자료유형  

 학위논문

Control Number  

0017161457

International Standard Book Number  

9798382372341

Dewey Decimal Classification Number  

004

Main Entry-Personal Name  

Shi, Ying.

Publication, Distribution, etc. (Imprint  

[S.l.] : Carnegie Mellon University., 2024

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2024

Physical Description  

143 p.

General Note  

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

General Note  

Advisor: Tang, Pingbo.

Dissertation Note  

Thesis (Ph.D.)--Carnegie Mellon University, 2024.

Summary, Etc.  

요약Heavy-duty trucks are a significant segment of the population involved in fatal accidents. In order to prevent crashes caused by vehicle malfunctions, effective inspection planning is essential. This research aims to develop a reliable predictive inspection planning strategy that can predict the future condition of the vehicle and to develop an inspection plan that can identify risky components with a few inspection costs and time. However, the limited historical data decrease the reliability of predictive inspection planning. To address this issue, this research developed data augmentation methods to generate synthetic data to fill in the limited historical data. The research also explores the potential to integrate humans and machines for more reliable inspection planning by handling data limitations from another perspective. This research addresses the challenges that: (1) the data augmentation is required to generate synthetic data similar to data in the real world, (2) the need for a comprehensive summary of characteristics of humans and machines in inspection planning, especially their advantages and limitations; (3) the need for integrating human knowledge into machine learning models with utilizing their advantages and avoiding limitations. To address the challenges, the researcher: (1) proposed a data augmentation method adapted to brake inspection planning considering the brake deterioration mechanism; (2) compared human and machine performance in inspection planning and summarized the scenarios in which human or machine perform better; (3) obtained a comprehensive summary of the advantages and limitations of human and machine inspection planning; and (4) This research proposed a framework for a human-machine collaboration mode that combines human and machine advantages and avoids their limitations. The outcomes of the research are expected to provide commercial fleets with reliable interpretation-based inspection suggestions, which can ensure vehicle safety with minimum inspection resources.

Subject Added Entry-Topical Term  

Computer science.

Subject Added Entry-Topical Term  

Environmental engineering.

Index Term-Uncontrolled  

Data imputation

Index Term-Uncontrolled  

Data sparsity

Index Term-Uncontrolled  

Human knowledge

Index Term-Uncontrolled  

Human-machine collaboration

Index Term-Uncontrolled  

Predictive inspection

Added Entry-Corporate Name  

Carnegie Mellon University Civil and Environmental Engineering

Host Item Entry  

Dissertations Abstracts International. 85-11B.

Electronic Location and Access  

로그인을 한후 보실 수 있는 자료입니다.

Control Number  

joongbu:658021