Integrated Vehicle Health Management Research Articles

Platform health management for aircraft maintenance – a review

Aircraft health management has been researched at both component and system levels. In instances of certain aircraft faults, like the Boeing 777 fuel icing problem, there is evidence suggesting that a platform approach using an Integrated Vehicle Health Management (IVHM) system could have helped detect faults and their interaction effects earlier, before they became catastrophic. This paper reviews aircraft health management from the aircraft maintenance point of view. It emphasizes the potential of a platform solution to diagnose faults, and their interaction effects, at an early stage. The paper conducts a thorough analysis of existing literature concerning maintenance and its evolution, delves into the application of Artificial Intelligence (AI) techniques in maintenance, explains the rationale behind their employment, and illustrates how AI implementation can enhance fault detection using platform sensor data. Further, it discusses how computational severity and criticality indexes (health indexes) can potentially be complementary to the use of AI for the provision of maintenance information on aircraft components, for assisting operational decisions.

Understanding the Role of Sensor Optimisation in Complex Systems.

Complex systems involve monitoring, assessing, and predicting the health of various systems within an integrated vehicle health management (IVHM) system or a larger system. Health management applications rely on sensors that generate useful information about the health condition of the assets; thus, optimising the sensor network quality while considering specific constraints is the first step in assessing the condition of assets. The optimisation problem in sensor networks involves considering trade-offs between different performance metrics. This review paper provides a comprehensive guideline for practitioners in the field of sensor optimisation for complex systems. It introduces versatile multi-perspective cost functions for different aspects of sensor optimisation, including selection, placement, data processing and operation. A taxonomy and concept map of the field are defined as valuable navigation tools in this vast field. Optimisation techniques and quantification approaches of the cost functions are discussed, emphasising their adaptability to tailor to specific application requirements. As a pioneering contribution, all the relevant literature is gathered and classified here to further improve the understanding of optimal sensor networks from an information-gain perspective.

Trend in tropopause warming and its influence aircraft performance

Purpose Temperature anomalies in the upper troposphere have become a reality as a result of global warming, which has a noticeable impact on aircraft performance. The purpose of this study is to investigate the total air temperature (TAT) anomaly observed during the cruise level and its impact on engine parameter variations. Design/methodology/approach Empirical methodology is used in this study, and it is based on measurements and observations of anomalous phenomena on the tropopause. The primary data were taken from the Boeing 747-8F's enhanced flight data recorder, which refers to the quantitative method, while the qualitative method is based on a literature review and interviews. The GEnx Integrated Vehicle Health Management system was used for the study's evaluation of engine performance to support the complete range of operational priorities throughout the entire engine lifecycle. Findings The study's findings indicate that TAT and SAT anomalies, which occur between 270- and 320-feet flight level, have a substantial impact on aircraft performance at cruise altitude and, as a result, on engine parameters, specifically an increase in fuel consumption and engine exhaust gas temperature values. The TAT and Ram Rise anomalies were the focus of the atmospheric deviations, which were assessed as major departures from the International Civil Aviation Organizations–defined International Standard Atmosphere, which is obvious on a positive tendency and so goes against the norms. Research limitations/implications Necessary fixed flight parameters gathered from the aircraft's enhanced airborne flight recorder (EAFR) via Aeronautical Radio Incorporated (ARINC) 664 Part 7 at a certain velocity and altitude interfacing with the diagnostic program direct parameter display (DPD), allow for analysis of aircraft performance in a real-time frame. Thus, processed data transmits to the ground maintenance infrastructure for future evaluation and for proper maintenance solutions. Originality/value A real-time analysis of aircraft performance is possible using the diagnostic program DPD in conjunction with necessary fixed flight parameters obtained from the aircraft's EAFR via ARINC 664 Part 7 at a specific speed and altitude. Thus, processed data is transmitted to the ground infrastructure for maintenance to be evaluated in the future and to find the best maintenance fixes.

