Integrated Vehicle Health Management (IVHM) systems use models and algorithmic techniques to process Condition-based Data (CBD) to offer prognostic information and actionable imperatives in support of Condition-based Maintenance (CBM) for the system. IVHM technology was first introduced by NASA to gather data, diagnose, detect, and predict faults, and support operational and post-maintenance activities in space vehicles. Eventually, it expanded to other vehicle types such as aircraft, ships, and land vehicles [1]. In recent years, the United States Army has been implementing a policy of CBM to transition from preventive to predictive maintenance [2]. One of the many challenges faced by the Army is the lack of accurate methods to assess ground vehicle reliability using modeling and/or simulation. This study aims at developing a Markov Chain-based algorithm that can detect anomalies and that is capable of accurately predicting the operational states of military ground vehicles. Several different Markov Chain Models (MCMs) have been developed and tested in their ability to predict the next state of a vehicle, given its current state (diagnostics and prognostics), and to examine how well a given model can detect unknown measurements (anomaly detection). A target of 90% Correct Classification (PCC) was established for all the vehicle performance data. The results suggest that it is possible to predict at a high level of accuracy the likely operational states of the military vehicles using MCMs. The anomaly detection test results revealed that MCMs can clearly distinguish a change in the performance data, that does not match the expected performance.

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This thesis presents a novel method of locating imbalance faults in rotating machinery through the study of bearing nonlinearities. Localisation in this work is presented as determining which discs/segments of a complex machine are affected with an imbalance fault. The novel method enables accurate localisation to be achieved using a single accelerometer, and is valid for both sub and super-critical machine operations in the presence of misalignment and rub faults. The development of the novel system for imbalance localisation has been driven by the desire for improved maintenance procedures, along with the increased requirement for Integrated Vehicle Health Management (IVHM) systems for rotating machinery in industry. Imbalance faults are of particular interest to aircraft engine manufacturers such as Rolls Royce plc, where such faults still result in undesired downtime of machinery. Existing methods of imbalance localisation have yet to see widespread implementation in IVHM and Engine Health Monitoring (EHM) systems, providing the motivation for undertaking this project. The imbalance localisation system described has been developed primarily for a lab-based Machine Fault Simulator (MFS), with validation and verification performed on two additional test rigs. Physics based simulations have been used in order to develop and validate the system. An Artificial Neural Network (ANN) has been applied for the purposes of reasoning, using nonlinear features in the frequency domain originating from bearing nonlinearities. The system has been widely tested in a range of situations, including in the presence of misalignment and rub faults and on a full scale aircraft engine model. The novel system for imbalance localisation has been used as the basis for a methodology aimed at localising common faults in future IVHM systems, with the aim of communicating the results and findings of this research for the benefit of future research. The works contained herein therefore contribute to scientific knowledge in the field of IVHM for rotating machinery.

Aircraft operators demand an ever increasing availability of their fleets with constant reduction of their operational costs. With the age of many fleets measured in decades, the options to face these challenges are limited. Integrated Vehicle Health Management (IVHM) uses data gathered through sensors in the aircraft to assess the condition of components to detect and isolate faults or even estimate their Remaining Useful Life (RUL). This information can then be used to improve the planning of maintenance operations and even logistics and operational planning, resulting in shorter maintenance stops and lower cost. Retrofitting health monitoring technology onto legacy aircraft has the capability to deliver what operators and maintainers demand, but working on aging platforms presents numerous challenges. This thesis presents a novel methodology to select the combination of diagnostic and prognostic tools for legacy aircraft that best suits the stakeholders’ needs based on economic return and financial risk. The methodology is comprised of different steps in which a series of quantitative analyses are carried out to reach an objective solution. Beginning with the identification of which components could bring higher reduction of maintenance cost and time if monitored, the methodology also provides a method to define the requirements for diagnostic and prognostic tools capable of monitoring these components. It then continues to analyse how combining these tools affects the economic return and financial risk. Each possible combination is analysed to identify which of them should be retrofitted. Whilst computer models of maintenance operations can be used to analyse the effect of retrofitting IVHM technology on a legacy fleet, the number of possible combinations of diagnostic and prognostic tools is too big for this approach to be practicable. Nevertheless, computer models can go beyond the economic analysis performed thus far and simulations are used as part of the methodology to get an insight of other effects or retrofitting the chosen toolset.

