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W.J.C. Verhagen

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65 records found

A re-evaluation of deep anomaly detection in spacecraft telemetry

Journal article (2024) - Lars Herrmann, Marie Bieber, Wim J.C. Verhagen, Fabrice Cosson, Bruno F. Santos
As the volume of telemetry data generated by satellites and other complex systems continues to grow, there is a pressing need for more efficient and accurate anomaly detection methods. Current techniques often rely on human analysis and preset criteria, presenting several challenges including the necessity for expert interpretation and continual updates to match the dynamic mission environment. This paper critically examines the use of deep anomaly detection (DAD) methods in addressing these challenges, evaluating their efficacy on real-world spacecraft telemetry data. It exposes limitations in current DAD research, highlighting the tendency for performance results to be overestimated and suggesting that simpler methods can sometimes outperform more complex DAD algorithms. By comparing established metrics for anomaly detection with newly proposed ones, this paper aims to improve the evaluation of DAD algorithms. It underscores the importance of using less accuracy-inflating metrics and offers a comprehensive comparison of DAD methods on popular benchmark datasets and real-life satellite telemetry data. Among the DAD methods examined, the LSTM algorithm demonstrates considerable promise. However, the paper also reveals the potential limitations of this approach, particularly in complex systems that lack a single, clear predictive failure channel. The paper concludes with a series of recommendations for future research, including the adoption of best practices, the need for high-quality, pre-split datasets, and the investigation of other prediction error methods. Through these insights, this paper contributes to the improved understanding and application of DAD methods, ultimately enhancing the reliability and effectiveness of anomaly detection in real-world scenarios. ...
Journal article (2024) - Marie Bieber, Wim J.C. Verhagen, Bruno F. Santos
Over the past years, advanced prognostic models and approaches have been developed. Most existing approaches are tailored to one specific system and cannot adaptively be used on different systems. This can lead to years of research and expertise being put into implementing prognostic models without the capacity to predict system failures, either because of a lack of data or data quality or because failure behavior cannot be captured by data-driven models. In addition, prognostic models are often evaluated using metrics only related to the correctness of predictions, preventing meaningful evaluation of operational performance. This paper makes use of a framework that can automatically choose prognostic settings based on specific system data. It simultaneously optimizes the choice of methodologies using metrics that capture multiple aspects of prediction quality. We apply this framework to both a simulated data set and a real aircraft data set to characterize the impact of metrics on the choice of prognostic methodologies. The results show that the choice of optimization metric greatly impacts the output of the generic prognostic framework and the overall performance. In addition, a definition for data suitability is provided and assessed on the aircraft system data sets. ...
Journal article (2023) - N. Borst, W. J.C. Verhagen
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. ...
Journal article (2023) - W.J.C. Verhagen, Bruno F. Santos, Floris Freeman, Paul van Kessel, D. Zarouchas, Theodoros Loutas, R.C.K. Yeun, I. Heiets
Condition-Based Maintenance (CBM) is a policy that uses information about the health condition of systems and structures to identify optimal maintenance interventions over time, increasing the efficiency of maintenance operations. Despite CBM being a well-established concept in academic research, the practical uptake in aviation needs to catch up to expectations. This research aims to identify challenges, limitations, solution directions, and policy implications related to adopting CBM in aviation. We use a generalizable and holistic assessment framework to achieve this aim, following a process-oriented view of CBM development as an aircraft lifecycle management policy. Based on various inputs from industry and academia, we identified several major sets of challenges and suggested three primary solution categories. These address data quantity and quality, CBM implementation, and the integration of CBM with future technologies, highlighting future research and practice directions. ...
