, the theory of DC bias for the transformer was analyzed. Next, the empirical mode decomposition (EMD) process, which is the key in HHT, was introduced. The results of EMD, namely, intrinsic mode functions (IMFs), were calculated and summed by Hilbert transform(HT) to obtain time-dependent series in a 2D time-frequency domain. Lastly, a test system of vibration measurement for the transformer was set up. Three direction (x, y, and z axes) components of core vibration were measured. Decomposition of EMD and HHT spectra showed that vibration strength increased, and odd harmonics were produced with DC bias. Results indicated that HHT is a viable signal processing tool for transformer health monitoring.Keywords: DC bias, transformer, vibration, HHT. RESUMENEn este trabajo se presenta un análisis tiempo-frecuencia de la vibración del transformador bajo polarización de corriente continua (CC) a través de transformación Hilbert-Huang (HHT). En primer lugar, se analizó la teoría de la DC de polarización para el transformador. A continuación, el proceso de descomposición de modo empírico (EMD), que es la clave en HHT, se introdujo. Los resultados de EMD, a saber, las funciones del modo intrínsecas (IMFS), se calcularon y se suman por transformada de Hilbert (HT) para obtener series dependienteS de tiempo en un dominio de tiempo-frecuencia 2D. Por último, un sistema de prueba de medición de vibraciones para el transformador se creó. Se midieron tres componentes de dirección de vibración del núcleo (ejes x, y, y z). La descomposición de los espectros EMD y HHT mostró que la fuerza de la vibración aumentó, y los armónicos impares se produjeron con polarización CC. Los resultados indicaron que la HHT es una herramienta de procesamiento de señales viable para la vigilancia de la salud del transformador.

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Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum

Cited by 283 publications

References 8 publications

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

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

A feature fusion method using WPD-SVD and t-SNE for gearbox fault diagnosis

Time-frequency analysis of DC bias vibration of transformer core on the basis of Hilbert–Huang transform

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