A Formal Framework for Modeling and Prediction of Aircraft Operability using SysML

Aircraft operability characterizes the ability of anaircraft to meet operational requirements in terms of reliability, availability, risks and costs. Airlines policy must cope with operational decision-making and maintenance planning to handle the impacts of any event that generates a maintenance demand during operations. Aircraft operability is therefore considereda major requirement by each airline. The subject reaches a complexity level that deserves investigations in a Model-Based System Engineering (MBSE) approach enabling abstractions, as well as simulation and formal verification of models. In this paper, aircraft operability is modeled using Finite State Machines(FSM) supported by SysML. Simulation and model checking techniques are used to evaluate the impact of an event on airline operations using operability Key Performance Indicators (KPIs)such as reliability, availability and cost. The modeling frameworkis demonstrated on a case study of air-conditioning pack. This kind of operability analysis helps to project the potential impactof aircraft design on airline operations early in the aircraft development

Operability projection of major aircraft components during early aircraft design

Aircraft operational performance is a key factor to achieve airline profitability and meet passenger expectations. It is determined by the ‘operability’ of major aircraft components along with the operational context in which the aircraft operates. Operability is the ability of a system to meet its operational requirements in terms of reliability, availability and costs. This paper proposes a approach to take into account the type of technology employed in a major aircraft component to perform operability projections. An operability model is developed using Bayesian networks that helps project the influence of different input parameters on the operational performance of the major aircraft components. An approach combining engineering and in-service data is used to instantiate the different parameters and train the Bayesian network model. The trained model can be used by system designers to perform operability projections of different design solutions through Bayesian inference and make trade-off studies from an operability point of view. Clustering of the data using unsupervised learning is also addressed in this paper to identify the best combinations of input parameters that can produce the desirable operational performance

Holistic projection of aircraft operability during early design

Les performances opérationnelles des avions sont l’un des facteurs clés de la rentabilité des compagnies aériennes et de la satisfaction des usagers. Au même titre que la sécurité et les performances techniques, les performances opérationnelles des avions doivent être anticipées dès les premières étapes du développement afin de concevoir un avion qui puisse répondre pleinement aux exigences opérationnelles.La capacité d’un avion à répondre aux exigences opérationnelles est appelée ‘opérabilité’ de l’avion.L’objectif de cette thèse est de développer une méthodologie pour modéliser et projeter de manière globale et holistique, l’opérabilité d’un système d’intérêt pouvant être l’avion dans son ensemble ou une sous-partie de l’avion dès les premières phases de conception, afin de comparer différentes solutions de conception et de permettre aux décideurs de sélectionner celle qui répondra le mieux aux exigences opérationnelles. Cette méthodologie combine différentes techniques de modélisation et de simulation afin d’utiliser au mieux les connaissances des experts et les données en service disponibles chez Airbus.Les paramètres d’opérabilité des avions ont été formalisés à deux niveaux d’abstraction.Le premier niveau permet la prise en compte d’événements techniques précis pouvant survenir sur une définition technique détaillée du système d’intérêt. Le second niveau est adapté à la phase amont de conception, quand seuls les composants principaux de l’avion sont identifiés. La projection de l’opérabilité de l’avion en phase amont repose sur une approche innovante faisant appel à des paramètres d’opérabilité de haut niveau qui intègrent plusieurs propriétés des composants majeurs de l’avion. Une approche de modélisation stochastique a été utilisée pour traiter la nature hautement incertaine des opérations de l’avion en développant différents types de machines à états finis pour représenter et simuler les opérations de l’avion. Des modèles d’opérabilité ont été développés à l’aide de réseaux bayésiens pour prédire les performances opérationnelles des nouvelles solutions de conception et évaluer l’influence des principaux facteurs. Les résultats de projection quantitatifs obtenus grâce à cette méthodologie sont prometteurs et ouvrent la voie vers le développement d’une méthodologie industrielle à la fois mieux adaptée aux phases amont de conception et prenant en compte de manière plus formelle les données des avions en exploitation.Aircraft operational performance is one of the key drivers to airline profitability and passenger satisfaction. Along with safety and technical performance, aircraft operational performance needs to be projected from the early stages of development to design an aircraft that can fully meet the expectations of airlines and passengers.The ability of an aircraft to meet its operational requirements is called as aircraft‘operability’.The goal of this thesis is to develop a methodology to model and project the operability of a system of interest which can be the whole aircraft or a part of aircraft in aholistic way during early design. This helps compare different design solutions and allows decision makers to select the one that will best meet the operational requirements.This methodology combines different modeling and simulation techniques to make the best use of expert knowledge and in-service data available at Airbus.Aircraft operability parameters were formalized at two levels of abstraction. Thefirst level considers the precise technical issues that may occur on a detailed technical definition of the system of interest. The second level corresponds to the early design phase where only the aircraft major components are identified. A novel methodology was developed to project aircraft operability during early design using high level operability parameters that integrate several properties of the aircraft’s major components. A stochastic modeling approach has been used to address the highly uncertain nature of aircraft operations by developing different kinds of finite state machines to represent and simulate aircraft operations. Operability models were developed using Bayesian networks to predict the operational performance of newdesign solutions and assess the influence of major drivers of aircraft operational performance.The quantitative projection results obtained from this methodology look promising to pave the way for the development of an industrialized tool that is well adapted to early design phases and considers aircraft in-service data more formally

