Blind Source Separation: A Tool for Rotating Machine Monitoring by Vibrations Analysis?

Gellé, Guillaume; Colas, Maxime; Servière, Christine

doi:10.1006/jsvi.2001.3819

Cited by 94 publications

(43 citation statements)

References 19 publications

(14 reference statements)

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“…From the figure a, b,. It can be seen that in the normal state of the autocorrelation coefficient R.r (z) in the three cycles, the fault features are characterized by very good circular ring effect, and not in a bulge or burr phenomenon appears on the characteristic frequency, objectively illustrates the bearing is not failure, but keep a good state of normal operation [9] .…”

Section: Bearing Vibration Experimentsmentioning

confidence: 96%

Bearing Fault Transient Components Based on Absolute Autocorrelation Detection and Feature Expression Research

Zhang¹

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-The article on the basis of analyzing the traditional autocorrelation, put forward an improvement on the transient impact composition of autocorrelation detection method, this method is able to signal the periodic transient component with more parameters. Under various faults of bearing vibration signal applications show that this method can very effectively expressed in the vibration impact characteristics, and can be conducted according to the characteristics of bearing fault diagnosis. The idea of this method is based on signal absolute value instead of the envelope calculations, so it can reduce the computational complexity, still can obtain good results.

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Section: Bearing Vibration Experimentsmentioning

confidence: 96%

Bearing Fault Transient Components Based on Absolute Autocorrelation Detection and Feature Expression Research

Zhang¹

2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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“…Many advanced techniques have been employed to detect and extract features from vibration signals [1][2][3][4][5][6]. Some conventional fault diagnosis techniques based on vibration signals extract the characteristic quantities from the time domain and frequency domain, statistical indexes, such as peak amplitude, root mean square amplitude, kurtosis and frequency components [7].…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction of Diesel Engine Based on Bispectrum Image Fractal Dimension

Zhang

Liu

et al. 2018

Chin. J. Mech. Eng.

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Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early fault signals are mostly weak energy signals, and time domain or frequency domain features will be overwhelmed by strong background noise. In order consistent features to be extracted that accurately represent the state of the engine, bispectrum estimation is used to analyze the nonlinearity, non-Gaussianity and quadratic phase coupling (QPC) information of the engine vibration signals under different conditions. Digital image processing and fractal theory is used to extract the fractal features of the bispectrum pictures. The outcomes demonstrate that the diesel engine vibration signal bispectrum under different working conditions shows an obvious differences and the most complicated bispectrum is in the normal state. The fractal dimension of various invalid signs is novel and diverse fractal parameters were utilized to separate and characterize them. The value of the fractal dimension is consistent with the non-Gaussian intensity of the signal, so it can be used as an eigenvalue of fault diagnosis, and also be used as a non-Gaussian signal strength indicator. Consequently, a symptomatic approach in view of the hypothetical outcome is inferred and checked by the examination of vibration signals from the diesel motor. The proposed research provides the basis for on-line monitoring and diagnosis of valve train faults.

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“…In such cases, a new contribution separation technique is necessary in order to identify high contributing sound sources and apply effective countermeasures. Accordingly, we have carried out basic studies to develop a new method utilizing independent component analysis (ICA) (Hyvarinen et al, 2001;Gelle et al, 2001;Araki et al, 2008;Yoshida and Ishihara, 2013;Yoshida and Hirano, 2014); the method does not require any input signals or transfer functions. In the ICA technique, sound source signals having independence from each other are estimated using their statistical characteristics (central limit theorem) and optimization theorem (Hyvarinen et al, 2001).…”

Section: Introductionmentioning

confidence: 99%

Road & wind noise contribution separation using only interior noise having multiple sound sources -Accuracy improvement and permutation solution-

Inoue

2017

Mechanical Engineering Journal

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A contribution separation technique for estimating road noise and wind noise contributions to vehicle interior noise without using any sound source information was considered when each noise was from multiple sources. In the test, road, wind or mixed noise sources were reproduced from four speakers placed at different positions. The mixed interior noises were recorded at two separate positions surrounded by the speakers. Independent component analysis (ICA) was then applied as the contribution separation method for obtaining them at the target position (driver seat position). As the original condition, ICA was firstly applied to the recorded noises at the driver seat (target noise) and at the assistant seat (reference noise). The result revealed that the estimated contribution was inaccurate due to the low accuracy at the low frequency and the permutation problem, in which the relationship between the calculated contribution and the actual contributions is not retained with changes in frequency. Next, the reference signal was changed to the other noise having lower correlation to the target for improvement of the accuracy and the permutation solution was considered by using the time trend correlation of the calculated contributions of dominant independent component. The modified ICA technique with the permutation solution could estimate more accurate contributions than the original condition.

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Blind Source Separation: A Tool for Rotating Machine Monitoring by Vibrations Analysis?

Cited by 94 publications

References 19 publications

Bearing Fault Transient Components Based on Absolute Autocorrelation Detection and Feature Expression Research

Bearing Fault Transient Components Based on Absolute Autocorrelation Detection and Feature Expression Research

Fault Feature Extraction of Diesel Engine Based on Bispectrum Image Fractal Dimension

Road & wind noise contribution separation using only interior noise having multiple sound sources -Accuracy improvement and permutation solution-

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