Aircraft operators demand an ever increasing availability of their fleets with constant reduction of their operational costs. With the age of many fleets measured in decades, the options to face these challenges are limited. Integrated Vehicle Health Management (IVHM) uses data gathered through sensors in the aircraft to assess the condition of components to detect and isolate faults or even estimate their Remaining Useful Life (RUL). This information can then be used to improve the planning of maintenance operations and even logistics and operational planning, resulting in shorter maintenance stops and lower cost. Retrofitting health monitoring technology onto legacy aircraft has the capability to deliver what operators and maintainers demand, but working on aging platforms presents numerous challenges. This thesis presents a novel methodology to select the combination of diagnostic and prognostic tools for legacy aircraft that best suits the stakeholders’ needs based on economic return and financial risk. The methodology is comprised of different steps in which a series of quantitative analyses are carried out to reach an objective solution. Beginning with the identification of which components could bring higher reduction of maintenance cost and time if monitored, the methodology also provides a method to define the requirements for diagnostic and prognostic tools capable of monitoring these components. It then continues to analyse how combining these tools affects the economic return and financial risk. Each possible combination is analysed to identify which of them should be retrofitted. Whilst computer models of maintenance operations can be used to analyse the effect of retrofitting IVHM technology on a legacy fleet, the number of possible combinations of diagnostic and prognostic tools is too big for this approach to be practicable. Nevertheless, computer models can go beyond the economic analysis performed thus far and simulations are used as part of the methodology to get an insight of other effects or retrofitting the chosen toolset.

Health condition monitoring, commonly referred as Integrated Vehicle Health Management (IVHM) for fleets or vehicles, studies the current and future health state of a system. Health monitoring techniques based on data driven approaches have proven successful in several areas and are easily scalable; however they do not rely on the understating of the physics of failure; whereas Physics-based Model (PbM) approaches require expert knowledge of the failure modes and are based on the understanding of the component behaviour and degradation mechanisms. The development of IVHM is particularly challenging for legacy aircraft due to the restrictive regulations of the aerospace industry. This thesis proposes a novel PbM technique to detect metal-metal contact in hydrodynamic bearings. The planetary transmission of an aircraft’s Integrated Drive Generator (IDG) is used as a case study. Research on the detection of metal-metal contact in hydrodynamic bearings has focused on data driven approaches using vibration or acoustic emissions rather than on PbMs. The proposed technique estimates metal-metal contact by modelling the physical phenomena involved in the failure mechanism and only the speed, load and temperature are required as inputs, all of them available in the IDG and not requiring any additional sensors. The study of metal-metal in hydrodynamic bearings in the field of tribology has focused on mixed lubrication models of the whole bearing, or computational models accounting for local effect under the hydrodynamic lubrication region. In addition to the IVHM technique, this thesis contributes to the field of tribology by proposing a computational mixed lubrication model capable of studying metal-metal contact locally along the lubricated surface of the bearing. Experimental results of a plain journal bearing have been used to validate the PbM and a replica of the transmission of the IDG has been tested to evaluate the effectiveness of the proposed technique at detecting metal-metal contact.

he digital revolution in the latter part of the twentieth century has resulted in the increased use and development of Cyber-Physical Systems. Two of which are Unmanned Aerial Systems (UAS) and Integrated Vehicle Health Management (IVHM). Both are relatively new areas of interest to academia, military, and commercial organisations. Designing IVHM for a UAS is no easy task – the complexity inherent in UAS, with projects involving multiple partners/organisations; multiple stakeholders are also interested in the IVHM. IVHM needs to justify itself throughout the life of the UAS, and the lack of established knowledge makes it hard to know where to start. The establishment and analysis of requirements for IVHM on UAS is known to be important and costly – and for IVHM a complex one. There are multiple stakeholders to satisfy and ultimately the needs of the customer, all demanding different things from the IVHM, and with limited resources they need to be prioritised. There are also many hindrances to this: differences in language between stakeholders, customers failing to see the benefits, scheduling conflicts, no operational data. The contribution to knowledge in this thesis is the IVHM Requirements Deployment (IVHM-RD) – a method for a designer of UAS IVHM to build a tool which can consolidate and evaluate the various stakeholder’s requirements. When the tool is subsequently populated with knowledge from individual Subject Matter Experts (SMEs), it provides a prioritised set of IVHM requirements. The IVHM-RD has been tested on two design cases and generalised for the use with other designs. Analysis of the process has been conducted and in addition the results of the design cases have been analysed in three ways: how the results relate to each design case, comparison between the two cases, and how much the relationships between requirements are understood. A validation exercise has also been conducted to establish the legitimacy of the IVHM-RD process. This research is likely to have an impact on the elicitation and analysis of IVHM requirements for UAS – and the wider design process of IVHM. The IVHM-RD process should also prove of use to designers of IVHM on other assets. The populations of the design cases also provide information which could be useful to other designer and future research.