A preliminary comparison study between conventional and more-electrical regional aircraft using Pacelab

Today’s engineers are facing new challenges when it comes to implementing more-electrical architectures, specifically within the aviation industry. Therefore, tools such as Pacelab Suite can be used to conduct preliminary analysis of such modern architectures within an aircraft for a specific designed mission. This work mostly pertains to the development and analysis between a conventional and a more-electric architecture of a regional aircraft on Pacelab. Conventionally, three types of non-propulsive power are used to drive the various sub-systems in an aircraft: hydraulic, pneumatic and electrical sub-systems. Recently, a trend towards more-electrical architecture is being adopted in the aviation industry owing to the many drawbacks identified with conventional sub-system architecture. The conventional architecture of a large regional aircraft (ATR-72) was studied and the complete architecture of the electrical, hydraulic, flight controls, fuel and pneumatic systems is modelled using the Pacelab Suite. The conventional model of the aircraft is subjected to different scenarios in order to evaluate the aerodynamics, fuel consumption and electrical energy performance for a comparison with more electrical versions. The intent of this work is to analyze whether one of the two architectures significantly outperforms the other considering a mission-level metric. Further, it is also planned to subject the more-electric model to the same scenarios as the conventional model and draw conclusions on the merits and demerits of introducing More-Electric Aircraft (MEA) architecture in a regional aircraft

Holistic Operability Projection during Early Aircraft Design

Aircraft operational performance is one of the key drivers to airline profitability and punctuality. Along with safety and technical performance, aircraft operational performance needs to be projected from the early stages of development to design an aircraft that can fully meet the expectations of airlines and passengers. The ability of a system to meet its operational requirements in terms of reliability, availability and costs is termed as ‘Operability’. This paper proposes a method to model the operability of an aircraft during early design and use it to predict its operational performance. Initially, in-service data is used to create a reference baseline for a system of interest. For a new design, the designers evaluate the changes (deltas) in terms of few high-level metrics from an operations point of view called Consolidated Operability Metrics. An operability model is developed using Bayesian networks that is then used to project the changes in operational performance of the new design in comparison to the baseline. This method will help aircraft architects in conducting trade-off studies during early design from an operational point of view

A hybrid approach of machine learning and expert knowledge for projection of aircraft operability

Aircraft operational performance is a key driving factor to flight punctuality and airline profitability. The ability of a system to meet its operational requirements in terms of reliability, availability and costs is termed as 'Operability'. It is of high importance for aircraft manufacturers to project operability during the early stages of development of an aircraft in order to make trade-off studies. This paper proposes a hybrid approach of using machine learning and expert knowledge to aid the projection of aircraft operational performance during the early design stages. This approach aims to benefit from the huge amount of in-service data available from the current and past fleet of aircraft. Hence, machine learning techniques are used to learn how different technical issues and their associated maintenance activities impact aircraft operations. Expert knowledge is used to establish the default rules of the simulation model used for the operability projection. Results from machine learning are used to improve these rules allowing one to make holistic projections of the operational performance of future aircraft. This approach allows one to estimate the elapsed time in different operational states of an aircraft like flying, turn-around, etc. which can then be used to calculate different operability Key Performance Indicators (KPIs) like aircraft reliability and maintenance unavailability