Search

The system concept is a widely-used concept in research and practice. Already in the 50s of the previous century, a community was created to investigate interrelationships between domains and create a theory surpassing and comparing domains. The General Systems Theory (GST) community has tried to come up with such a theory for several decades. The ambition has grown more realistic in the years after, recognizing that an all-encompassing theory would not be possible. Since then, systems research was aimed at generating useful and usable approaches to compare and interrelate domains, thus creating a trans-disciplinary approach to enable description and analysis of large, and even complex, systems. The concept of systems, however, is often loosely used. Levels of abstraction are neglected, and interrelationships between systems ignored. In this paper, the concept of system is put in historical context, and further elaborated upon in the context of complex and trans-disciplinary systems. Two examples of transdisciplinary systems are presented and discussed to illustrate the use of the system concept.

Costs associated with unscheduled and preventive maintenance can contribute significantly to an airline's expenditure. Reliability analysis can help to identify and plan for maintenance events. Reliability analysis in industry is often limited to statistically based approaches that incorporate failure times as the primary stochastic variable, with additional strict assumptions regarding independence of events and underlying distributions of failure phenomena. This foregoes the complex nature of aircraft operations, where a whole range of operational factors may influence the probability of occurrence of a maintenance event. The aim of this research is to identify operational factors affecting component reliability and to assess whether these can be used to reduce the number of unscheduled occurrences (i.e. failures). To do so, a data-driven approach is adopted where historical operational and maintenance data is gathered and analysed to identify operational factors with a measurable influence on maintenance event occurrence. Both time-independent and time-dependent Proportional Hazard Models (PHMs), models which incorporate operational factors as covariates, are employed to generate reliability estimates. Results obtained from analysing historical data of a set of ten components with respect to unscheduled removals indicates that adopting new maintenance schedules, derived from the proposed reliability models, could reduce the number of unscheduled occurrences by approximately 37%. The potential benefits of adopting the proposed strategy are extensive. Nonetheless, numerous assumptions have been introduced to overcome challenges imposed by the complex nature of the data. To overcome these challenges, recommendations are made for future development of the proposed approach

The aircraft dispatch decision is a complex analysis based on many factors related to airworthiness regulations, aircraft health status, resource availability at current and future stop(s) and operational preferences of the operator. Within the turnaround time (TAT) a decision has to be made whether the aircraft can return to service, defects have to be deferred, operational restrictions apply, maintenance has to be performed, or if the aircraft is unable to safely perform the next flight. This paper presents a framework for automated dispatch decision support and, as a first step of implementation of the framework, a proof of concept for automated root cause identification by means of a case study on a Airbus A320 wing anti-ice valve. A decision tree algorithm has been applied to a synthetic dataset, representing historical failure data with associated root causes and observed symptoms, achieving correct classification of the root cause for 40% of the cases. Analysis of the results show that the accuracy of the method increases with an increasing number of symptoms associated to a root cause. Furthermore, the method cannot distinguish between root causes with similar symptoms. Although the use of synthetic data restricts conclusions that can be drawn from the results, the work shows a proof of concept for automated root cause identification and leads to initial findings that are essential for future implementation and optimisation of the method. Eventually, the framework will be operationalised in the form of a mobile tool to assist stakeholders in on-site aircraft dispatch decision support.