Discovering Depressurization Events in Service Difficulty Reports using Machine Learning

Abstract

Service Difficulty Reports (SDRs) are reports submitted by aircraft operators and certified repair stations after they discover or experience a failure, malfunction, or defect while operating, or performing maintenance on an aircraft. The SDR records are rich in information pertaining to aviation safety. However, most of that data is not easily accessible as the problems are described in free form text. The text records often describe critical safety events such as depressurization, onboard fire, and runway excursion. Extracting critical information like the safety events in millions of records is labor intensive and infeasible without automated methods. In this study, we describe a machine learning approach to automatically discover depressurization safety events in SDR records. We are able to achieve the F1 score up to 95% to discover the depressurization events.