Higher order statistics for detection and classification of faulty fanbelts using acoustical analysis

Gellé, Guillaume; Colas, Maxime; Delaunay, G.

doi:10.1109/host.1997.613484

Cited by 15 publications

(7 citation statements)

References 5 publications

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“…(8) shows that the flux contains not only the fundamental part but also sidebands around the fundamental frequency. This simplified flux allows the current expression to be obtained based on the motor equivalent circuit [17]:…”

Section: Electrical Motor Current Signalmentioning

confidence: 97%

“…Higher order spectra (HOS) are useful signal processing tools that have shown [7,8] significant benefits over traditional spectral analyses because HOS have nonlinear system identification, phase information retention and Gaussian noise elimination properties. The application of HOS techniques in condition monitoring has been reported in [9,10] and it is clear that multi-dimensional HOS measures can contain more useful information than traditional two-dimensional spectral measures for diagnostic purposes.…”

Section: Introductionmentioning

confidence: 99%

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Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment

Shao

et al. 2011

Mechanical Systems and Signal Processing

125

101

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Section: Electrical Motor Current Signalmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment

Shao

et al. 2011

Mechanical Systems and Signal Processing

125

101

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“…This is conducted by computing the HOS for all pixels in the low-DOF image. The HOS mapping method is known to be well suited to solving detection and classification problems because it can suppress Gaussian noise and preserve some of the non-Gaussian information [1], [16], [18]. The fourth-order moments are calculated for all pixels in the red, green, and blue channels for the M×N input image, respectively.…”

Section: Color-based Hos Map Constructionmentioning

confidence: 99%

Fast Extraction of Objects of Interest from Images with Low Depth of Field

Kim¹,

Park²,

Lee³

et al. 2007

ETRI J

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In this paper, we propose a novel unsupervised video object extraction algorithm for individual images or image sequences with low depth of field (DOF). Low DOF is a popular photographic technique which enables the representation of the photographer's intention by giving a clear focus only on an object of interest (OOI). We first describe a fast and efficient scheme for extracting OOIs from individual low‐DOF images and then extend it to deal with image sequences with low DOF in the next part. The basic algorithm unfolds into three modules. In the first module, a higher‐order statistics map, which represents the spatial distribution of the high‐frequency components, is obtained from an input low‐DOF image. The second module locates the block‐based OOI for further processing. Using the block‐based OOI, the final OOI is obtained with pixel‐level accuracy. We also present an algorithm to extend the extraction scheme to image sequences with low DOF. The proposed system does not require any user assistance to determine the initial OOI. This is possible due to the use of low‐DOF images. The experimental results indicate that the proposed algorithm can serve as an effective tool for applications, such as 2D to 3D and photorealistic video scene generation.

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“…Thus, the global sharpness of the image can be estimated from the sparse local sharpness by an extension with the weight of the high frequency components. We suggest using higher order statistics (HOS) to extract the high frequency components from images, as they can suppress Gaussian noise and preserve nonGaussian information [42]. The fourth-order moments of the HOS are calculated for all pixels within the luminance channel I of an M × N image,…”

Section: Sharpnessmentioning

confidence: 99%

A new quality assessment and improvement system for print media

Liu

Konya

Nandzik

et al. 2012

EURASIP J. Adv. Signal Process.

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Print media collections of considerable size are held by cultural heritage organizations and will soon be subject to digitization activities. However, technical content quality management in digitization workflows strongly relies on human monitoring. This heavy human intervention is cost intensive and time consuming, which makes automization mandatory. In this article, a new automatic quality assessment and improvement system is proposed. The digitized source image and color reference target are extracted from the raw digitized images by an automatic segmentation process. The target is evaluated by a reference-based algorithm. No-reference quality metrics are applied to the source image. Experimental results are provided to illustrate the performance of the proposed system. We show that it features a good performance in the extraction as well as in the quality assessment step compared to the state-of-the-art. The impact of efficient and dedicated quality assessors on the optimization step is extensively documented.

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Higher order statistics for detection and classification of faulty fanbelts using acoustical analysis

Cited by 15 publications

References 5 publications

Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment

Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment

Fast Extraction of Objects of Interest from Images with Low Depth of Field

A new quality assessment and improvement system for print media

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