Browsing by Author "Ezhilarasu, Cordelia Mattuvarkuzhali"

Now showing 1 - 10 of 10
  • Thumbnail Image
    ItemOpen Access
    The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities
    (Elsevier, 2019-01-11) Ezhilarasu, Cordelia Mattuvarkuzhali; Skaf, Zakwan; Jennions, Ian K.
    This paper aims to discuss the importance and the necessity of reasoning applications in the field of Aerospace Integrated Vehicle Health Management (IVHM). A fully functional IVHM system is required to optimize Condition Based Maintenance (CBM), avoid unplanned maintenance activities and reduce the costs inflicted thereupon. This IVHM system should be able to utilize the information from multiple subsystems of the vehicle to assess the health of those subsystems, their effect on the other subsystems, and on the vehicle as a whole. Such a system can only be realized when the supporting technologies like sensor technology, control and systems engineering, communications technology and Artificial Intelligence (AI) are equally advanced. This paper focuses on the field of AI, especially reasoning technology and explores how it has helped the growth of IVHM in the past. The paper reviews various reasoning strategies, different reasoning systems, their architectures, components and finally their numerous applications. The paper discusses the shortcomings found in the IVHM field, particularly in the area of vehicle level health monitoring and how reasoning can be applied to address some of them. It also highlights the challenges faced when the reasoning system is developed to monitor the health at the vehicle level and how a few of these challenges can be mitigated.
  • Thumbnail Image
    ItemOpen Access
    Cross-condition fault diagnosis of an aircraft environmental control system (ECS) by transfer learning
    (MDPI, 2023-12-09) Jia, Lilin; Ezhilarasu, Cordelia Mattuvarkuzhali; Jennions, Ian K.
    Fault diagnosis models based on machine learning are often subjected to degradation in performance when dealing with data that are differently distributed than the training data. Such an occasion is common in reality because machines usually operate under various conditions. Transfer learning is a solution for the performance degradation of cross-condition fault diagnosis problems. This paper studies how transfer learning algorithms transfer component analysis (TCA) and joint distribution alignment (JDA) improve the cross-condition fault diagnosis accuracy of an aircraft environmental control system (ECS). Both methods work by transforming the source and target domain data into a feature space where their distributions are aligned to allow a uniform classifier to act accurately in both domains. This paper discovered that both TCA and JDA produce significantly more accurate results than traditional methods on target domains with unlabelled ECS data taken at different operating conditions than the source domain. Additionally, when dealing with unlabelled data from unknown conditions bearing a different composition of classes in the target domain, TCA is found to be more robust and accurate, generating an average predictive accuracy of 95.22%, which demonstrates the ability of transfer learning in solving similar problems in the real-world application of fault diagnosis.
  • Thumbnail Image
    ItemOpen Access
    Development and Implementation of a Framework for Aerospace Vehicle Reasoning (FAVER)
    (IEEE, 2021-07-28) Ezhilarasu, Cordelia Mattuvarkuzhali; Jennions, Ian
    This paper discusses the development and implementation of the architecture of a Framework for A erospace Ve hicle R easoning, ‘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.
  • Thumbnail Image
    ItemOpen Access
    A framework for aerospace vehicle reasoning (FAVER)
    (Cranfield University, 2019-10) Ezhilarasu, Cordelia Mattuvarkuzhali; Jennions, Ian K.; Skaf, Zakwan
    Airliners spend over 9% of their total revenue in Maintenance, Repair, and Overhaul (MRO) and working to bring down the cost and time involved. The prime focus is on unexpected downtime and extended maintenance leading to delays in the flights, which also reduces the trustworthiness of the airliners among the customers. One of the effective solutions to address this issue is Condition based Maintenance (CBM), in which the aircraft systems are monitored frequently, and maintenance plans are customized to suit the health of these systems. Integrated Vehicle Health Management (IVHM) is a capability enabling CBM by assessing the current condition of the aircraft at component/ Line Replaceable Unit/ system levels and providing diagnosis and remaining useful life calculations required for CBM. However, there is a lack of focus on vehicle level health monitoring in IVHM, which is vital to identify fault propagation between the systems, owing to their part in the complicated troubleshooting process resulting in prolonged maintenance. This research addresses this issue by proposing a Framework for Aerospace Vehicle Reasoning, shortly called FAVER. FAVER is developed to enable isolation and root cause identification of faults propagating between multiple systems at the aircraft level. This is done by involving Digital Twins (DTs) of aircraft systems in order to emulate interactions between these systems and Reasoning to assess health information to isolate cascading faults. FAVER currently uses four aircraft systems: i) the Electrical Power System, ii) the Fuel System, iii) the Engine, and iv) the Environmental Control System, to demonstrate its ability to provide high level reasoning, which can be used for troubleshooting in practice. FAVER is also demonstrated for its ability to expand, update, and scale for accommodating new aircraft systems into the framework along with its flexibility. FAVER’s reasoning ability is also evaluated by testing various use cases.
  • Thumbnail Image
    ItemOpen Access
    A generalised methodology for the diagnosis of aircraft systems
    (IEEE, 2021-01-11) Ezhilarasu, Cordelia Mattuvarkuzhali; Skaf, Zakwan; Jennions, Ian K.
