Learning from Incidents in Aircraft Maintenance and Continuing Airworthiness Management: A Systematic Review

Abstract

The purpose of this systematic review is to highlight the salient elements of learning from incidents in the aircraft maintenance and continuing airworthiness management area. This involved the review of more than 1,000 publications reflecting practice in different domains. The cache was eventually distilled to 18 publications of relevance to learning from incidents. The systematic review of the literature was not intended to be exhaustive, but it was deliberately bound by the parameters of predefined search terms. A robust analysis was performed on the 18 distilled publications with the use of the NVivo software. A critical and systematic examination of this body of literature further supported the development of the five codification themes. The analysis of the literature revealed the benefits of a just culture as an enabler of reporting and learning from incidents. Moreover, it identified limitations inherent in the current body of knowledge. The most evident being a paucity of literature relevant to the featured industry segment. Some impediments to learning from incidents are also highlighted. Central to this is the prevalence of lack of effective focus and practice on satisfactory causation of events. Currently, the efforts applied across many featured domains appear to be based upon ineffective legacy linear practices. However, emerging investigative philosophies that look beyond direct cause and effect contain opportunities for practitioners to consider causation through dawning axioms. This systematic review could be used in the European aviation regulatory activities associated with improving learning from incident in aircraft maintenance and continuing airworthiness management.

Safety Management System and Hazards in the Aircraft Maintenance Industry: A Systematic Literature Review

“…The initial screening included an assessment of the title and abstract of all the review studies. Only one study (Clare & Kourousis, 2021c), indexed in both the Scopus and Web of Science databases, was identified as closer to the proposed review subject. The study is a systematic literature review that underlines the learning from past incidents, a reactive hazard identification method under the SMS framework.…”

Learning from Past in the Aircraft Maintenance Industry: An Empirical Evaluation in the Safety Management Framework

“…This section examines applicable aviation regulations to the aircraft maintenance industry and scholarly research articles on learning from the past concept. The systematic literature review of [23] is the baseline for distinguishing the research articles, whereas updated regulatory publications of ICAO are referred for regulations.…”

“…The purpose of mentioning the developmental background of this regulatory document is to underscore the inadequacy of 'safety data' and 'safety information' generated from past investigations. Based on the past investigation report analysis, it was observed that 'investigation' is an area that lacks objectivity and focus [23], and the regulatory framework does not include the guidelines and quality standards norms of safety recommendations [34]. The example of the Indian scheduled operator quoted in the 'Introduction' section can also be viewed with this reflection.…”

“…The two primary outcomes of the previous section are, firstly, the LPSI model specific to the aviation industry (Figure : 2) underpinned by the models of [1,18,32] and the regulatory framework [25] and secondly, the identification of the factors affecting LPSI based on [13,21,23,26,37]. Combining the two, a conceptual model depicting the effect of variables on safety communication and, eventually, on learning from the past is developed by the researchers (Fig.…”

A fuzzy logic evolution of the functional resonance analysis method (FRAM) to assess risk in ground operation

“…Bellamy’s (2015) study shows that the link between minor and major events is the hazard. These studies require to examine dozens of accident reports and thousands of occurrence reports (Pramono et al , 2020; Clare and Kourousis, 2021). It is necessary to use smart data technologies such as genetic algorithm (Das and Abdel-Aty, 2011), data mining and deep learning technics (Zhang et al , 2021) to catch information.…”