An Ontology-Based Approach for Knowledge Lifecycle Management within Aircraft Lifecycle Phases

In the aerospace domain, manufacturers and operators constantly seek to improve their products and processes. Increasingly, knowledge-based applications are developed to support or automate knowledge-intensive engineering tasks, saving time and money. However, engineering knowledge changes over time, which has implications for the usability and maintainability of knowledge-based applications. The research presented in this thesis contributes to the development of theory regarding knowledge change in engineering tasks. A conceptual knowledge lifecycle model to characterise and quantify knowledge change is presented. Additionally, this thesis proposes a methodology and an ontology-based approach to support the development of robust knowledge-based applications that can cope with knowledge change. These research contributions are validated in three case studies that consider engineering tasks in the aircraft design, manufacturing and maintenance lifecycle phases. The case studies demonstrate the utility of knowledge lifeycle management as usability and maintainability of knowledge-based applications are improved.Air Transport & OperationsAerospace Engineerin

A Generic Framework for Prognostics of Complex Systems

In recent years, there has been an enormous increase in the amount of research in the field of prognostics and predictive maintenance for mechanical and electrical systems. Most of the existing approaches are tailored to one specific system. They do not provide a high degree of flexibility and often cannot be adaptively used on different systems. This can lead to years of research, knowledge, and expertise being put in the implementation of prognostics models without the capacity to estimate the remaining useful life of systems, either because of lack of data or data quality or simply because failure behaviour cannot be captured by data-driven models. To overcome this, in this paper we present an adaptive prognostic framework which can be applied to different systems while providing a way to assess whether or not it makes sense to put more time into the development of prognostic models for a system. The framework incorporates steps necessary for prognostics, including data pre-processing, feature extraction and machine learning algorithms for remaining useful life estimation. The framework is applied to two systems: a simulated turbofan engine dataset and an aircraft cooling unit dataset. The results show that the obtained accuracy of the remaining useful life estimates are comparable to what has been achieved in literature and highlight considerations for suitability assessment of systems data towards prognostics.Air Transport & Operation

Time to retire: Indicators for aircraft fleets

It is well known that aircraft fleets are aging alongside rising operations and support costs. Logisticians and fleet managers who better understand the milestones and timeline of an aging fleet can recognise potential savings. This paper outlines generalised milestones germane to military aircraft fleets and then discusses the causes that lead to retirement motivations. Then this paper develops a utility per cost metric for aging aircraft fleet comparison as a means for determining when to retire a fleet. It is shown that utility per cost is a pragmatic metric for gauging the desirability of an existing fleet because of naturally occurring zones. Historical data from the US Air Force's fleet are used to validate the existence of these zones. Lastly, this work highlights the need for increased vigilance during the waning years of a fleet's lifecycle and discusses the intricacies of asset divestment planning

Introducing CNN-LSTM network adaptations to improve remaining useful life prediction of complex systems

Prognostics and Health Management (PHM) models aim to estimate remaining useful life (RUL) of complex systems, enabling lower maintenance costs and increased availability. A substantial body of work considers the development and testing of new models using the NASA C-MAPSS dataset as a benchmark. In recent work, the use of ensemble methods has been prevalent. This paper proposes two adaptations to one of the best-performing ensemble methods, namely the Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) network developed by Li et al. (IEEE Access, 2019, 7, pp 75464-75475)). The first adaptation (adaptable time window, or ATW) increases accuracy of RUL estimates, with performance surpassing that of the state of the art, whereas the second (sub-network learning) does not improve performance. The results give greater insight into further development of innovative methods for prognostics, with future work focusing on translating the ATW approach to real-life industrial datasets and leveraging findings towards practical uptake for industrial applications.Air Transport & Operation

Automating Contextualized Maintenance Documentation

Currently, task support information in aircraft maintenance is mostly provided using paper-based solutions, which are burdensome, slow and prone to error. Aircraft maintenance documentation contains vast amounts of information irrelevant for the task at hand and even for the simplest tasks multiple documents need to be consulted. Next to these issues with the documentation itself, Aircraft Maintenance Technicians (AMTs) have very limited on-site access to support information. These factors lead to 15–20% of hands-on-aircraft time being wasted on acquiring the right information or not using maintenance documentation for task support at all, risking maintenance error. This paper describes the development of a system for a first level of contextualization of maintenance documentation to simplify the retrieval of task support information. Combining a tailor-made ontology with a relational database system for Ontology-Based Data Access (OBDA), maintenance documents relevant to a specific aircraft registration mark can be identified. The system contributes to the research field of knowledge management by using OBDA for selecting relevant maintenance documents stored in a regular file folder. Future work will focus on increasing the level of contextualization, development of a mobile tool for on-site access and prototype verification and validation in an operational environment