A Review of Diagnostic Methods for Hydraulically Powered Flight Control Actuation Systems

Aircraft systems are designed to perform functions that will aid the various missions of the aircraft. Their performance, when subjected to an unfamiliar condition of operation, imposes stress on them. The system components experience degradation due to fault which ultimately results in failure. Maintenance and monitoring mechanisms are put in place to ensure these systems are readily available when required. Thus, the sensing of parameters assists in providing conditions under which healthy and faulty scenarios can be indicated. To obtain parameter values, sensor data is processed, and the results are displayed so that the presence of faults may be known. Some faults are intermittent and incipient in nature. These are not discovered easily and can only be known through a display of unusual system performance by error code indication. Therefore, the assessed faults are transmitted to a maintenance crew by error codes. The results may be fault found (FF), no fault found (NFF), or cannot display (CND). However, the main classification of the faults and their origins may not be known in the system. This continues throughout the life cycle of the system or equipment. This paper reviews the diagnostic methods used for the hydraulically powered flight control actuation system (HPFCAS) of an aircraft and its interaction with other aircraft systems. The complexities of the subsystem’s integration are discussed, and different subsystems are identified. Approaches used for the diagnostics of faults, such as model-based, statistical mapping and classification, the use of algorithms, as well as parity checks are reviewed. These are integrated vehicle health management (IVHM) tools for systems diagnostics. The review shows that when a system is made up of several subsystems on the aircraft with dissimilar functions, the probability of fault existing in the system increases, as the subsystems are interconnected for resource sharing, space, and weight savings. Additionally, this review demonstrates that data-driven approaches for the fault diagnostics of components are good. However, they require large amounts of data for feature extraction. For a system such as the HPFCAS, flight-management data or aircraft maintenance records hold information on performance, health monitoring, diagnostics, and time scales during operation. These are needed for analysis. Here, a knowledge of training algorithms is used to interpret different fault scenarios from the record. Thus, such specific data are not readily available for use in a data-driven approach, since manufacturers, producers, and the end users of the system components or equipment do not readily distribute these verifiable data. This makes it difficult to perform diagnostics using a data-driven approach. In conclusion, this paper exposes the areas of interest, which constitute opportunities and challenges in the diagnostics and health monitoring of flight-control actuation systems on aircraft.

The Rise of the Conscious Aircraft

Advances in technology could enable a new generation of conscious aircraft able to anticipate problems and book themselves in for maintenance write Professor Ian Jennions and Jim Angus from Cranfield’s Integrated Vehicle Health Management (IVHM) Centre

Digital Simulation and Identification of Faults with Neural Network Reasoners in Brushed Actuators Employed in an E-Brake System

The aerospace industry is constantly looking to adopt new technologies to increase the performance of the machines and procedures they employ. In recent years, the industry has tried to introduce more electric aircraft and integrated vehicle health management technologies to achieve various benefits, such as weight reduction, lower fuel consumption, and a decrease in unexpected failures. In this experiment, data obtained from the simulation model of an electric braking system employing a brushed DC motor is used to determine its health. More specifically, the data are used to identify faults, namely open circuit fault, intermittent open circuit, and jamming. The variation of characteristic parameters during normal working conditions and when faults are encountered are analysed qualitatively. The analysis is used to select the features that are ideal to be fed into the reasoner. The selected features are braking force, wheel slip, motor temperature, and motor angular displacement, as these parameters have very distinct profiles upon injection of each of the faults. Due to the availability of clean data, a data-driven approach is adopted for the development of the reasoner. In this work, a Long Short-Term Memory Neural Network time series classifier is proposed for the identification of faults. The performance of this classifier is then compared with two others—K Nearest Neighbour time series and Time Series Forest classifiers. The comparison of the reasoners is then carried out in terms of accuracy, precision, recall and F1-score.

