Vehicle Health Management Systems 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.

Short-term maintenance planning of autonomous trucks for minimizing economic risk

New autonomous driving technologies are emerging every day and some of them have been commercially applied in the real world. While benefiting from these technologies, autonomous trucks are facing new challenges in short-term maintenance planning, which directly influences the truck operator’s profit. In this paper, we implement a vehicle health management system by addressing the maintenance planning issues of autonomous trucks on a transport mission. We also present a maintenance planning model using a risk-based decision-making method, which identifies the maintenance decision with minimal economic risk of the truck company. Both availability losses and maintenance costs are considered when evaluating the economic risk. We demonstrate the proposed model by numerical experiments illustrating real-world scenarios. In the experiments, compared to three baseline methods, the expected economic risk of the proposed method is reduced by up to 47%. We also conduct sensitivity analyses of different model parameters. The analyses show that the economic risk significantly decreases when the estimation accuracy of remaining useful life, the maximal allowed time of delivery delay before order cancellation, or the number of workshops increases. The experiment results contribute to identifying future research and development attentions of autonomous trucks from an economic perspective.

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.

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.

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.

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.

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.

Using Big-data and Surface Fitting to Improve Aircraft Safety Through the Study of Relationships and Anomalies

The aim of this paper is to assess the utility of a Big-Data approach to fault detection for ‘systems of systems’, utilising the derivation of empirical relationships identified through surface fitting. So-called Big-Data Integrated Vehicle Health Management systems do currently exist, but tend to analyse the health of vehicle systems based on the behaviour of individual sensors and readings. This paper proposes that it is possible to consider vehicle systems with a ‘macro’ approach and identify relationships between key variables which may not be initially apparent. Used in this paper is the open source flight simulation software FlightGear which has previously been assessed for the development of fault detection systems with positive results. The relationships found can be combined into a model of expected results against which real-time data is tested. Surface fitting and the assessment of ‘goodness of fit’ is used to identify these relationships. It is proposed that this technique need not be limited to fault detection in vehicle systems but is also applicable to other vital systems which require redundancy and constant health analysis. This paper concludes that this method is a viable approach and that relationships can be successfully identified for fault detection purposes.

Integrated Vehicle Health Management System for Fighter Aircraft

Integrated Vehicle Health Management System for Fighter Aircraft

Lesson Learned in the Research and Development of Specialized Vehicle Health Management System: A Hybrid Approach for Decision Support System

The reality is that PHM is mostly a matter of the analytic method and process of building tailored and hybrid functional DSS to meet various requirements in field of maintenance and logistics support, not only creating models and algorithms in black box which is philosophical basis of so many PHM universal platforms, especially in the concept and design stage of systems. Some issues under discussions are presented firstly. Then, the critical cases analysis method is described, and the demonstration on the different level of specialized vehicle health management system is shown as human-computer interfaces. Lastly, the preliminary design approach to implement PHM system is presented. In addition, potential difficult points in practice are investigated for R&D of PHM.

