Blind source separation: A new pre-processing tool for rotating machines monitoring?

Gellé, Guillaume; Colas, Maxime; Servière, Christine

doi:10.1109/tim.2003.814356

Cited by 38 publications

(23 citation statements)

References 8 publications

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“…(1) is a linear mixture [4]. In many fields such as telecommunication, the mixture is commonly assumed to be linear which usually include instantaneous mixture and convolutive mixture.…”

Section: A Time-frequency Modelmentioning

confidence: 99%

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Blind Source Separation for Forecast of Solar Irradiance

Chen

Zhang

2012

2012 Second International Conference on Intelligent System Design and Engineering Application

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The application of blind source separate (BSS) for forecasting the solar irradiance is presented. First, we used BSS method to separate the initial time sequence, and then we designed the best neural network topology. In consideration of the complex behavior of solar irradiance, either periodic or random, a kind of dynamic neural network, RBFN, was used for such case. After that the separating results were supplied to the input layer and were trained through adjusting the number of neurons in different layers and the weights and biases of the network. until the errors reached the stop conditions. Finally the forecasting model mentioned in this paper was tested through a practical sample, which indicates that the accuracy of the model is more satisfactory than without blind source separation. Thus the method proposed in this paper could also be applicable to other relating fields.

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“…(1) is a linear mixture [4]. In many fields such as telecommunication, the mixture is commonly assumed to be linear which usually include instantaneous mixture and convolutive mixture.…”

Section: A Time-frequency Modelmentioning

confidence: 99%

“…Blind source separation (BSS) as a new pre-processing tool for rotating machines monitoring was pointed out as in [4]. In the large number of cases, the signal received by a sensor is the mixture of all sources and the source signals as well as mixtures are unknown generally.…”

Section: Introductionmentioning

confidence: 99%

Blind Source Separation for Forecast of Solar Irradiance

Chen

Zhang

2012

2012 Second International Conference on Intelligent System Design and Engineering Application

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“…The nonlinear function we used is (7). Sources were defined as three periodic and deterministic signals and a random signal, see Fig.1.…”

Section: Simulation Signals Separationmentioning

confidence: 99%

“…The vibration acquired from cabinet could be modelled as the convolution of fault sources, exterior disturbances, mode vibration components and measurement noises [6]. Some researchers had carried out the application of ICA on rotation machinery via convolution model [7][8]. G.Gelle recovered the vibration information issued from a single rotating machine working in a noisy environment with the decorrelation approach via convolution model [8].…”

Section: Introductionmentioning

confidence: 99%

Vibration Sources Identification with Independent Component Analysis

Yang

et al. 2006

2006 6th World Congress on Intelligent Control and Automation

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Independent Component Analysis (ICA) could recover the original sources in multichannel observations when the original sources and the mixing process are not known. We applied this technique to linear mixtures of simulated fault signals. The success separation inspired us to apply the technique to an experiment set-up where three known sources were used as an analogue of the real world in order to separate each source. Our goal was to remove the influence of the other machines and recur the vibration source in gearbox. The mixing model of signal transmission was investigated. The experiment results show that when the signal frequency is less than 100hz, it is appropriate to consider the mixing model of vibration sources as instantaneous model. The separation results by mean of FastICA algorithm show that we can separate the disturbing signal and inner source signals from the observation. The experiment results suggest that ICA is a promising method to enhance the accuracy of fault diagnosis.

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“…Gelle et al [15] have applied the BSS technique to machine fault diagnosis, using BSS as a preprocessing step for the detection and diagnosis of faults in rotating machinery. Gelle's paper compares temporal and frequency based approaches to the solution of the BSS problem for rotating machine signals.…”

Section: Introductionmentioning

confidence: 99%

Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis

2010

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Air-borne acoustic based condition monitoring is a promising technique because of its intrusive nature and the rich information contained within the acoustic signals including all sources. However, the back ground noise contamination, interferences and the number of Internal Combustion Engine ICE vibro-acoustic sources preclude the extraction of condition information using this technique. Therefore, lower energy events; such as fuel injection, are buried within higher energy events and/or corrupted by background noise.This work firstly investigates diesel engine air-borne acoustic signals characteristics and the benefits of joint time-frequency domain analysis. Secondly, the air-borne acoustic signals in the vicinity of injector head were recorded using three microphones around the fuel injector (120° apart from each other) and an Independent Component Analysis (ICA) based scheme was developed to decompose these acoustic signals. The fuel injection process characteristics were thus revealed in the timefrequency domain using Wigner-Ville distribution (WVD) technique. Consequently the energy levels around the injection process period between 11 and 5 degrees before the top dead center and of frequency band 9 to 15 kHz are calculated. The developed technique was validated by simulated signals and empirical measurements at different injection pressure levels from 250 to 210 bars in steps of 10 bars. The recovered energy levels in the tested conditions were found to be affected by the injector pressure settings.

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Blind source separation: A new pre-processing tool for rotating machines monitoring?

Cited by 38 publications

References 8 publications

Blind Source Separation for Forecast of Solar Irradiance

Blind Source Separation for Forecast of Solar Irradiance

Vibration Sources Identification with Independent Component Analysis

Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis

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