Development and Implementation of a Framework for Aerospace Vehicle Reasoning (FAVER)

This paper discusses the development and implementation of the architecture of a Framework for Aerospace Vehicle Reasoning, `FAVER'. Integrated Vehicle Health Management systems require a holistic view of the aircraft to isolate faults cascading between aircraft systems. FAVER is a system-agnostic framework developed to isolate such propagating faults by incorporating Digital Twins (DTs) and reasoning techniques. The flexibility of FAVER to work with different types and scales of DTs and diagnostics, and its ability to adapt and expand for previously unknown faults and new systems are demonstrated in this paper. The paper also shows the novel combination of relationship matrix and fault attributes database used to structure the knowledge of FAVER's expert system. The paper provides the working mechanism of FAVER's reasoning and its ability to isolate faults at the system level, identify their root causes, and predict the cascading effects at the vehicle level. Four aircraft systems are used for demonstration purposes: i) the Electrical Power System, ii) the Fuel System, iii) the Engine, and iv) the Environmental Control System, and the use case scenarios are adapted from real aircraft incidents. The paper also discusses the pros and cons of FAVER's reasoning via demonstrations and evaluates the performance of FAVER's reasoning through a comparative study with a supervised neural network model.

Real-Time Maintenance Optimization Considering Health Monitoring and Additive Manufacturing

Prognostics Health Management (PHM) and Integrated Vehicle Health Management (IVHM) are extensive areas of research. Whereas a lot of work has been done in diagnostics and prognostics, economic viability is an important consideration. The availability of aircraft in the aerospace sector is a critical factor. Thus, cost and downtime are the main parameters to assess the impact of IVHM. Additionally, new repair technologies, such as additive manufacturing (AM), have the potential to become standard repair procedures, complementing IVHM, and its viability also has to be assessed. However, to accurately study the impact of these factors, the characteristics of aerospace maintenance have to be taken into account. Several approaches are followed in aircraft maintenance, depending on cost, downtime and aircraft availability constraints. For instance, some parts can be repaired on the ground and assembled again on the same aircraft, while single Line Replaceable Units (LRUs) need to be removed, replaced and later repaired in the workshop without affecting the availability of the aircraft. With the gradual introduction of IVHM, the viability of any new IVHM technology needs to be assessed.This paper describes an extensive cost and downtime model to take into account all these scenarios, including the impact of using different types of IVHM systems. The impact of IVHM and new repair technologies is discussed comparing maintenance cost and downtime of parts of LRUs and parts repaired when the aircraft is on the ground.Secondly, a real-time maintenance case study based on IVHM, a cost and downtime model and additive manufacturing is presented. This application allows the optimization of maintenance activities by updating the available resources and their corresponding cost and time, along with the actual prediction of the Remaining Useful Life (RUL) using a health monitoring system, instead of depending on historical component/sub-system failure probabilities.

Integrating IVHM and Asset Design

Integrated Vehicle Health Management (IVHM) describes a set of capabilities that enable effective and efficient maintenance and operation of the target vehicle. It accounts for the collecting of data, conducting analysis, and supporting the decision-making process for sustainment and operation. The design of IVHM systems endeavours to account for all causes of failure in a disciplined, systems engineering, manner. With industry striving to reduce through-life cost, IVHM is a powerful tool to give forewarning of impending failure and hence control over the outcome. Benefits have been realised from this approach across a number of different sectors but, hindering our ability to realise further benefit from this maturing technology, is the fact that IVHM is still treated as added on to the design of the asset, rather than being a sub-system in its own right, fully integrated with the asset design. The elevation and integration of IVHM in this way will enable architectures to be chosen that accommodate health ready sub-systems from the supply chain and design trade-offs to be made, to name but two major benefits. Barriers to IVHM being integrated with the asset design are examined in this paper. The paper presents progress in overcoming them, and suggests potential solutions for those that remain. It addresses the IVHM system design from a systems engineering perspective and the integration with the asset design will be described within an industrial design process.

