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The main objective of this study was to analyze anomalies voluntarily reported by pilots in civil aviation sector and identify factors leading to such anomalies. Experimental data were obtained from the NASA aviation safety reporting system (ASRS) database. These data contained a range of text records spanning 30 years of civilian aviation, both commercial (airline operations) and general aviation (private aircraft). Narrative data as well as categorical data were used. The associations between incident contributing factors and self-reported anomalies were investigated using data mining and correspondence analysis. The results revealed that a broadly defined human factors category and weather conditions were the main contributors to self-reported civil aviation anomalies. New associations between identified factors and reported anomaly conditions were also reported.

A study investigating what factors are present leading to pilots submitting voluntary anomaly reports regarding their flight performance was conducted. Diffusion Maps (DM) were selected as the method of choice for performing dimensionality reduction on text records for this study. Diffusion Maps have seen successful use in other domains such as image classification and pattern recognition. High-dimensionality data in the form of narrative text reports from the NASA Aviation Safety Reporting System (ASRS) were clustered and categorized by way of dimensionality reduction. Supervised analyses were performed to create a baseline document clustering system. Dimensionality reduction techniques identified concepts or keywords within records, and allowed the creation of a framework for an unsupervised document classification system. Results from the unsupervised clustering algorithm performed similarly to the supervised methods outlined in the study. The dimensionality reduction was performed on 100 of the most commonly occurring words within 126,000 text records describing commercial aviation incidents. This study demonstrates that unsupervised machine clustering and organization of incident reports is possible based on unbiased inputs. Findings from this study reinforced traditional views on what factors contribute to civil aviation anomalies, however, new associations between previously unrelated factors and conditions were also found.

This chapter presents an overview of key Human Systems Integration (HSI), Human Factors (HF), and Knowledge Management (KM) methods that support building user-centered systems. The chapter stresses that KM can benefit the systems design process by reducing rework and duplication of effort. In addition, tools aiding KM implementation within the HSI and Human Factors (HF) domains are discussed. HSI practices created and employed within the discipline of Systems Engineering (SE) have brought positive changes to the systems development lifecycle (SDLC) process, affording increasingly complex and smarter systems to be built. These increases in systems complexity have created a need for systems designers and program managers to apply KM principles to systematically create, share, retain, and transfer workforce skills, facts, processes, capabilities, and experiences in a systematic fashion. The authors describe the importance and benefits of integrating HSI and KM practices to build better and smarter systems.

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