A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing

Ma, Jun; Wu, Jiande; Wang, Xiaodong

doi:10.1177/1461348418765973

Cited by 15 publications

(9 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It retains the effective signal value and rid the noise signal value, then reconstructs the effective signal in a suitable order. Essentially, SVD is a method of matrix orthogonalization in the mathematical view, which decomposes the given matrix into two matrixes U m×m and V n×n , as shown: [29][30][31][32].…”

Section: Theory Of the Svd Methodsmentioning

confidence: 99%

Signal de-noising in gear pitting fault identification by an improved singular value decomposition method

Zhou

Cui

Li³

et al. 2020

Forsch Ingenieurwes

View full text Add to dashboard Cite

In this research a new method of improved singular value decomposition (ISVD) is proposed for the vibration signal de-noising of gear pitting fault identification. In this method, the delay time τ and embedding dimension m of the Hankel matrix for SVD are optimized by autocorrelation function and Cao's algorithm respectively. Simulation and experiments are conducted to demonstrate the method. In the simulation, the ISVD method is employed to de-noise the artificial vibration signal in a mathematical model of gear pitting fault, the result demonstrates the signal-noise ratio (SNR) value is SNR = 31.3 dB, and the root-mean-square error (RMSE) value is RMSE = 0.34. In the experiment, the ISVD method is adopted to de-noising the vibration signal of gear pitting fault identification, the results demonstrate SNR is SNR >45 dB, and the RMSE value is RMSE <0.4 of the fault characteristic signals at each measuring point position. The results of simulation and experiment show, the ISVD method is efficient to de-noise the vibration signal of gear pitting fault. Signalentrauschen in Zahnrad-Lochfehler-Identifikation durch eine verbesserte Singular-Value-Zersetzungsmethode Zusammenfassung In dieser Forschung wird eine neue Methode zur verbesserten Singularwertzersetzung (ISVD) für die Schwingungssignalentnosierung von Zahnrad-Lochfehler-Identifikation vorgeschlagen. Bei dieser Methode werden die Verzögerungszeit und die Einbettungsdimension m der Hankel-Matrix für SVD durch die Autokorrelationsfunktion bzw. den Cao-Algorithmus optimiert. Simulationen und Experimente werden durchgeführt, um die Methode zu demonstrieren. In der Simulation wird die ISVD-Methode verwendet, um das künstliche Schwingungssignal in einem mathematischen Modell von Zahnrad-Pitting-Fehlern zu entrauschen; das Ergebnis zeigt den Signal-Rausch-Verhältnis-Wert (SNR) sNR = 31,3 dB und der RMSE-Wert (Root-Mean-Square Error) ist RMSE = 0,34. Im Experiment wird die ISVD-Methode zum Entrauschen des Schwingungssignals der Schaltfehleridentifikation von Zahnrädern angewandt, die Ergebnisse zeigen, dass SNR SNR > 45 dB ist, und der RMSE-Wert ist RMSE-0,4 der Fehlerkennzeichensignale an jeder Messpunktposition. Die Ergebnisse der Simulation und des Experiments zeigen, dass die ISVD-Methode effizient ist, um das Schwingungssignal von Zahnrad-Pitting-Fehlern zu entlärmen. Abbreviations C(τ) The autocorrelation function d Singular value difference spectrum D Singular value difference spectrum matrix Xintao Zhou

show abstract

Section: Theory Of the Svd Methodsmentioning

confidence: 99%

Signal de-noising in gear pitting fault identification by an improved singular value decomposition method

Zhou

Cui

Li³

et al. 2020

Forsch Ingenieurwes

View full text Add to dashboard Cite

show abstract

“…This method assumes that a signal, x(t), consists of p complex exponentials in the presence of Gaussian white noise. Recently, Ma et al (2018) used the Teager energy spectrum which is obtained by the application of the FFT on the Teager energy operator of the vibration signal and aims at envelope demodulation to achieve fault diagnosis of bearing. This operator calculates the energy of the signal at each time by using the data of three samples.…”

Section: Frequency Analysismentioning

confidence: 99%

“…These new feature vectors are the input of the improved support vector machines to classify data into fault classes. This criterion may change from a framework to another; for example, Ma et al (2018) used a correlation coefficient criterion between the PFs and the original vibration signal in order to choose the efficient PFs.…”

Section: Time-frequency Analysismentioning

confidence: 99%

PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes

Soualhi¹,

Hawwari

Medjaher

et al. 2020

IJPHM

View full text Add to dashboard Cite

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes.

show abstract

“…However, this problem is a difficult barrier for lack of priori knowledge about the original signal, which is very complex in actual engineering application. The useful feature will be lost if the selected singular value order is too small using the traditional difference spectrum method, while the excessive redundant noises will be remained if the selected order is too large using the median value or the mean value method [30,31]. Usually, after performing SVD on the original signal, there exists an elbow in the obtained singular value curve.…”

Section: Singular Value Order Determinationmentioning

confidence: 99%

Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

Wang

Yan

2019

Applied Sciences

View full text Add to dashboard Cite

The fault feature of wind turbine bearing is usually very weak in the early injury stage, in order to accurately identify the defect location, an original approach based on optimized cyclostationary blind deconvolution (OCYCBD) and singular value decomposition denoising (SVDD) is put forward to extract and enhance the fault feature effectively. In this diagnosis method, the fast spectral coherence is fused with the equal step size search strategy for the cyclic frequency parameter and the filter length parameter optimization, and a new frequency weighted energy entropy (FWEE) indicator which combining the advantages of the frequency weighted energy operator (FWEO) and the Shannon entropy, is developed for deconvolution signal evaluation during parameter optimization process. In addition, a novel singular value order determination approach based on fitting error minimum principle is utilized by SVDD to enhance the fault feature. During the process of defect identification, OCYCBD with the optimal parameters is firstly used to recover the informative source from the collected vibration signal. FWEO is further utilized to highlight the potential impulsive characteristics, and the instantaneous energy signal of deconvolution result can be acquired. The whole interferences contained in the instantaneous energy signal can’t be removed due to the weak fault signature and the severe background noise. Then, SVDD is applied to purify the instantaneous energy signal of deconvolution signal, by which the residual interference component is eliminated and the fault feature is strengthened immensely. Finally, frequency domain analysis is performed on the denoised instantaneous energy signal, and the defect location identification of wind turbine bearing can be achieved through analyzing the obvious spectral lines in the obtained enhanced energy spectrum. The collected signals from the experimental platform and the engineering field are both utilized to verify the feasibility of proposed method, and its superiority is further demonstrated through comparing with several well known diagnosis methods. The results indicate this novel method has distinct advantage on bearing weak feature extraction and enhancement.

show abstract

A hybrid fault diagnosis method based on singular value difference spectrum denoising and local mean decomposition for rolling bearing

Cited by 15 publications

References 44 publications

Signal de-noising in gear pitting fault identification by an improved singular value decomposition method

Signal de-noising in gear pitting fault identification by an improved singular value decomposition method

PHM Survey : Implementation of Signal Processing Methods for Monitoring Bearings and Gearboxes

Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD

Contact Info

Product

Resources

About