Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock

Zhao, Xuezhi; Bang-yan, YE

doi:10.1016/j.ymssp.2011.01.003

Cited by 166 publications

(102 citation statements)

References 16 publications

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“…NIS analysis is accomplished by utilizing Singular Value Decomposition (SVD) which has been used as a powerful tool in diverse fields that includes SVDbased signal processing for selection of the number of effective component signals (6) . SVD also performs well towards determining the best locations for installation of Power System 3 Stabilizers (7) , and as a possible application of Generalized SVD in machine condition monitoring (8) .…”

Section: Introductionmentioning

confidence: 99%

Technique for optimal placement of transducers for fault detection in rotating machines

Fatima

Dastidar

Mohanty

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part O:

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Online fault detection and diagnosis of rotating machinery requires a number of transducers which can be significantly expensive for industrial processes. The sensitivity of various transducers and their appropriate positioning are dependent on different types of fault conditions. It is critical to formulate a method to systematically determine the effectiveness of transducer locations for monitoring the condition of a machine. In this paper, Number of Independent Sources analysis is used as an effective tool for reducing the number of vibration sources within the system which is then followed by Principal Component Analysis to identify the incoherent transducers to be employed for fault detection. This experiment is conducted on a machine fault simulator for unbalanced rotor, 1 misaligned shaft and cracked shaft. The validation of the proposed selection process is illustrated using spectral analysis for each defect.

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Section: Introductionmentioning

confidence: 99%

Technique for optimal placement of transducers for fault detection in rotating machines

Fatima

Dastidar

Mohanty

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part O:

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show abstract

“…Supposing that the number of effective singular values is r, if the spectrum curve is convex at the s point, we will select the first r effective singular value. Otherwise, we will select the first r´1 effective singular value [20]. Similarly, the concavity and convexity of the curve at the curvature peak should be considered while determining the effective singular values based on the change of differential curvature peaks; the strategy is described above.…”

Section: Improved Methods Based On the Curvature Spectrum Of Incrementmentioning

confidence: 99%

“…This peak can be used to reflect the changing states of the incremental singular entropy sequence. Because the incremental singular entropy sequence is discrete, the difference was used to approximate the derivative, which is defined in Equations (11) and (12) [20].…”

Section: Curvature Spectrum Of Incremental Singular Entropymentioning

confidence: 99%

“…However, the variation of the curve becomes discontinuous after adding the noise to the pure signal, which influences the results, while choosing an improper type of difference operator [20]. Thus, the fraction value is inversely proportional to that of the denominator, which is composed of the square of first-order difference values.…”

Section: Curvature Spectrum Of Incremental Singular Entropymentioning

confidence: 99%

“…Recently, different methods have been proposed to capture this turning point. Zhao et al [20] put forward a concept of the difference spectrum that can describe the sudden change in status of singular values of a complicated signal. Using the difference spectrum by tracing the maximum peak, the hidden modulation feature caused by gear vibration in the head stock is isolated from a turning force signal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Gao

Wang

et al. 2016

Applied Sciences

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Singular value decomposition (SVD) is a widely used and powerful tool for signal extraction under noise. Noise attenuation relies on the selection of the effective singular value because these values are significant features of the useful signal. Traditional methods of selecting effective singular values (or selecting the useful components to rebuild the faulty signal) consist of seeking the maximum peak of the differential spectrum of singular values. However, owing to the small number of selected effective singular values, these methods lead to excessive de-noised effects. In order to get a more appropriate number of effective singular values, which preserves the components of the original signal as much as possible, this paper used a difference curvature spectrum of incremental singular entropy to determine the number of effective singular values. Then the position was found where the difference of two peaks in the spectrum declines in an infinitely large degree for the first time, and this position was regarded as the boundary of singular values between noise and a useful signal. The experimental results showed that the modified methods could accurately extract the non-stationary bearing faulty signal under real background noise.

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Noise reduction algorithm of GPR data based on MMSE‐PDS

Ma,

Fan,

Zhang

et al. 2023

Near Surface Geophysics

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Ground penetrating radar (GPR) technology is widely used in tunnel engineering detection. however, various factors, such as environmental interference and low signal‐to‐noise ratio characteristics of the echo data, limit the detection accuracy. A noise and interference suppression algorithm based on improved singular value decomposition is proposed in this paper. Compared with traditional filtering methods, the proposed method has the advantages of thorough denoising, no clutter, efficient improvement of profile resolution, and less dependence on parameters. The main features of the proposed algorithm are as follows: (1) Given the global characteristics of the noise disturbance on the signal space, the minimum mean square error (MMSE) estimation is employed to approximate the effective signal, introducing the correction factor to suppress the larger singular value from the noise output in the reconstructing process of the effective signal subspace, and to eliminate the strong direct wave interference to avoid producing false signals. (2) A positive difference sequence search algorithm (PDS) based on rank order variance, as well as the method of selecting correction factors are proposed to improve the processing accuracy. In order to verify the design, the tunnel lining simulation model and the actual tunnel lining detection data are used. The results show good performance for noise and interference suppression, providing technical support for improving GPR data quality and tunnel detection accuracy.This article is protected by copyright. All rights reserved

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Selection of effective singular values using difference spectrum and its application to fault diagnosis of headstock

Cited by 166 publications

References 16 publications

Technique for optimal placement of transducers for fault detection in rotating machines

Technique for optimal placement of transducers for fault detection in rotating machines

Development of a Method for Selection of Effective Singular Values in Bearing Fault Signal De-Noising

Noise reduction algorithm of GPR data based on MMSE‐PDS

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