Preliminary study towards the definition of a PHM framework for the hydraulic system of a fly-by-wire helicopter

On-board hydraulic systems are tasked to provide a number of critical functions to ensure the in-flight operability of rotary-wings vehicles; the hydraulic plant is required to supply power to the flight control actuators and utilities, as well as condition the hydraulic fluid, under a wide range of possible in-service conditions. Being a flight-critical system, the definition of a Prognostics and Health Management framework would provide significant advantages to the users, such as better risk mitigation, improved availability and a reduction in the occurrences of unpredicted failures which still represent one of the more known downsides of helicopters. A preliminary analysis on the effects of the inception and progression of several degradation types is the first step towards assessing if such PHM system is feasible, and which failure modes are more likely to be observed. Moreover, since several key components are frequently provided by different suppliers to the airframer, this preliminary analysis would allow to better assess if an Integrated Vehicle Health Management approach, integrating signals coming from different components, could be beneficial. To pursue this study, a complete model of the hydraulic system for a fly-by-wire helicopter has been prepared. Then, an in-depth simulation campaign was pursued with the aim of studying the interactions between different failure modes, the effects that the propagating degradations have on the system performances and which signals can be used to define a robust set of features. The paper introduces the case-study under analysis, a general configuration for fly-by-wire helicopters, presenting the most prominent peculiarities of the system and the effect of such peculiarities on the definition of health monitoring schemes. The model is then used to describe the behavior of the system under nominal and degraded conditions is introduced. Between the possible failure modes, the interaction between wear in several mechanical components and the occlusion of the hydraulic lines filters was chosen as the subject of this study; motivations are provided and the degradation model described in detail. Hence, results of a wide-ranging simulation campaign are presented, where the time-domain response of the system is used to guide in the definition of a proper set of features able to characterize the selected fault cases. Selected features are presented, chosen according to significant metrics such as correlation with the simulated degradations, signal-to-noise ratio and accuracy. Two different approaches with a varying degree of integration between system signals are proposed and compared. Prognostics is then pursued through well-known particle filter algorithms. The analysis provides promising results on the capability of successfully detecting, isolating and identifying the selected fault mode; laying the foundations for further and more comprehensive studies on the subject.

Vehicular Sensor Network and Data Analytics for a Health and Usage Management System.

Automated collection of on-vehicle sensor data allows the development of artificial intelligence (AI) techniques for vehicular systems’ diagnostic and prognostic processes to better assess the state-of-health, predict faults and evaluate residual life of ground vehicle systems. One of the vital subsystems, in terms of safety and mission criticality, is the power train, (comprising the engine, transmission, and final drives), which provides the driving torque required for vehicle acceleration. In this paper, a novel health and usage monitoring system (HUMS) architecture is presented, together with dedicated diagnosis/prognosis algorithms that utilize data gathered from a sensor network embedded in an armoured personnel carrier (APC) vehicle. To model the drivetrain, a virtual dynamometer is introduced, which estimates the engine torque output for successive comparison with the measured torque values taken from the engine control unit. This virtual dynamometer is also used in conjunction with other sensed variables to determine the maximum torque output of the engine, which is considered to be the primary indicator of engine health. Regression analysis is performed to capture the effect of certain variables such as engine hours, oil temperature, and coolant temperature on the degradation of maximum engine torque. Degradations in the final drives system were identified using a comparison of the temperature trends between the left-hand and right-hand final drives. This research lays foundations for the development of real-time diagnosis and prognosis functions for an integrated vehicle health management (IVHM) system suitable for safety critical manned and unmanned vehicle applications.

Opportunities for Explainable Artificial Intelligence in Aerospace Predictive Maintenance

This paper aims to look at the value and the necessity of XAI (Explainable Artificial Intelligence) when using DNNs (Deep Neural Networks) in PM (Predictive Maintenance). The context will be the field of Aerospace IVHM (Integrated Vehicle Health Management) when using DNNs. An XAI (Explainable Artificial Intelligence) system is necessary so that the result of an AI (Artificial Intelligence) solution is clearly explained and understood by a human expert. This would allow the IVHM system to use XAI based PM to improve effectiveness of predictive model. An IVHM system would be able to utilize the information to assess the health of the subsystems, and their effect on the aircraft. Even if the underlying mathematical principles are understood, they lack an understandable insight, hence have difficulty in generating the underlying explanatory structures (i.e. black box). This calls for a process, or system, that enables decisions to be explainable, transparent, and understandable. It is argued that research in XAI would generally help to accelerate the implementation of AI/ML (Machine Learning) in the aerospace domain, and specifically help to facilitate compliance, transparency, and trust. This paper explains the following areas:
 
 Challenges & benefits of AI based PM in aerospace
 Why XAI is required for DNNs in aerospace PM?
 Evolution of XAI models and industry adoption
 Framework for XAI using XPA (Explainability Parameters)
 Discussion about future research in adopting XAI & DNNs in improving IVHM.