Journal article (2023) - M.T. Bieber, W.J.C. Verhagen, Fabrice Cosson, Bruno F. Santos
Spacecraft systems collect health-related data continuously, which can give an indication of the systems’ health status. While they rarely occur, the repercussions of such system anomalies, faults, or failures can be severe, safety-critical and costly. Therefore, the data are used to anticipate any kind of anomalous behaviour. Typically this is performed by the use of simple thresholds or statistical techniques. Over the past few years, however, data-driven anomaly detection methods have been further developed and improved. They can help to automate the process of anomaly detection. However, it usually is time intensive and requires expertise to identify and implement suitable anomaly detection methods for specific systems, which is often not feasible for application at scale, for instance, when considering a satellite consisting of numerous systems and many more subsystems. To address this limitation, a generic diagnostic framework is proposed that identifies optimal anomaly detection techniques and data pre-processing and thresholding methods. The framework is applied to two publicly available spacecraft datasets and a real-life satellite dataset provided by the European Space Agency. The results show that the framework is robust and adaptive to different system data, providing a quick way to assess anomaly detection for the underlying system. It was found that including thresholding techniques significantly influences the quality of resulting anomaly detection models. With this, the framework can provide both a way forward in developing data-driven anomaly detection methods for spacecraft systems and guidance relative to the direction of anomaly detection method selection and implementation for specific use cases. ...
Conference paper (2022) - Jeffrey M. Newcamp, W. J.C. Verhagen, Richard Curran
Aircraft fleet managers lack tools to aid decision-making for fleets nearing retirement, which leads to rushed and ill-informed decisions. Accordingly, aging aircraft fleets are underutilized and fleets can be retired before their useful lifetime has been expended. A decision support framework is proposed to solve the aging military aircraft retirement problem. It integrates four steps for fleet managers to simplify the decision-making process: (i) Understanding the structural toll caused by utilization, (ii) Recognizing the indicators that predispose a fleet for retirement, (iii) Determining an optimal fleet size and choosing which aircraft to retire and (iv) Optimizing end-of-life usage prior to retirement. An example using a sample military fleet is used to illustrate the effectiveness of the decision support framework, integrating both computational results and manager judgement. Fleet managers were used to validate the concepts in the framework and their opinions are presented herein. It is shown that fleet managers can utilize a decision support framework to positively impact their decision-making for full-spectrum aging aircraft retirement decisions. ...
Conference paper (2022) - M.T. Bieber, W.J.C. Verhagen, Bruno F. Santos
Metrics play an important part in the development and application of prognostic methodologies as they provide the capability to characterize and assess the quality of remaining useful life predictions. Although there is a wide range of both, prognostic metrics and prognostic methodologies available, the choice of those often is a demanding and time consuming task. Additionally, they are often treated as two separate problems to solve, while the choice of metrics has an impact on the choice of prognostic methodology and vice versa. In this paper, we therefore present a framework with the capability to automatically choose prognostic settings given specific system data to account for five different prognostic metrics. We then apply this framework to an aircraft data set to characterize the impact of metrics on the choice of prognostic methodologies. The results show that the choice of optimization metric has a big impact on the output of the generic prognostic framework and on the overall prognostic performance. ...
Journal article (2022) - Michael J. Scott, W.J.C. Verhagen, M.T. Bieber, Pier Marzocca
In recent decades, the increased use of sensor technologies, as well as the increase in digitalisation of aircraft sustainment and operations, have enabled capabilities to detect, diagnose, and predict the health of aircraft structures, systems, and components. Predictive maintenance and closely related concepts, such as prognostics and health management (PHM) have attracted increasing attention from a research perspective, encompassing a growing range of original research papers as well as review papers. When considering the latter, several limitations remain, including a lack of research methodology definition, and a lack of review papers on predictive maintenance which focus on military applications within a defence context. This review paper aims to address these gaps by providing a systematic two-stage review of predictive maintenance focused on a defence domain context, with particular focus on the operations and sustainment of fixed-wing defence aircraft. While defence aircraft share similarities with civil aviation platforms, defence aircraft exhibit significant variation in operations and environment and have different performance objectives and constraints. The review utilises a systematic methodology incorporating bibliometric analysis of the considered domain, as well as text processing and clustering of a set of aligned review papers to position the core topics for subsequent discussion. This discussion highlights state-of-the-art applications and associated success factors in predictive maintenance and decision support, followed by an identification of practical and research challenges. The scope is primarily confined to fixed-wing defence aircraft, including legacy and emerging aircraft platforms. It highlights that challenges in predictive maintenance and PHM for researchers and practitioners alike do not necessarily revolve solely on what can be monitored, but also covers how robust decisions can be made with the quality of data available. ...
