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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 variety of sensors, uses a variety of fault diagnosis model to conduct diversified and intelligent diagnosis.

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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 equipment accident rate and ensure the long-term stable operation of the device. And applying the augmented reality to machinery fault diagnosis method research can maximize the efficiency of mechanical fault diagnosis and equipment efficiency. This article explores the prospects for the development of mechanical fault diagnosis methods based on the theoretical basis and application value of augmented reality.

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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 fault signals. To address this issue, this paper presents a novel feature extraction method called composite multiscale diversity entropy (CMDE). The proposed CMDE utilizes the mean complexity value of multiple sliding windows for each scale to enhance the stability, which enables the diversity entropy could dig richer fault information from deeper scales for the compound fault diagnosis of rotating machinery. Then, the stability of CMDE has been evaluated using synthetic gear signals. At last, the proposed CMDE has been applied in the compound mechanical fault diagnosis. The experimental results show that the CMDE achieves the highest diagnosis accuracy compared to the existing entropy-based feature extraction methods.

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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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