Application of AI for Predicting Test Cycles of Drivetrain Component
- 提供方法
- 本サイト上にてダウンロード・閲覧可
- 形態
- 価格
- 一般価格(税込)：￥1,100 会員価格(税込)：￥880
- 文献番号
- 20229014
- 文献・情報種別
- SETC
No.2022-32-0014
- 掲載ページ
- 1-7(Total 7 p)
- 発行年月
- 2022年 10月
- 出版社
- (公社)自動車技術会
- 言語
- 英語
- イベント
- SETC2022
書誌事項
| 著者（英） | 1) Gaurav Sirsaj, 2) Vinay Kharche |
|---|---|
| 勤務先（英） | 1) Dana India Technical Center, 2) Dana India Technical Center |
| 抄録（英） | Industries are currently going through “The Fourth Industrial Revolution,” as professionals have called it “Industry 4.0” (I4.0). Integration of physical and digital systems for the product life cycle mainly concerns Industry 4.0. With the appearance of I4.0, the concept of prediction management has become an unavoidable tendency in the framework of big data and smart manufacturing. At the same time, it offers a reliable solution for handling test fatigue failures. AI and its key technologies play an essential role - 1. to make industrial systems autonomous like predicting test failures 2. to make possible the automatized data collection from industrial machines/components. Based on these collected data types, machine learning algorithms can be applied for automated failure detection and diagnosis. However, it is a bit difficult to select appropriate machine learning (ML) techniques, type of data, data size, and equipment to apply ML in industrial systems. Selection of inappropriate technique, dataset, and data size may cause time loss and infeasible result prediction. Therefore, this study aims to present a comprehensive case study of predicting the testing failure using ML techniques. This work presents a novel approach for different parameterbased fatigue failure (rig testing failure) characterization using artificial intelligence (AI). The deep learning algorithm is trained on carefully collected physical testing data (historical data), which helps in predicting the new product development testing failure cycles based on basic design parameters available at the start of the program such as loading, component dimensions, distances, and inclination angle, etc. Rig testing reveals the testing cycles which indicate either failure or non-failure of the component (depending upon the passing criteria). Thus, every driveline component subjected to this research work generates at least one data set (testing values from AI). Based on this study, a conservative failure prediction accuracy of 88% is achieved. So, this methodology is pioneering to predict fatigue failure without - 1. comprehensive expensive physical testing. 2. the need for extensive, error-prone, use of complex assessment methodologies With expert knowledge of evaluation procedures, the developed AI approach enables quick and reliable prediction of fatigue failure of components based on elementary key design parameters which can reduce the overall design cycle time. 翻訳 |