Journal article (2022) - M.T. Bieber, W.J.C. Verhagen
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. ...
Conference paper (2022) - I. Tseremoglou, M.T. Bieber, W.J.C. Verhagen, Bruno F. Santos, F.C. Freeman, P.J. van Kessel
One of the challenges of Condition-Based Maintenance (CBM) is to combine health monitoring and predictions with efficient scheduling tools. However, the majority of literature is focusing on the assessment of prognostics algorithms performance. In fact, the added value of these algorithms can only be assessed when considering their impact on maintenance decision process. Furthermore, in practice, when considering the scenario of an aircraft fleet with multiple monitored components, it is hard for a human decision-maker to translate and identify the effect of probabilistic results from all prognostics models from all systems on the maintenance schedule. Therefore, to support the implementation of CBM, the prognostics algorithms have to be integrated within a scheduling framework. Our paper proposes this integration in order to evaluate the impact of different level of prognostics accuracy and uncertainty on the aircraft fleet maintenance scheduling level. First, a Support Vector Regression (SVR) model is used to predict the Remaining Useful Life (RUL) distributions of the monitored components. Second, the maintenance scheduling problem is solved within a Reinforcement Learning (RL) approach incorporating a state-of-the-art Partially Observable Monte Carlo algorithm. Implementing a rolling horizon approach, our proposed framework is applied to a fleet of 10 aircraft, each equipped with multiple monitored systems. A case study with multiple different prediction accuracy and uncertainty scenarios is performed to assess the impact of prognostics uncertainty on optimal maintenance scheduling. The performed analysis aims to guide the development and assessment of prognostic models in terms of accuracy and uncertainty in the context of CBM. ...
Journal article (2021) - Q. Deng, Bruno F. Santos, W.J.C. Verhagen
Modern aircraft have thousands of parts, systems, and components that need to be recurrently inspected or replaced. To keep the fleet airworthy, maintenance planners have to schedule the maintenance checks for each aircraft and the associated tasks. In practice, these two complex problems are solved following the experience of planners, resulting in sub-efficient solutions. This paper presents the first decision support system (DSS) developed for optimizing both aircraft maintenance check schedule and task allocation. The DSS integrates aircraft maintenance check scheduling, task allocation to each maintenance check, and shift planning in the same framework. The practical relevance of the DSS is illustrated through three test cases. The results show that the DSS can be used not only to optimize maintenance plans but also to study future maintenance policies. The results reveal substantial improvements in all key performance indicators compared with the planning approach followed by a partner airline. ...
Journal article (2021) - Hemmo Koornneef, Wim J.C. Verhagen, Richard Curran
Aircraft dispatch involves determining the optimal dispatch option when an aircraft experiences an unexpected failure. Currently, maintenance technicians at the apron have limited access to support information and finding the right information in extensive maintenance manuals is a time-consuming task, often leading to technically induced delays. This paper introduces a novel web-based prototype decision support system to aid technicians during aircraft dispatch decision-making and subsequent maintenance execution. A system architecture for real-time dispatch decision support is established and implemented. The developed system is evaluated through a case study in an operational environment by licensed maintenance technicians. The system fully automates information retrieval from multiple data sources, performs alternative identification and evaluation for a given fault message, and provides the technician with on-site access to relevant information, including the related maintenance tasks. The case study indicates a potential time saving of up to 98% per dispatch decision. Moreover, it enables digitalization of the—currently mostly paper-based—dispatch decision process, thereby reducing logistics and paper waste. The prototype is the first to provide operational decision support in the aircraft maintenance domain and addresses the lack of correlation between theory and practice often found in decision support systems research by providing a representative case study. The developed custom parser for SGML-based documents enables efficient identification and extraction of relevant information, vastly contributing to the overall reduction of the decision time. ...
