Blind source separation of noisy harmonic signals for rotating machine diagnosis

Servière, Christine; Fabry, P.

doi:10.1016/s0022-460x(03)00774-0

Cited by 42 publications

(32 citation statements)

References 22 publications

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“…They also enjoyed various applications in structural dynamics, including damage detection [5], condition monitoring [6,7] and discrimination between pure tones and sharp-pointed resonances [8]. A special issue dealing with BSS was published in Mechanical Systems and Signal Processing in 2005 [9].…”

Section: Introductionmentioning

confidence: 99%

Output-only modal analysis using blind source separation techniques

Poncelet

Kerschen

Golinval

et al. 2007

Mechanical Systems and Signal Processing

220

112

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Section: Introductionmentioning

confidence: 99%

Output-only modal analysis using blind source separation techniques

Poncelet

Kerschen

Golinval

et al. 2007

Mechanical Systems and Signal Processing

220

112

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“…These methods are very popular for communications or audio processing applications [1]. BSS [2,3] is also very attractive for diagnosis of mechanical systems or rotating machine monitoring [4][5][6][7][8][9][10]. Indeed, useful signals are generally corrupted with signals coming from other mechanical systems and ambient noise.…”

Section: Introductionmentioning

confidence: 99%

“…In [9], the authors proposed a robust-to-noise technique of PCA, available for spatially correlated noises. No assumption is made in this paper regarding the statistical property of the noise component.…”

Section: Introductionmentioning

confidence: 99%

“…This lack of knowledge on noises is compensated by a model of sinusoidal sources. The proposed PCA in [9] exploits spectral matrices of delayed observations and introduces new estimators of the signal subspace and the whitening matrix. This paper extends [9] and shows that the signal subspace can still be estimated with other model of sources.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed PCA in [9] exploits spectral matrices of delayed observations and introduces new estimators of the signal subspace and the whitening matrix. This paper extends [9] and shows that the signal subspace can still be estimated with other model of sources. One interesting case in rotating machine vibrations concerns the case where the instantaneous frequency of one source is not constant but sinusoidal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Principal component analysis and blind source separation of modulated sources for electro-mechanical systems diagnostic

Servière

Fabry

2005

Mechanical Systems and Signal Processing

Self Cite

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A decentralized blind source separation algorithm for ambient modal identification in the presence of narrowband disturbances

Sadhu

Narasimhan

2013

Struct. Control Health Monit.

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In this paper, a new decentralized identification method based on the principles of blind source separation (BSS) that can perform modal identification in the presence of narrowband external excitations is presented. This algorithm is based on the principle of sparse BSS involving wavelet transforms to undertake identification for underdetermined static mixtures. Most BSS methods in the context of modal identification assume that the excitation is white and does not contain narrow band excitation frequencies. However, this assumption is not satisfied in many situations when the excitation is a superposition of narrowband harmonic(s) and broadband disturbance, for example, rotating machinery fixed to flexible structures, human-induced vibration in pedestrian bridges, or dynamic interaction with surrounding structures. Under such conditions, traditional BSS methods (such as second-order blind identification) yield sources (modes) without any indication as to whether the identified source(s) is a system or an excitation harmonic. In the proposed method, underdetermined BSS is employed, involving transformation of measurements to the time-frequency domain, resulting in a sparse representation. The statistical characteristics of the sources are estimated using residual bootstrap technique, which are then used to delineate the sources corresponding to external disturbances versus intrinsic modes of the system, in a decentralized fashion. The decentralized architecture is aimed at reducing the cost of instrumentation in both traditional wired and recent wireless applications. The performance of the algorithm is demonstrated using both a numerical study and a bench-scale laboratory experiment. The primary advantage of the proposed method is its ability to identify the modal harmonics of a flexible structure under narrowband excitations, in a decentralized fashion, where traditional BSS algorithms fail to achieve the same goals.

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Blind source separation of noisy harmonic signals for rotating machine diagnosis

Cited by 42 publications

References 22 publications

Output-only modal analysis using blind source separation techniques

Output-only modal analysis using blind source separation techniques

Principal component analysis and blind source separation of modulated sources for electro-mechanical systems diagnostic

A decentralized blind source separation algorithm for ambient modal identification in the presence of narrowband disturbances

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