Spherea (anciennement Cassidian Test & Services), initiateur de la thèse, est un des leaders sur le marché des systèmes automatiques de test (ATE) pour la maintenance des véhicules aéronautiques et de défense. L’intérêt de la société dans la recherche en gestion intégrée de la santé de véhicules est motivé par le taux élevé de fausses déposes d’équipements survenues lors de la maintenance opérationnelle, détectées par les ATE. Ceci engendre des actions de maintenance superflues, et par conséquent des pertes majeurs de temps, d'énergie et d'argent. L’IVHM, de par ses capacités avancées de diagnostic et de pronostic, et son intégration au niveau de l'entreprise de la gestion de santé de véhicules pourrait permettre la réduction des taux de NFF. Néanmoins, les solutions de systèmes IVHM proposées par les communautés scientifique et industrielle sont la plupart du temps développées pour des systèmes spécifiques, et souvent fondées sur des concepts propriétaires. Cela a pour conséquence un manque de consensus à la fois dans les principes structurants des systèmes IVHM et dans leur ingénierie. Aujourd'hui, un défi majeur est de fournir un cadre de modélisation d’IVHM indépendamment du type de système d’intérêt, soutenant l’ingénierie des systèmes IVHM. Vers ce cadre, les principales contributions développées dans cette thèse construisent progressivement les fondations et les piliers d'un cadre de modélisation d’IVHM. La proposition, dans une vision système, des principes structurants d’un système de systèmes permet de définir au général un système IVHM. A partir de cette vision système, le focus de la thèse est orienté sur la gestion de santé du véhicule, fonction de l’IVHM centrée sur le véhicule constituant le catalyseur des décisions de maintenance au niveau opérationnel, ayant la capacité de résoudre le problème industriel à la genèse de la thèse. Les principes structurant clés de cette fonction en trois dimensions (dimension fonctionnelles, dimension d’abstraction, dimension de distribution entre le segment embarqué/sol) sont à la base de la proposition d’un cadre générique de modélisation d’IVHM considérant à la fois les fonctions internes et externes au véhicule. Ce cadre est investigué en cohérence avec une approche construite sur les modèles (MBSE). Conformément à cette approche MBSE, la modélisation, au sein de ce cadre d’IVHM, du module générique de gestion de la santé (gHMM) constitue le support d’intégration des fonctionnalités de diagnostic et de pronostic. Cette modélisation repose sur une vision « boîte noire » et « boîte blanche » du module traduite par un ensemble cohérent de diagrammes SysML, et se basant sur les structures de données standardisées d’OSA-CBM. La formalisation du gHMM permet d'intégrer le diagnostic et le pronostic, processus clés de l’IVHM, dans son sens conventionnel : du diagnostic vers le pronostic, que dans un sens original : du pronostic vers le diagnostic. Ce dernier sens est illustré par la proposition d'un algorithme support à une activité élémentaire du gHMM dans la finalité de réduire les groupes d’ambiguïtés dans le diagnostic. Cette ingénierie aboutit par conséquent à un cadre générique de modélisation, qui par un principe d’instanciation, permet la construction d’une architecture de gestion de l’état de santé d’un système IVHM particulier. Vers cette instanciation la thèse examine les caractéristiques qui impactent la conception d’architectures de gestion de la santé et la sélection d’algorithmes supportant ces architectures, et en propose une formalisation basée sur les ontologies pour la sélection multicritères d’algorithmes de diagnostic et de pronostic appropriés pour la gestion de la santé de véhicules. Finalement, le protocole de validation de l’ensemble des contributions est proposé et illustré à des échelles différentes pour la gestion de l’état de santé d’éoliennes et de drones / Spherea (formerly Cassidian Test & Services), initiator of the PhD thesis, is a leading provider of Automatic Test Equipment (ATE) solutions for aerospace and military vehicles’ maintenance. The company’s interest in Integrated Vehicle Health Management (IVHM) research is motivated by occurrence of No Fault Found (NFF) events detected by ATE, and determining superfluous maintenance activities and consequently major wastes of time, energy and money. IVHM, through its advanced diagnostics and prognostics capabilities, and integration at enterprise level of vehicle health management could solve NFF events occurring during operational-level maintenance. Nevertheless, IVHM systems proposed so far are most of the times developed and matured empirically, for specific vehicle systems, founded on proprietary concepts, and lacking of consensual structuring principles. This results in a lack of consensus in both the structuring principles of IVHM systems and their Systems Engineering. Today, the challenge is to provide an IVHM modelling framework independent from the type of supported system and usable for IVHM Systems Engineering. Towards such framework, the main contributions developed in this thesis progressively build the foundation and pillars of an IVHM modelling framework. The notion of system of systems drives our first proposal of defining principles of an overall IVHM system. From this system vision, the focus of the thesis is oriented on the vehicle centric function of IVHM as catalyst of maintenance decisions at operational level, having the ability to solve the industrial problems at the genesis of the thesis. The key structuring principles of this function are analysed upon three dimensions (functional dimension, a dimension of abstraction, and distribution between the on-board /on-ground segment), setting the basis of the proposal of a generic modelling framework IVHM, considering both vehicle and enterprise centric functions. This framework is built following a Model-based Systems Engineering (MBSE) approach, supported by SysML. The major contribution of the thesis is the modelling, within the framework of IVHM, of the generic Health Management Module (gHMM), support for integration of diagnostics and prognostics, key processes of health management. The gHMM formalization enables to integrate diagnostics and prognostics not only in the conventional way: from diagnosis to prognosis, but also in an original one: from prognostics to diagnostics with the purpose of reducing ambiguity groups; the latter is backed-up through the proposal of an algorithm for one elementary activities of the gHMM. The gHMM MBSE engineering thus leads to a generic modelling framework, which, by a principle of instantiation, allows the construction of an IVHM system designed for the health management of individual vehicle systems. Towards such particularization, the thesis investigates characteristics impacting selection of appropriate supporting algorithms. This analysis enables to identify ten generic macro-criteria, which are further formalized based on ontologies and used within a multi-criteria based methodology suited for selecting diagnostics and prognostics algorithms for vehicle health management. Finally, the validation protocol of the scientific contributions is proposed, and applied at different scales of implementation in the field of wind turbine and UAV health management