Conference paper (2021) - Marie Bieber, Wim J.C. Verhagen, Bruno F. Santos
During flights aircraft continuously collect data regarding operations, health status and system condition. Data-driven approaches typically applied to system specific sensor data provide a way to predict failures of aircraft systems. However, it is believed that some systems deteriorate faster when subjected to particular environmental conditions, such as humidity or dust. In this study, we consider an aircraft system which is suspected to experience degradation due to humidity during ground operations. We apply a Random Forest approach to sensor data only and a combination of sensor data and environmental data from airports to estimate the system's remaining useful life. To our knowledge this is the first paper addressing the problem of integrating environmental data in prognostics for aircraft systems using raw sensor data. The method is validated on a data set provided by an airline that includes the per-second sensor data of 11 different sensors for roughly 12,300 flights, as well as 15 removals. Meteorological data for airports worldwide is obtained from the Meteorological Aerodrome Reports database. The results show that incorporating environmental data in prognostics has a potential towards more accurate prediction models. ...
Conference paper (2021) - M.T. Bieber, W.J.C. Verhagen, Bruno F. Santos
Prognostics for condition-based maintenance does not only consist of prognostic algorithms but also involves steps such as data pre-processing, feature extraction, and feature selection, all of which contribute to the quality of the remaining useful life estimation. This process requires a lot of expertise and technical knowledge, which for many application systems is neither feasible nor affordable. In this paper, therefore, we present a generic framework with the capability to automatically choose the optimal settings for prognostics, given a specific data set. The framework consists of two phases. In the first one, a genetic algorithm optimizes the choice of methodologies together with hyperparameter settings for the feature extraction, feature selection, and prognostic algorithm selection. In the second phase, the identified settings define the prognostic setup, which in turn is used to train the model for remaining useful life estimation. This framework is then applied to a simulated aircraft engine data set. The first results show that remaining useful life estimates are comparable to the values obtained using established prognostic algorithms on the same data set. In addition, the framework is applied to estimate the remaining useful life of real aircraft systems. Results on underlying data sets suggest that the generic prognostic framework can easily and quickly be adapted to various systems. In further consequence, such a generic framework offers a way to assess the feasibility of prognostics for systems depending on the underlying existing data sets. ...
This paper proposes an analytical model that uses historical damage dimension data to deduce physical impactor characteristics (size and energy) that has caused a certain resulting damage. Maintenance tasks occur in operations due to impact, however the source of the damage caused in the event remains in most cases unknown. Consequently, by inferring what has caused a certain type of damage from the distribution of the damage type and severity relative to impactor types, maintainers can be better prepared in terms of what to expect from a given impactor source. The developed model introduces a novel transition deformation region between the local deformation and the global plate deflection, allowing for fast and accurate predictions of the impact event. Using the known aluminium structural properties and damage dimensions, the damage data is converted into impactor data. The model is applied in a case study using 120 fuselage dent damages dimensions (length, width, and depth) from a Boeing 777 fleet. The results show that the model deduces impactor characteristics for 94% of the considered damages, ranging up to 240 J and 110 mm for impactor energy and radius respectively. ...
Journal article (2020) - Rui Li, Wim J.C. Verhagen, Richard Curran
Aircraft maintenance has been further developed with predictive maintenance instead of solely condition-based maintenance. Prognostics and health management (PHM) with advanced technologies can utilize real-time and historical health state information to provide actionable information, enabling predictive maintenance decision-making. In this case, the methodology of how to design the PHM systems is an issue to be faced. The state of the art has provided several conceptual design methodologies and associated methods to support the conceptual requirements development of PHM systems. However, there is no rigorous process available for requirements definition. Existing options for requirements derivation are lacking details, which restricting PHM system design and development. This constitutes a major drawback and hurdle towards the successful design of PHM systems in practice. This paper consequently proposes a methodology for the systematic derivation of system requirements towards PHM system development. Besides, this methodology defines detailed processes for requirements definition, and positions mean through which various categories of requirements can be derived through appropriate analyses in detail. Sequences of interoperability requirements categories and associated flow-down perspectives are identified. To evaluate the applicability, this paper undertakes the case study of requirements definition for a generic PHM system, which provides a comprehensive application of the methodology. Designers can perform requirements definition under this methodology as guidance towards the design of a successful PHM system, providing solutions for predicting remaining useful life (RUL) to support aircraft predictive maintenance. ...