Overview of Vehicle Integrated Propulsion Research (VIPR) Testing

Maintaining safe operation of an aircraft is a major emphasis of the NASA Aviation Safety Program [1]. An Integrated Vehicle Health Management (IVHM) system aims to maintain vehicle health through detection, diagnostics, state awareness, prognostics, and lastly, mitigation of detrimental situations for each of the vehicle subsystems and throughout the vehicle as a whole [2]. Major subsystems associated with a vehicle wide IVHM system are the airframe, avionics, and propulsion systems. Each subsystem has its own challenges associated with development of IVHM technologies. One particular challenging subsystem for vehicle health management is related to Propulsion Health Management (PHM). The engine is a complex system with a range of operating components whose failure can affect passenger safety. Reliable operation of these engines is critical for aircraft safety. The harsh environment conditions within an engine often present significant challenges for the integration and application of health management systems. In parallel, diagnostic systems often have to perform evaluations with limited sensor information, while evaluating a complex system whose components include gas path, turbomachinery, hydraulics, and other components. Demonstration of Propulsion Health Management technologies is critical for their commercial acceptance. A major aspect of ongoing NASA Propulsion Health Management work is demonstration of these maturing technologies on an operational engine in series of tests called Vehicle Integrated Propulsion Research, or VIPR. This paper gives a brief overview of the VIPR series of tests, the technologies involved in this testing, and how this approach is being used to advance the commercialization and acceptance of these technologies. VIPR is a means to test and evaluate emerging health management technologies on a commercial engine, incorporating new sensors directly on the engine, providing seeded fault scenarios, and evaluating advances in engine diagnostics. This step is critical in order for IVHM technologies to mature from lab work and simulation demonstrations needed for industry acceptance. This work is in partnership with the Air Force, which has provided access to two F117 high bypass turbofan engines and time on operational planes for the ground-based, on-board engine tests. In these tests, the research engines are instrumented to achieve NASA and partner goals and mounted on a C-17 aircraft, though it is important to note that the planes are grounded and do not take flight. A series of tests are planned with overall Vehicle Systems Safety Technologies (VSST) project objectives of testing health management sensors, sensor systems and algorithms on a high bypass turbofan engine under the following conditions: (1) Normal engine operations; (2) Seeded mechanical faults; (3) Seeded gas path faults; and (4) Accelerated engine life degradation through volcanic ash ingestion testing. The first VIPR test took place in December 2011 at NASA Dryden Flight Center/Edwards Air Force Base as an on-wing ground test on a heavily instrumented high bypass turbofan engine. This test featured: 1. Emission Sensor System: Investigation of the ability of of an array of chemical sensors to detect a simulated oil leak and measure changes in sensor response with changing engine conditions.2. Self Diagnostic Accelerometer feasibility test: Demonstrate operability of self-diagnostic accelerometer in aircraft engine environment3. Validate Gas Path Diagnostics: Test analytical model predictions of engine parameters in the presence of bleed valve faults (failed, full open; schedule bias) An unique feature of this VIPR series is an upcoming test, which includes the planned ingestion of volcanic ash into an engine on-board an aircraft. The primary objectives of this work are to determine the effect on the engine of several hours of exposure to low to moderate ash concentrations and to evaluate the capability of engine health management technologies for detecting these effects. Numerous observations and measurements of the engine’s performance and degradation will be made during the course of the experiment. While not intended to be sufficient for rigorous certification of engine performance when ash is ingested, the experiment should provide useful information to aircraft manufacturers, airline operators, and military and civil regulators in their efforts to evaluate the range of risks that ash hazards pose to aviation. A research team of U.S. Government agencies and engine manufacturers are involved in this VIPR testing. This government and industry partnership is not only crucial in enabling the tests, but is also a method by which various organizations can be involved in the demonstration and maturation of technologies, facilitating potential future acceptance. A conclusion of this paper is that partnerships between developers and users of new technologies are crucial towards enabling technology commercialization and acceptance. Not only does this type of work allow the ability to mature and evaluate the technologies in environments reflective of potential application, interaction with potential technology users can provide feedback on pathways towards technology improvements and possible implementation.

Unbalance localization through machine nonlinearities using an artificial neural network approach

Excessive levels of unbalance in rotating machinery continue to contribute to machine downtime and unscheduled and costly maintenance actions. While unbalance as a rotor dynamic fault has been studied in great detail during the last century, the localization of unbalance within a complex rotating machine is today often performed in practice using little more than ‘rules of thumb’. In this work, unbalance faults have been localized through a data driven approach applied to a rotor dynamic test rig fitted with multiple discs. Sub-synchronous nonlinear features in the frequency domain have been identified and studied as a method of aiding the localization of unbalance faults, particularly in situations where sensor placement options are limited. The process of automating the localization has been achieved using an artificial neural network (ANN), and the addition of rub and misalignment faults in the study have been used in order to validate the performance of the system. The results of the study are discussed from the perspective of next-generation integrated vehicle health management (IVHM) systems for rotating machines.