Identification of Optimal Preventive Maintenance Decisions for Composite Components

This research proposes a decision support tool which identifies cost-optimal maintenance decisions for a given planning period. Simultaneously, the reliability state of the component is kept at or below a given reliability threshold: a failure limit policy applies. The tool is developed to support repair-or-replacement decision making for composite components likely to suffer impact damage. As a core part of the tool, a cost minimization problem is defined and solved using a search tree algorithm with heuristic constraints. Application to a case study which utilizes historical damage data and subsequent simulation shows the potential of the tool to identify cost-minimal maintenance decisions. The decision support tool is capable of incorporating a wide range of parameters to study preventive maintenance decision making in depthAerospace Transport & Operation

A decision support framework and prototype for aircraft dispatch assessment

When an aircraft experiences an unexpected issue during flight operations, a technician determines whether the aircraft can safely perform the next flight. This operational decision process - known as dispatch assessment - has to happen within limited available time between aircraft arrival and departure. Currently, technicians face two main problems during the assessment: lack of access to decision support information and a time-consuming process for finding relevant information in extensive maintenance manuals. These issues often lead to delays and additional costs and are indicative of three larger challenges in the decision support domain: 1) a paucity of decision support models and applications for operational processes in maintenance; 2) relatively few efforts in applying and evaluating artifacts in experimental and real-life operational settings; and 3) a lack of systematic development, application and evaluation of digitization and automation efforts of complex decision processes in maintenance. This paper applies a design science research approach to address these challenges and introduces two novel artifacts: a decision support framework for real-time decision making in aircraft dispatch, and a web-based prototype tool accessible through mobile solutions. The practical relevance of the framework and prototype is validated through two representative application and evaluation studies, one in an experimental setting and one in an operational environment. Results show significant time savings and strong qualitative indications towards a higher incentive to use documentation and reducing human risk factors that lead to maintenance error.</p

Identification of Optimal Preventive Maintenance Decisions for Composite Components

This research proposes a decision support tool which identifies cost-optimal maintenance decisions for a given planning period. Simultaneously, the reliability state of the component is kept at or below a given reliability threshold: a failure limit policy applies. The tool is developed to support repair-or-replacement decision making for composite components likely to suffer impact damage. As a core part of the tool, a cost minimization problem is defined and solved using a search tree algorithm with heuristic constraints. Application to a case study which utilizes historical damage data and subsequent simulation shows the potential of the tool to identify cost-minimal maintenance decisions. The decision support tool is capable of incorporating a wide range of parameters to study preventive maintenance decision making in dept

Predictive maintenance for aircraft components using proportional hazard models

Unscheduled maintenance can contribute significantly to an airline's cost outlay. 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 nine components with respect to unscheduled removals indicates that adopting new maintenance schedules, derived from the proposed reliability models, can reduce the number of unscheduled occurrences.Aerospace Transport & Operation

Automating Contextualized Maintenance Documentation

Currently, task support information in aircraft maintenance is mostly provided using paper-based solutions, which are burdensome, slow and prone to error. Aircraft maintenance documentation contains vast amounts of information irrelevant for the task at hand and even for the simplest tasks multiple documents need to be consulted. Next to these issues with the documentation itself, Aircraft Maintenance Technicians (AMTs) have very limited on-site access to support information. These factors lead to 15–20% of hands-on-aircraft time being wasted on acquiring the right information or not using maintenance documentation for task support at all, risking maintenance error. This paper describes the development of a system for a first level of contextualization of maintenance documentation to simplify the retrieval of task support information. Combining a tailor-made ontology with a relational database system for Ontology-Based Data Access (OBDA), maintenance documents relevant to a specific aircraft registration mark can be identified. The system contributes to the research field of knowledge management by using OBDA for selecting relevant maintenance documents stored in a regular file folder. Future work will focus on increasing the level of contextualization, development of a mobile tool for on-site access and prototype verification and validation in an operational environment.Aerospace Transport & Operation