Cost-Safety-Aware Inspection Strategy for Truck Fleets with Limited Historical Data

 Heavy-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.