Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning

Although aviation accidents are rare, safety incidents occur more frequently and require a careful analysis to detect and mitigate risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to airline companies as well as to governing organizations such as the Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved in mining multi-dimensional heterogeneous time series data, the lack of time-step-wise annotation of events in a flight, and the lack of scalable tools to perform analysis over a large number of events. In this work, we propose a precursor mining algorithm that identifies events in the multidimensional time series that are correlated with the safety incident. Precursors are valuable to systems health and safety monitoring and in explaining and forecasting safety incidents. Current methods suffer from poor scalability to high dimensional time series data and are inefficient in capturing temporal behavior. We propose an approach by combining multiple-instance learning (MIL) and deep recurrent neural networks (DRNN) to take advantage of MIL's ability to learn using weakly supervised data and DRNN's ability to model temporal behavior. We describe the algorithm, the data, the intuition behind taking a MIL approach, and a comparative analysis of the proposed algorithm with baseline models. We also discuss the application to a real-world aviation safety problem using data from a commercial airline company and discuss the model's abilities and shortcomings, with some final remarks about possible deployment directions

Human Performance Contributions to Safety in Commercial Aviation

In the commercial aviation domain, large volumes of data are collected and analyzed on the failures and errors that result in infrequent incidents and accidents, but in the absence of data on behaviors that contribute to routine successful outcomes, safety management and system design decisions are based on a small sample of non- representative safety data. Analysis of aviation accident data suggests that human error is implicated in up to 80% of accidents, which has been used to justify future visions for aviation in which the roles of human operators are greatly diminished or eliminated in the interest of creating a safer aviation system. However, failure to fully consider the human contributions to successful system performance in civil aviation represents a significant and largely unrecognized risk when making policy decisions about human roles and responsibilities. Opportunities exist to leverage the vast amount of data that has already been collected, or could be easily obtained, to increase our understanding of human contributions to things going right in commercial aviation. The principal focus of this assessment was to identify current gaps and explore methods for identifying human success data generated by the aviation system, from personnel and within the supporting infrastructure

A METHODOLOGY FOR THE PREDICTION AND ANALYSIS OF PRECURSORS TO FLIGHT ADVERSE EVENTS

Air transportation is known to be the safest mean of transportation nowadays. The drastic improvements in aviation safety since its gain in popularity are undeniably a factor in the industry's growth over the last several decades. This growth brought social and economic benefits throughout the world and was expected to keep its momentum pre-COVID-19. Stakeholders such as the National Aeronautics and Space Administration (NASA), the Federal Aviation Administration (FAA), the National Transportation Safety Board (NTSB), aircraft manufactures, and airlines have developed systems, techniques, and technologies that are to thank for today's overall safety improvements and the reduction of accidents. The industry's maintained growth is welcomed, but current safety performances have been observed to stagnate instead of declining. With safety initiatives such as the Flight Operational Quality Assurance (FOQA) program and the growing number of aviation data, many of the previous techniques used to understand the causes of accidents are not scalable. These reasons led to the development of novel methods leveraging advanced analytical tools such as machine learning and deep learning. However, current use cases have focused mainly on anomaly detection and system health monitoring, which does not bring enough reaction time to deal with an imminent event. This research proposes the improvement of aviation safety through precursor mining. Precursors are defined as events that are highly correlated to the adverse event that they precede. Therefore, they provide predictive capabilities and can be used to explain pre-defined events. This thesis uses publicly available flight data to 1) develop a novel deep learning method to identify and rank precursors of multiple adverse events, 2) use unsupervised learning algorithms to group flights based on their precursors to identify potential causes for these events at a fleet-level, and finally 3) detect novelty to ensure that the developed precursor models operate within their limits and that new non pre-defined adverse events could be detected.M.S

Patient Safety As An Interactional Achievement: Conversational Analysis In The Trauma Center Of An Inner City Hospital

In this dissertation, I apply the methodology of Conversational Analysis to highlight the informal communication of an emergency room work group with the objective of discovering recurrent patterns of interaction and the inherent relational work necessary to accomplish the safe medical care of patients in a Trauma Code on a level of safety comparative to that of ultra-safe systems as described in the literature of High Reliability Organizations. The significance of relational elements of interaction on emerging social order is highlighted in processes of attunement, or the diminishing of difference of status in the use of mitigated speech and the co-construction of narrative. The use of mitigated speech and narrative serve as conversational moves of consequence, by which participants seek cooperation, coordination, and collaborate in face-to-face interaction, in a mutually constructed course of action; that is, in providing safe medical care in a highly complex and high risk environment

