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Relevant bibliographies by topics / Mechanical diagnosis

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Journal articles

Academic literature on the topic 'Mechanical diagnosis'

Author: Grafiati

Published: 6 June 2023

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Journal articles on the topic "Mechanical diagnosis"

1

Remmerbach, Torsten W., Falk Wottawah, Julia Dietrich, Bryan Lincoln, Christian Wittekind, and Jochen Guck. "Oral Cancer Diagnosis by Mechanical Phenotyping." Cancer Research 69, no. 5 (2009): 1728–32. http://dx.doi.org/10.1158/0008-5472.can-08-4073.

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2

Donelson, Ronald. "Mechanical Diagnosis and Therapy for Radiculopathy." Physical Medicine and Rehabilitation Clinics of North America 22, no. 1 (2011): 75–89. http://dx.doi.org/10.1016/j.pmr.2010.11.001.

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3

Zhu, Hong. "Machine-Learning-Based Mechanical Fault Diagnosis Method." Advanced Materials Research 1044-1045 (October 2014): 798–800. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.798.

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With the development of science and technology, the theoretical content of mechanical fault diagnosis technology has been initially improved and established a scientific research system. Combining the mechanical diagnostic techniques with the current advanced science and technology, a variety of mechanical fault diagnosis methods have been researched and developed. Mechanical fault diagnosis evolved from empirical diagnosis to mechanical diagnosis and then to the current intelligent learning diagnosis. Now mechanical fault diagnosis collects mechanical failure data precisely mainly by a variet

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4

Jing, Hong Lei, Jing Nie, and Nian Zhang. "Augmented-Reality-Based Mechanical Fault Diagnosis Method." Advanced Materials Research 1044-1045 (October 2014): 720–22. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.720.

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With the rapid development of modern society, the industrial mechanized production reached unprecedented climax in this era. Science and technology advance increasingly, modern equipment from structure to function tends to be complex and improved, and gradually achieve a high degree of automation. However, due to the inevitable factors such as wear and tear, abrasion and chemicals infection, machinery equipment will inevitably appear unforeseen fault, causing the machine to detract from the performance, or even causing serious economic losses. Therefore, mechanical fault diagnosis can reduce e

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5

Yu, Guangwei, Xianzhi Wang, and Chunlin Da. "Compound mechanical fault diagnosis based on CMDE." Advances in Mechanical Engineering 14, no. 2 (2022): 168781322210805. http://dx.doi.org/10.1177/16878132221080560.

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The fault diagnosis technique is of important for the safety operation of the rotating machinery. In the fault diagnosis framework, the entropy-based method is a promising tool for the feature extraction and signal processing. Among the entropy-based methods, the diversity entropy has arisen increasing attention due to its merits of high consistency, strong robustness, and high calculation efficiency. However, it suffers the defect that the multiscale procedure leads to unstable complexity estimation at higher scales. This induces a poor cluster performance in analyzing the compound mechanical

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6

Xu, Gang. "MECHANICAL FAILURE DIAGNOSIS IN UNSTEADY OPERATING CONDITIONS." Chinese Journal of Mechanical Engineering 37, no. 12 (2001): 104. http://dx.doi.org/10.3901/jme.2001.12.104.

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7

Wu, Wen Bing, Shu Qun Yang, and Yi Jian Huang. "Application of Bipectrum in Mechanical Fault Diagnosis." Advanced Materials Research 538-541 (June 2012): 2645–48. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.2645.

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Bispectrum produced by third order cumulant contains the asymmetric and nonlinear information of signal, which can be used to describe the nonlinear phase coupling, especially the quadratic phase coupling, has been widely applied in fault diagnosis. The features of fault signals and normal signals are fetched by 2-D wavelet in different directions, then these features are used to diagnose fault. The experiment shows that the method can achieve satisfactory result.

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8

CHEN, Yushu. "Nonlinear dynamical principle of mechanical fault diagnosis." Chinese Journal of Mechanical Engineering 43, no. 01 (2007): 25. http://dx.doi.org/10.3901/jme.2007.01.025.

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9

Gelman, L., S. Gorpinich, and C. Thompson. "Adaptive diagnosis of the bilinear mechanical systems." Mechanical Systems and Signal Processing 23, no. 5 (2009): 1548–53. http://dx.doi.org/10.1016/j.ymssp.2009.01.007.

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10

Remmerbach, T. W., J. Guck, and J. Käs. "O33. Oral cancer diagnosis by mechanical phenotyping." Oral Oncology 47 (July 2011): S39. http://dx.doi.org/10.1016/j.oraloncology.2011.06.144.

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