    An aircraft is made up of a number of complicated systems which work in harmony to ensure safe and trouble-free flight. In order to maintain such a platform, many diagnostic and prognostic techniques have been suggested, mostly aimed at components but some at the system level. Together these form a patchwork approach to the overall problem of efficiently informing aircraft maintenance to the Original Equipment Manufacturers, the operators /airlines, and the Maintenance, Repair, and Overhaul organisations. It involves these organisations having to support several different approaches to aircraft health management, and is therefore inefficient and costly. In the current work, a streamlined methodology is put forward. This is based on OSA-CBM (Open System Architecture for Condition Based Maintenance) and can be applied to any aircraft system. Integral with this is the use of mRMR (minimum redundancy maximum relevance) for feature selection, the resulting symptom vector being used for fault diagnosis. This approach is demonstrated on three test cases: the engine, the environmental control system, and the fuel system. In each case, the digital twin setup, simulation conditions for healthy and faulty scenarios, a methodology based on OSA-CBM up to diagnostics are detailed. Diagnostics is carried out for each system in turn, using four machine learning supervised algorithms. The best performing algorithm for each system will then subsequently be used in a vehicle level reasoner called FAVER (A Framework for Aerospace Vehicle Reasoning), which requires these system diagnoses as a starting point for vehicle reasoning and fault ambiguity resolution
  • Thumbnail Image
    ItemOpen Access
    An integrated reasoning framework for vehicle level diagnosis of aircraft subsystem faults
    (PHM Society, 2018-09-24) Ezhilarasu, Cordelia Mattuvarkuzhali
    A framework for integrated diagnostic reasoning to detect and isolate faults in complex aircraft systems, at the vehicle level, is proposed. A Digital Twin emulating the functions of an aircraft’s selected subsystems is to be developed; this will include their input/output parameters connecting to other systems, for simulating the subsystem level interactions. The failure propagation across subsystems will be observed by injecting different faults. A diagnostic module for each subsystem will detect and isolate faults. This will be complemented by an integrated reasoner at the vehicle level which will isolate the root cause of propagated faults. On successful completion, the fully developed integrated reasoner shall distinguish an effect (for example, engine power reduction in B777 (Sleight and Carter, 2014)) from its root cause (blocked Fuel Oil Heat Exchanger (Sleight and Carter, 2014)), yielding maintenance savings and increasing dispatch reliability.
  • Thumbnail Image
    ItemOpen Access
    Platform health management for aircraft maintenance – a review
    (Sage, 2024-01-13) Kwakye, Andrews Darfour; Jennions, Ian K.; Ezhilarasu, Cordelia Mattuvarkuzhali
    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.
  • Thumbnail Image
    ItemOpen Access
    Progress towards a Framework for Aerospace Vehicle Reasoning (FAVER)
    (PHM Society, 2019-09-23) Ezhilarasu, Cordelia Mattuvarkuzhali; Skaf, Zakwan; Jennions, Ian
    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.
  • Thumbnail Image
    ItemOpen Access
    A system-level failure propagation detectability using ANFIS for an aircraft electrical power system
    (MDPI, 2020-04-20) Ezhilarasu, Cordelia Mattuvarkuzhali; Jennions, Ian K.
    The Electrical Power System (EPS) in an aircraft is designed to interact extensively with other systems. With a growing trend towards more electric aircraft, the complexity of interactions between the EPS and other systems has grown. This has resulted in an increased necessity of implementing health monitoring methods like diagnosis and prognosis of the EPS at the systems level. This paper focuses on developing a diagnostic algorithm for the EPS to detect and isolate faults and their root causes that occur at the Line Replaceable Units (LRUs) connecting with aircraft systems like the engine and the fuel system. This paper aims to achieve this in two steps: (i) developing an EPS digital twin and presenting the simulation results for both healthy and fault scenarios, (ii) developing an Adaptive Neuro-Fuzzy Inference System (ANFIS) monitor to detect faults in the EPS. The results from the ANFIS monitor are processed in two methods: (i) a crisp boundary approach, and (ii) a fuzzy boundary approach. The former approach has a poor misclassification rate; hence the latter method is chosen to combine with causal reasoning for isolating root causes of these interacting faults. The results from both these methods are presented through examples in this paper.
  • Thumbnail Image
    ItemOpen Access
    Understanding the role of a Digital Twin in Integrated Vehicle Health Management (IVHM)
    (IEEE, 2019-11-28) Ezhilarasu, Cordelia Mattuvarkuzhali; Skaf, Zakwan; Jennions, Ian
    Integrated Vehicle Health Management (IVHM) aims to support Condition-Based Maintenance (CBM) by monitoring, diagnosing, and prognosing the health of the host system. One of the technologies required by IVHM to carry out its objectives is the means to emulate the functioning of the host system, and the concept of a Digital Twin (DT) was introduced in aerospace IVHM to represent the functioning of such a complex system. This paper aims to discuss the role played by DT in the field of IVHM. A DT is the virtual representation of any physical product, that is used to project the functioning of the product at a given instance. The DT is used across the lifecycle of any product, and its output can be customized depending upon the area of application. The DT is currently popular in industry because of the technologies like sensors, cloud computing, Internet of Things, machine learning, and advanced software, which enabled its development. This paper discusses what encompasses a DT, the technologies that support the DT, its applications across industries, and its development in academia. This paper also talks about how a DT can combine with IVHM technology to assess the health of complex systems like an aircraft. Lastly, this paper presents various challenges faced by industry during the implementation of a DT and some of the possible opportunities for future growth.