Model-based vehicular prognostics framework using Big Data architecture

Nowadays, the continuous technological advances allow designing novel Integrated Vehicle Health Management (IVHM) systems to deal with strict safety regulations in the automotive field with the aim at improving efficiency and reliability of automotive components. However, challenging issue, which arises in this domain, is handling a huge amount of data that are useful for prognostic. To this aim, in this paper we propose a cloud-based infrastructure, namely Automotive predicTOr Maintenance In Cloud (ATOMIC), for prognostic analysis that leverages Big Data technologies and mathematical models of both nominal and faulty behaviour of the automotive components to estimate on-line the End-Of-Life (EOL) and Remaining Useful Life (EUL) indicators for the automotive systems under investigation. A case study based on the Delphi DFG1596 fuel pump has been presented to evaluate the proposed prognostic method. Finally, we perform a benchmark analysis of the deployment configurations of ATOMIC architecture in terms of scalability and cost.

Implementing Integrated Vehicle Health Management (IVHM) Protocol for Support and Reliability of Digital Project Engineering.

Implementing Integrated Vehicle Health Management (IVHM) Protocol for Support and Reliability of Digital Project Engineering.

Towards an Enhanced Data- and Knowledge Management Capability: A Data Life Cycle Model Proposition for Integrated Vehicle Health Management

The creation, capturing, using and sharing of knowledge is based on data. The rate of data creation, collection, and elicitation through wide range experiments, simulations and measurements is rapidly increasing within Integrated Vehicle Health Management (IVHM). In addition, Knowledge Management (KM), data abstraction, analyses, storage and accessibility challenges persist, resulting in loss of knowledge and increased costs. This growth in the creation of research data, algorithms, technical papers, reports and logs, requires both a strategy and tool to address these challenges. A Data Life Cycle Model (DLCM) ensures the efficient and effective abstraction and management of both data and knowledge outputs. IVHM which depend heavily on high-quality data to perform data-driven, model-based and hybrid computational analysis of asset health. IVHM Centre does not yet have a systematic and coherent approach to its data management. The absence of a DLCM means that valuable knowledge might be lost or is difficult to find. Data visualization is fragmented and done on a project by project basis leading to increased costs. There is insufficient algorithm documentation and communication for easy transition between subsequent researchers and personnel. A systematic review of DLCMs, frameworks, standards and process models pertaining to data- and KM in the context of IVHM, found that there is no DLCM that is consistent with IVHM data and knowledge management requirements. Specifically, there is a need to develop a DLCM based on Open System Architecture for Condition-Based Maintenance framework.

Progress towards a Framework for Aerospace Vehicle Reasoning (FAVER)

This paper proposes a reasoning framework to diagnose faults at the vehicle level in a complex machine like an aircraft. The current focus of Integrated Vehicle Health Management (IVHM) is on diagnosing and prognosing faults at the component and subsystem levels; only a few IVHM systems consider the interaction between the systems. To diagnose faults at the vehicle level, an IVHM System needs a framework that recognizes the causal relationships between systems and the likelihood of fault propagation between them. The framework should also possess an element of reasoning to assess data from all systems, to assign priorities, and to resolve ambiguities. The Framework for Aerospace VEhicle Reasoning (FAVER) that is proposed in this paper uses a digital twin of the aircraft systems to emulate functioning of the aircraft and to simulate the effect of fault propagation due to systems interactions. FAVER applies reasoning that can handle fault signatures from multiple systems in the form of symptom vectors, to detect and isolate cascading faults and their root causes. The blending of a digital twin and reasoning in this framework will enable FAVER to: i) isolate faults that have both local and cascading effects on the concerned systems, ii) identify faults that were previously unknown, and iii) resolve ambiguous faults. This paper explains the different steps involved in developing FAVER and how this framework can be demonstrated in the aforementioned scenarios with the help of different use cases. This paper also talks about the challenges to be faced while developing this framework and ways to overcome them.