Purpose – The research purpose is to enhance knowledge of how organizations can form a PSS through an IVHM system when information is sensitive. Method – A single case study design of abductive approach was used, with data collection through six semi-structured interviews. Findings – A system combining IVHM and PSS has many potential benefits, however there are several challenges that need to be overcome in order to implementing a successful model. Theoretical implications – This study treads a new area not previously explored in the literature when it combines PSS and IVHM, which relies heavily on information flow to succeed, with a case of information sensitivity. This study hence explores a problematic area for either PSS or IVHM, expanding the current literature and providing initial suggestions of how to navigate this. Practical implications – Firstly, it shows managers the challenges that comes with implementing PSS-IVHM and increasing involvement in the customers’ processes. Secondly, this study shows the theoretical and general challenges of PSS-IVHM and applies the case study’s perspective of information management, granting managers a larger foundation of knowledge before starting their initiatives of PSS-IVHM. Limitations and future research – This study provides a limited amount of empirical data. Therefore, future research should focus on increasing and widening data collection. The study suggests there is a considerable challenge in conservatism within the defence industry and therefore future research is suggested to explore how change management can combat this challenge.

Yes / Advancing Industry 4.0 concepts by mapping the product of the automotive industry on the spectrum of Cyber Physical Systems, we immediately recognise the convoluted processes involved in the design of new generation vehicles. New technologies developed around the communication core (IoT) enable novel interactions with data. Our framework employs previously untapped data from vehicles in the field for intelligent vehicle health management and knowledge integration into design. Firstly, the concept of an inter-disciplinary artefact is introduced to support the dynamic alignment of disparate functions, so that cyber variables change when physical variables change. Secondly, the axiomatic categorisation (AC) framework simulates functional transformations from artefact to artefact, to monitor and control automotive systems rather than components. Herein, an artefact is defined as a triad of the physical and engineered component, the information processing entity, and communication devices at their interface. Variable changes are modelled using AC, in conjunction with the artefacts, to aggregate functional transformations within the conceptual boundary of a physical system of systems. / Jaguar Land Rover funded research “Intelligent Personalised Powertrain Healthcare” 2016-2019