Foreword postscript (2020) - Josip Stjepandić, Nel Wognum, Wim J.C. Verhagen
The system concept has existed for several decades now, but is still a viable concept to be used to denote a problem area and to adopt a holistic view. The essence of a system is that it consists of elements and relationships between these elements, and that it exerts a function in its environment, provided it is an open system. A system can be defined at different layers of abstraction consisting of subsystems, which themselves may consist of subsystems again. Themost complex level includes human beings. The system concept is adopted in Systems Engineering (SE) in which not only the engineering system under development is modeled, but also the development process itself in which many different disciplines need to be involved depending on the (lifecycle) requirements in focus. In this introductory chapter we draw the way we have paved to provide this book from the first idea on. The system concept, the origins, the goals and the expected audience of this book are roughly described. Finally, we give the first insight in the structure of this book and themutual interdependence of the chapters. This book contains many different contributions in the area of SE, categorized into 4 parts: an introduction to the concept, methods and tools, applications, and challenges. ...
Journal article (2020) - Xiaojia Zhao, Wim J.C. Verhagen, Richard Curran
The present study proposes an economic indicator to support the evaluation of aircraft End of Life (EoL) strategies in view of the increasing demand with regards to aircraft decommissioning. This indicator can be used to evaluate the economic performance and to facilitate the trade-off studies among different strategies. First, Disposal and Recycle (D&R) scenarios related to stakeholders are investigated to identify the core concepts for the economic evaluation. Next, we extracted the aircraft D&R process from various real-life practices. In order to obtain the economic measure for the engineering process, a method of estimating the D&R cost and values are developed by integrating product, process and cost properties. This analysis is demonstrated on an averaged data set and two EoL aircraft cases. In addition, sensitivity analysis is performed to evaluate the impact of the D&R cost, residual value, and salvage value. Results show that the disassembly and dismantling of an aircraft engine possesses relatively more economic gains than that for the aircraft. The main factors influencing the proposed D&R economic indicator are the salvage value and D&R cost for economically efficient D&R cases. In addition, delaying the disposal and recycle process for EoL aircraft can lead to economically unfavorable solutions. The economic indicator combined with the evaluation methods is widely applicable for evaluations of engineering products EoL solutions, and implies a significant contribution of this research to decision making for such complex systems in terms sustainable policy. ...
Journal article (2020) - Rui Li, Wim J.C. Verhagen, Richard Curran
Prognostic and Health Management (PHM) systems support aircraft maintenance through the provision of diagnostic and prognostic capabilities, leveraging the increased availability of sensor data on modern aircraft. Diagnostics provide the functionalities of failure detection and isolation, whereas prognostics can predict the remaining useful life (RUL) of the system. In literature, PHM technologies have been studied from different perspectives, covering various aims such as improving aircraft system reliability, availability, safety and reducing the maintenance cost. From a design perspective, several conceptual formulations of design methodologies are available, enabling a set of PHM system architectures based on different frameworks and the derivation of system requirements. However, a systematic methodology towards a consistent definition of PHM architectures has not been well established. The characteristics of architectures have not been dealt with in depth. To address these gaps, this paper presents a systematic methodology for PHM architecture definition to ensure a more complete and consistent design during the development phase of the product lifecycle. Moreover, a generic PHM architecture in accordance with this systematic methodology is proposed in this article. A case study is conducted to verify and validate the architecture, ensuring it meets the requirements for a correct and complete representation of PHM characteristics. ...