Software Framework for Prototyping Embedded Integrated Vehicle Health Management Applications

Integrated vehicle health management is a major component in a new, future fleet-management paradigm where a conscious effort is made to shift aircraft maintenance from a schedule-based approach to a more proactive and predictive approach (that is, condition-based maintenance). Its goal is to maximize fleet operational availability while minimizing the logistics footprint through monitoring of the deterioration of equipment conditions. A comprehensive integrated vehicle health-management system will be executed in an environment that includes different sensor technologies, multiple information systems, and different data models. Integrated vehicle healthmanagement implementers have to therefore deal with an integration problem that involves different specialized algorithms and embedded hardware platforms. Integrated vehicle health management applications will have common execution logic, and many will share the same data-processing algorithms; hence, development productivity and the quality of integrated vehicle health management applications can be increased through reusable software building blocks and algorithm libraries or, in particular, by using a software development framework. This paper presents an approach to distributed integrated vehicle health management systems that offers reusable software architecture for a class of integrated vehicle health management applications. The focus of this paper deals with an open software framework for development of integrated vehicle health management applications stemming from the open system architecture for a condition-based maintenance specification, which is an architecture-promoting interoperability and a component framework that enables reuse, data-process partitioning, configuration, and rapid prototyping. The framework is developed using Java and Internet communications engines’ distributed middleware, and its application is demonstrated through a gearbox health-monitoring system, where the integrated vehicle health management software is deployed on the distributed embedded devices. This approach provides software-enabled capability to distribute/reconfigure the integrated vehicle health management data process (through the open system architecture for condition-based maintenance common interface and data model) across the hardware platforms to meet the given system configuration. The performance evaluation for the test example shows negligible overhead in the central processing unit, bandwidth, and latency when using the framework.

Rotordynamic Faults: Recent Advances in Diagnosis and Prognosis

Diagnosis and condition monitoring in rotating machinery has been a subject of intense research for the last century. Recent developments indicate the drive towards integration of diagnosis and prognosis algorithms in future integrated vehicle health management (IVHM) systems. With this in mind, this paper concentrates on highlighting some of the latest research on common faults in rotating machines. Eight key faults have been described; the selected faults include unbalance, misalignment, rub/looseness, fluid-induced instability, bearing failure, shaft cracks, blade cracks, and shaft bow. Each of these faults has been detailed with regard to sensors, fault identification techniques, localization, prognosis, and modeling. The intent of the paper is to highlight the latest technologies pioneering the drive towards next-generation IVHM systems for rotating machinery.

Integrating nondestructive inspections with autonomic logistics and structural health monitoring strategies for aeronautic systems

In recent years, there has been much activity concerning health management of aviation systems. Many government- and industry-based research projects/programs are focused on integrated vehicle health management strategies that include sensor development for automated or manual fault detection, diagnostics, prognostics to define remaining useful life, as well as mitigation plans for continued safe operation during a fault. The overall objectives for such activities include increased safety as well as efficient and economic operation of an aircraft fleet. Currently, to assess structural faults, visual inspections and/or nondestructive inspection techniques are utilized as a required routine for certain design and life management strategies. The inspections are manually involved and are carried out during aircraft downtime (e.g. on the runway or nightly hangar maintenance) or during a more extensive aircraft overhaul. Even with the potential of automated structural health monitoring within an integrated vehicle health management system, it is foreseen that manual and nondestructive inspections will continue to be an integral part of fleet life management. This article addresses and clarifies the role of nondestructive inspections in aircraft life management and its potential inclusion in future integrated vehicle health management systems.

Autonomous safety and reliability features of the K-1 avionics system

Kistler Aerospace Corporation is developing the K-1, a fully reusable, two-stage-to-orbit launch vehicle. Both stages return to the launch site using parachutes and airbags. Initial flight operations will occur from Woomera, Australia. K-1 guidance is performed autonomously. Each stage of the K-1 employs a triplex, fault tolerant avionics architecture, including three fault tolerant computers and three radiation hardened Embedded GPS/INS units with a hardware voter. The K-1 has an Integrated Vehicle Health Management (IVHM) system on each stage residing in the three vehicle computers based on similar systems in commercial aircraft. During first-stage ascent, the IVHM system performs an Instantaneous Impact Prediction (IIP) calculation 25 times per second, initiating an abort in the event the vehicle is outside a predetermined safety corridor for at least 3 consecutive calculations. In this event, commands are issued to terminate thrust, separate the stages, dump all propellant in the first-stage, and initiate a normal landing sequence. The second-stage flight computer calculates its ability to reach orbit along its state vector, initiating an abort sequence similar to the first stage if it cannot. On a nominal mission, following separation, the second-stage also performs calculations to assure its impact point is within a safety corridor. The K-1's guidance and control design is being tested through simulation with hardware-in-the-loop at Draper Laboratory. Kistler's verification strategy assures reliable and safe operation of the K-1.