Considerations in Assuring Safety of Increasingly Autonomous Systems

Recent technological advances have accelerated the development and application of increasingly autonomous (IA) systems in civil and military aviation. IA systems can provide automation of complex mission tasks-ranging across reduced crew operations, air-traffic management, and unmanned, autonomous aircraft-with most applications calling for collaboration and teaming among humans and IA agents. IA systems are expected to provide benefits in terms of safety, reliability, efficiency, affordability, and previously unattainable mission capability. There is also a potential for improving safety by removal of human errors. There are, however, several challenges in the safety assurance of these systems due to the highly adaptive and non-deterministic behavior of these systems, and vulnerabilities due to potential divergence of airplane state awareness between the IA system and humans. These systems must deal with external sensors and actuators, and they must respond in time commensurate with the activities of the system in its environment. One of the main challenges is that safety assurance, currently relying upon authority transfer from an autonomous function to a human to mitigate safety concerns, will need to address their mitigation by automation in a collaborative dynamic context. These challenges have a fundamental, multidimensional impact on the safety assurance methods, system architecture, and V&V capabilities to be employed. The goal of this report is to identify relevant issues to be addressed in these areas, the potential gaps in the current safety assurance techniques, and critical questions that would need to be answered to assure safety of IA systems. We focus on a scenario of reduced crew operation when an IA system is employed which reduces, changes or eliminates a human's role in transition from two-pilot operations

A scoping literature review of natural language processing application to safety occurrence reports

Safety occurrence reports can contain valuable information on how incidents occur, revealing knowledge that can assist safety practitioners. This paper presents and discusses a literature review exploring how Natural Language Processing (NLP) has been applied to occurrence reports within safety-critical industries, informing further research on the topic and highlighting common challenges. Some of the uses of NLP include the ability for occurrence reports to be automatically classified against categories, and entities such as causes and consequences to be extracted from the text as well as the semantic searching of occurrence databases. The review revealed that machine learning models form the dominant method when applying NLP, although rule-based algorithms still provide a viable option for some entity extraction tasks. Recent advances in deep learning models such as Bidirectional Transformers for Language Understanding are now achieving a high accuracy while eliminating the need to substantially pre-process text. The construction of safety-themed datasets would be of benefit for the application of NLP to occurrence reporting, as this would allow the fine-tuning of current language models to safety tasks. An interesting approach is the use of topic modelling, which represents a shift away from the prescriptive classification taxonomies, splitting data into “topics”. Where many papers focus on the computational accuracy of models, they would also benefit from real-world trials to further inform usefulness. It is anticipated that NLP will soon become a mainstream tool used by safety practitioners to efficiently process and gain knowledge from safety-related text

Risk Communication for the Future

The conventional approach to risk communication, based on a centralized and controlled model, has led to blatant failures in the management of recent safety related events. In parallel, several cases have proved that actors not thought of as risk governance or safety management contributors may play a positive role regarding safety. Building on these two observations and bridging the gap between risk communication and safety practices leads to a new, more societal perspective on risk communication, that allows for smart risk governance and safety management. This book is Open Access under a CC-BY licence

Strategic Renewal Process Towards Sustainability – An Ecosystem Approach

Due to the ongoing sustainability megatrend, companies are increasingly incorporating environmental sustainability concerns into their strategies. Meanwhile this sustainability transition, strategic renewal processes of individual companies are inevitably co-evolving with their increasingly dynamic and complex business ecosystems. The internal rate of change in a company needs to be adjusted to that of its environment. Consequently, when companies are renewing their strategies, they need to consider not only the transition towards sustainability, but also their alignment within the constantly evolving business ecosystem and its focal value proposition. Addressing the limited existing knowledge on these issues, the objective of this study is to investigate how the process of strategic renewal towards environmental sustainability is co-evolving with its business ecosystem. To meet the research objective, a qualitative longitudinal single-case study of extreme kind was conducted at a technological forerunner Neste Oyj. Longitudinal and multi-sourced data covered Neste’s strategy process renewal from 2000 to 2019 with 6 top management interviews, validating group discussions with interviewees and Neste strategy team, 14 annual reports from 2005 to 2018, and versatile secondary data. Data-driven and thematical analysis of the rich dataset was enhanced by mapping the longitudinal processes with critical incident technique and ecosystem mapping software Kumu. The findings show that the mapped strategic renewal process follows the steps of formulation, implementation and evaluation meanwhile its encompassing business ecosystem follows the business ecosystem lifecycle of birth, expansion, leadership and renewal. These co-evolutionary processes in Neste case have taken place over time in four identified eras, which each have had their own critical incidents that construct sub-processes of strategic renewal. These sub-processes have been influenced by both internal and external drivers, which have discrete and ongoing natures. Identified internal drivers include organizational structure, organizational culture, competences and leadership, whereas external ones are divided into market development, regulation, collaboration, society and other drivers. The study contributes to the intersection of strategic renewal, business ecosystem and sustainability transition literature by providing longitudinal and processual insights to an extreme case with an exceptional strategic renewal process. As for managerial implications, top management benefits from considering strategy as a cyclic process that co-evolves with its business ecosystem and acknowledging that in the early phases of this co-evolutive process, inter-nal drivers have a dominant role. Along with other internal drivers, strengthening an internal vi-sion is of high importance as it allows engagement and proactive steering of ecosystem actors through collaborations throughout the entire process of strategic renewal. When renewing a value proposition along the renewed strategy, communications with ecosystem actors play a significant role in ensuring a successful re-alignment of business ecosystem to its focal value proposition. Further, the findings of this study are useful to policymakers who consider driving measures for strategic renewal towards sustainability. For future research agenda, it is recom-mended to expand the research scope to the hindering factors and drivers which may simultaneously hinder and support the strategic renewal towards environmental sustainability