Detection of car abnormal vibration using machine learning

被引：2

作者：

Hashimoto, Wataru ^{[1
]}

Hirota, Masaharu ^{[2
]}

Araki, Tetsuya ^{[3
]}

Yamamoto, Yukio ^{[4
]}

Egi, Masashi ^{[1
]}

Hirate, Morihiro ^{[5
]}

Maura, Masao ^{[5
]}

Ishikawa, Hiroshi ^{[1
]}

机构：

[1] Tokyo Metropolitan Univ, Grad Sch Syst Design, Hino, Tokyo, Japan

[2] Okayama Univ Sci, Dept Integrated Informat, Okayama, Okayama, Japan

[3] Gunma Univ, Grad Sch Sci & Technol, Kiryu, Gunma, Japan

[4] Japan Aerosp Explorat Agcy, Sagamihara, Kanagawa, Japan

[5] AISIN AW CO LTD, Connected Solut Dept, Vehicle Informat Technol Div, Okazaki, Aichi, Japan

来源：

2019 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2019) | 2019年

关键词：

Vibration analysis; Supervised learning; Abnormal detection;

D O I：

10.1109/ISM46123.2019.00015

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

One difficulty of car-sharing services is trouble among users related to damage of the automobile. With the rapid growth of car-sharing services, demand exists for a new approach for automatic detection of the occurrence of damage during crewless operation. In this study, we propose a method for automatic detection of abnormal vibration that damages an automobile that occurs driving and stopping. After using multiple piezoelectric sensors installed in the automobile to acquire vibration, we applied supervised learning to extract features from vibrations and to detect abnormal vibrations. The proposed method achieved recall of 0.96 for stopped automobiles and recall of 0.82 for driving automobiles.

引用

页码：40 / 47

页数：8

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