Artificial Neural Network for Vibration Frequency Measurement Using Kinect V2

Joint Authors

Yang, Xiaoxiang
Liu, Jiantao

Source

Shock and Vibration

Issue

Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-16, 16 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2019-03-12

Country of Publication

Egypt

No. of Pages

16

Main Subjects

Civil Engineering

Abstract EN

Optical measurement can substantially reduce the required amount of labor and simplify the measurement process.

Furthermore, the optical measurement method can provide full-field measurement results of the target object without affecting the physical properties of the measurement target, such as stiffness, mass, or damping.

The advent of consumer grade depth cameras, such as the Microsoft Kinect, Intel RealSence, and ASUS Xtion, has attracted significant research attention owing to their availability and robustness in sampling depth information.

This paper presents an effective method employing the Kinect sensor V2 and an artificial neural network for vibration frequency measurement.

Experiments were conducted to verify the performance of the proposed method.

The proposed method can provide good frequency prediction within acceptable accuracy compared to an industrial vibrometer, with the advantages of contactless process and easy pipeline implementation.

American Psychological Association (APA)

Liu, Jiantao& Yang, Xiaoxiang. 2019. Artificial Neural Network for Vibration Frequency Measurement Using Kinect V2. Shock and Vibration،Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1211665

Modern Language Association (MLA)

Liu, Jiantao& Yang, Xiaoxiang. Artificial Neural Network for Vibration Frequency Measurement Using Kinect V2. Shock and Vibration No. 2019 (2019), pp.1-16.
https://search.emarefa.net/detail/BIM-1211665

American Medical Association (AMA)

Liu, Jiantao& Yang, Xiaoxiang. Artificial Neural Network for Vibration Frequency Measurement Using Kinect V2. Shock and Vibration. 2019. Vol. 2019, no. 2019, pp.1-16.
https://search.emarefa.net/detail/BIM-1211665

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1211665