Automotive IVHM: Towards Intelligent Personalised Systems Healthcare

AbstractUnderpinned by a contemporary view of automotive systems as cyber-physical systems, characterised by progressively open architectures increasingly defined by their interaction with the users and the smart environment, this paper provides a critical and up-to-date review of automotive Integrated Vehicle Health Management (IVHM) systems. The paper discusses the challenges with prognostics and intelligent health management of automotive systems, and proposes a high-level framework, referred to as the Automotive Healthcare Analytic Factory, to systematically collect and process heterogeneous data from across the product lifecycle, towards actionable insight for personalised healthcare of systems.

Engineering Project Health Management: A Computational Approach for Project Management Support Through Analytics of Digital Engineering Activity

Due to the situational and contextual individuality of engineering work, the in-progress monitoring and assessment of those factors that contribute to the success and performance in a given scenario poses a distinct and unresolved challenge, with heavy reliance on managerial skill and interpretation. Termed engineering project health management (EPHM), this paper presents a novel approach and framework for monitoring of engineering work through data-driven and computational analytics that in turn support the managerial interpretation and generation of higher level, context-specific understanding. EPHM is formed through the first adaptation of integrated vehicle health management (IVHM) to the field of engineering management; an approach that has been used to-date for the machine monitoring and predictive maintenance. The approach is applied to four industrial cases, which demonstrates the generation of project-specific information. The approach thereby acts to increase understanding of an engineering activity and a work state, and is complementary to existing managerial toolsets and approaches. A key tenet of the adaption of IVHM is to place the manager in a central role, supporting their professional judgment while reducing investigative effort.

Developing a Knowledge Management System for Integrated Vehicle Health Management Using a Data Life Cycle Model

Data forms the foundation on which knowledge is created, captured, used and shared. The lack of an approach consistent with technological changes and needs can facilitate loss of knowledge and increased costs. Integrated Vehicle Health Management (IVHM) is characterized by prognostics and diagnostics which depend heavily on high quality data to perform data-driven, model-based and hybrid computational analysis of asset health. As a result, managing data and knowledge for Integrated Vehicle Health Management (IVHM) requires a data life cycle model that adopts the OSA-CBM data model and integrate with other approaches. This project will propose such a model and use it to support the development of an IVHM knowledge management system.

Physics-based integrated vehicle health management system for predicting the remaining useful life of an aircraft planetary gear transmission

PurposeIntegrated vehicle health management has been developed for several years in different industries, to be able to provide the required inputs to determine the optimal maintenance operations depending on the actual health status of the system. The purpose of this paper is to demonstrate the potential of a physics-based model (PbM) for prognostics with a real case study, based on the detection of incipient faults and estimate the remaining useful life of a planetary transmission of an aircraft system.Design/methodology/approachMost of the research in the area of health assessment algorithms has been focused on data-driven approaches that are not based on the knowledge of the physics of the system, while PbM approaches rely on the understanding of the system and the degradation mechanisms. A physics-based modelling approach to represent metal-metal contact and fatigue in the gears of the planetary transmission of an aircraft system is applied.FindingsBoth the failure mode caused by metal-metal contact as caused by fatigue in the gears is described. Furthermore, the real-time application that retrieves the results from the simulations to assess the health of the system is described. Finally the decision making that can be executed during flight in the aircraft is incorporated.Originality/valueThe paper proposes an innovative prognostics health management system that assesses two important failure modes of the planetary transmission that regulates the speed of the generators of an aircraft. The results from the models have been integrated in an application that emulates a real system in the aircraft and computes the remaining useful life in real time.