Application of Wavelet Scattering and Machine Learning on Structural Health Diagnosis for Quadcopter

The aim of this study was to examine the health diagnosis classification method of quadcopter structures with different mixed faults. The loosening of the motor mount, damage to the propeller, and the loosening of the arm mount were the main fault conditions investigated. Data were first acquired un...

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Autores principales:	Wei-Hsiang Lai, Sung-Ting Tsai, De-Li Cheng, Yih-Rong Liang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	UAS quadcopter fault detection and classification data analysis wavelet scattering machine learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/3ea8cbd1e5e24179958a428ddb2d4350
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spelling	oai:doaj.org-article:3ea8cbd1e5e24179958a428ddb2d43502021-11-11T15:19:44ZApplication of Wavelet Scattering and Machine Learning on Structural Health Diagnosis for Quadcopter10.3390/app1121102972076-3417https://doaj.org/article/3ea8cbd1e5e24179958a428ddb2d43502021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10297https://doaj.org/toc/2076-3417The aim of this study was to examine the health diagnosis classification method of quadcopter structures with different mixed faults. The loosening of the motor mount, damage to the propeller, and the loosening of the arm mount were the main fault conditions investigated. Data were first acquired under non-fault conditions and the conditions of the three types of fault. Then, the features of the vibration and pulse width modulation signals were extracted by root mean square, standard deviation, and sample entropy. Moreover, the features of the audio signal were extracted by wavelet scattering, which contains time-frequency domain information that provides significant power for classification. In this paper, we propose a simple machine learning method, based on the k-Nearest Neighbor (kNN), not only for classification but also demonstrating the efficacy of the features. To test the limits of accuracy, different configurations of kNN parameters are deployed, in addition to the features. In summary, as a result of the highly efficacious features, despite mixed fault conditions, the accuracy reached 90.73%. This method improves the accuracy of mixed faults’ classification and maintains a certain level of classification effectiveness.Wei-Hsiang LaiSung-Ting TsaiDe-Li ChengYih-Rong LiangMDPI AGarticleUASquadcopterfault detection and classificationdata analysiswavelet scatteringmachine learningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10297, p 10297 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	UAS quadcopter fault detection and classification data analysis wavelet scattering machine learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	UAS quadcopter fault detection and classification data analysis wavelet scattering machine learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Wei-Hsiang Lai Sung-Ting Tsai De-Li Cheng Yih-Rong Liang Application of Wavelet Scattering and Machine Learning on Structural Health Diagnosis for Quadcopter
description	The aim of this study was to examine the health diagnosis classification method of quadcopter structures with different mixed faults. The loosening of the motor mount, damage to the propeller, and the loosening of the arm mount were the main fault conditions investigated. Data were first acquired under non-fault conditions and the conditions of the three types of fault. Then, the features of the vibration and pulse width modulation signals were extracted by root mean square, standard deviation, and sample entropy. Moreover, the features of the audio signal were extracted by wavelet scattering, which contains time-frequency domain information that provides significant power for classification. In this paper, we propose a simple machine learning method, based on the k-Nearest Neighbor (kNN), not only for classification but also demonstrating the efficacy of the features. To test the limits of accuracy, different configurations of kNN parameters are deployed, in addition to the features. In summary, as a result of the highly efficacious features, despite mixed fault conditions, the accuracy reached 90.73%. This method improves the accuracy of mixed faults’ classification and maintains a certain level of classification effectiveness.
format	article
author	Wei-Hsiang Lai Sung-Ting Tsai De-Li Cheng Yih-Rong Liang
author_facet	Wei-Hsiang Lai Sung-Ting Tsai De-Li Cheng Yih-Rong Liang
author_sort	Wei-Hsiang Lai
title	Application of Wavelet Scattering and Machine Learning on Structural Health Diagnosis for Quadcopter
title_short	Application of Wavelet Scattering and Machine Learning on Structural Health Diagnosis for Quadcopter
title_full	Application of Wavelet Scattering and Machine Learning on Structural Health Diagnosis for Quadcopter
title_fullStr	Application of Wavelet Scattering and Machine Learning on Structural Health Diagnosis for Quadcopter
title_full_unstemmed	Application of Wavelet Scattering and Machine Learning on Structural Health Diagnosis for Quadcopter
title_sort	application of wavelet scattering and machine learning on structural health diagnosis for quadcopter
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3ea8cbd1e5e24179958a428ddb2d4350
work_keys_str_mv	AT weihsianglai applicationofwaveletscatteringandmachinelearningonstructuralhealthdiagnosisforquadcopter AT sungtingtsai applicationofwaveletscatteringandmachinelearningonstructuralhealthdiagnosisforquadcopter AT delicheng applicationofwaveletscatteringandmachinelearningonstructuralhealthdiagnosisforquadcopter AT yihrongliang applicationofwaveletscatteringandmachinelearningonstructuralhealthdiagnosisforquadcopter
_version_	1718435361414184960

Application of Wavelet Scattering and Machine Learning on Structural Health Diagnosis for Quadcopter

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