This paper focuses on an analysis of the stress, the pressure and the fatigue as part of the Dirty Dozen and Human Factors procedures. An online international survey has been carried out to ascertain the professional levels of the fatigue, the stress and their pressure exposure. This work is a contribution to the aeronautical safety in order to alert authorities about the stress, the pressure and the fatigue that aircraft maintenance personnel suffers. Safety is the main driver in aviation related professions. Maintenance related personnel is constantly subjected to several external circumstances that might originate errors in the performance or evaluation in maintenance related tasks. Authorities have clearly regulated flight crew and air traffic controllers working and resting periods, but maintenance personnel regulations do not reflect the same procedures. The Aeronautical industry 4.0 with the upcoming digital transformation will increase the safety margin and it will reduce the aircraft maintenance ground time.
In the aviation sector, human factors are the primary cause of safety incidents. Intelligent prediction systems, which are capable of evaluating human state and managing risk, have been developed over the years to identify and prevent human factors. However, the lack of large useful labelled data has often been a drawback to the development of these systems. This study presents a methodology to identify and classify human factor categories from aviation incident reports. For feature extraction, a text pre-processing and Natural Language Processing (NLP) pipeline is developed. For data modelling, semi-supervised Label Spreading (LS) and supervised Support Vector Machine (SVM) techniques are considered. Random search and Bayesian optimization methods are applied for hyper-parameter analysis and the improvement of model performance, as measured by the Micro F1 score. The best predictive models achieved a Micro F1 score of 0.900, 0.779, and 0.875, for each level of the taxonomic framework, respectively. The results of the proposed method indicate that favourable predicting performances can be achieved for the classification of human factors based on text data. Notwithstanding, a larger data set would be recommended in future research.
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