Browsing by Author "Wang, Chengwei"

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    Digital Twin-Based Health Management for Complex Aircraft Systems: Case Studies and Applications
    (IEEE, 2025-03-24) Wang, Chengwei; Fan, Ip-Shing; Plastropoulos, Angelos
    Digital Twin technology, initially conceptualized during the NASA's Apollo program, has evolved into a transformative tool for system health management, particularly in aviation. By integrating high-fidelity simulations, real-time sensor data, and predictive analytics, DTs enable significant innovation in Prognostics and Health Management methods. This paper explores the application of DTs in health management for complex aircraft systems, focusing on two critical subsystems: Flight Control Electrical Actuators and Main Landing Gear. Leveraging MATLAB Simscape, modular DT frameworks were developed to simulate these systems under nominal, degraded, and fault conditions. The inclusion of fault injection models enables the generation of realistic datasets to support predictive maintenance, alleviating difficulties in data availability. Two case studies are presented to illustrate the potential of DT-based approaches to reduce downtime, optimizing performance, and enhancing system reliability. This paper provides a comparative analysis of existing DT tools, highlighting their capabilities and limitations in aerospace contexts. While platforms such as MATLAB Simulink and ANSYS Twin Builder offer robust modeling capabilities, operational tools like AVIATAR and IBM Maximo excel in fleet management and predictive analytics. This comparison highlights the need for tailored DT solutions that balance real-time capabilities, scalability, and configurability. This study contributes to the growing body of knowledge on DT technology, offering insights into its role in enhancing aviation safety, efficiency, and sustainability. It serves as a guide for applying DT-based health management, paving the way for broader adoption in next-generation aerospace systems.
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    Failures mapping for aircraft electrical actuation system health management
    (PHM Society, 2022-06-29) Wang, Chengwei; Fan, Ip-Shing; King, Stephen
    This paper presents the different types of failure that may occur in flight control electrical actuation systems. Within an aircraft, actuation systems are essential to deliver physical actions. Large actuators operate the landing gears and small actuators adjust passenger seats. As developing, aircraft systems have become more electrical to reduce the weight and complexity of hydraulic circuits, which could improve fuel efficiency and lower NOx emissions. Electrical Actuation (EA) are one of those newly electrified systems. It can be categorized into two types, Electro-Hydraulic Actuation (EHA) and Electro-Mechanical Actuation (EMA) systems. Emerging electric and hydrogen fuel aircraft will rely on all-electric actuation. While electrical actuation seems simpler than hydraulic at the systems level, the subsystems and components are more varied and complex. The aim of the overall project is to develop a highly representative Digital Twin (DT) for predictive maintenance of electrical flight control systems. A comprehensive understanding of actuation system failure characteristics is fundamental for effective design and maintenance. This research focuses on the flight control systems including the ailerons, rudders, flaps, spoilers, and related systems. The study uses the Cranfield University Boeing 737 as the basis to elaborate the different types of actuators in the flight control system. The Aircraft Maintenance Manual (AMM) provides a baseline for current maintenance practices, effort, and costs. Equivalent EHA and EMA to replace the 737 systems are evaluated. In this paper, the components and their failure characteristics are elaborated in a matrix. The approach to model these characteristics in DT for aircraft flight control system health management is discussed. This paper contributes to the design, operation and support of aircraft systems.
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    A review of digital twin for vehicle predictive maintenance system
    (Society of Automotive Engineers, 2023-03-07) Wang, Chengwei; Fan, Ip-Shing; King, Stephen
    The development of Digital Twin (DT) has become popular. A dominant description of DT is that it is a software representation that mimics a physical object to portray its real-world performance and operating conditions of an asset. It uses near real-time data captured from the asset and enables proactive optimal operation decisions. There are many other definitions of DT, but not many explicit evaluations of DT performance found in literature. The authors have an interest to investigate and evaluate the quality and stability of appropriate DT techniques in real world aircraft Maintenance, Repair, and overhaul (MRO) activities. This paper reviews the origin of DT concept, the evolution and development of recent DT technologies. Examples of DTs in aircraft systems and transferable knowledge in related vehicle industries are collated. The paper contrasts the benefits and bottlenecks of the two categories of DT methods, Data-Driven (DDDT) and Model-Based (MBDT) models. The paper evaluates the applicability of the two models to represent vehicle system management. The authors present their methodological approach on Predictive Maintenance (PM) development basing on reliable DT models for vehicle systems. This paper contributes to design, operation, and support of aircraft/vehicle systems.
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    Wireless data transfer system architecture for predictive maintenance of aircraft landing gear
    (AIAA, 2025-01-06) Skaltsis, Georgios Marios; Wang, Chengwei; Plastropoulos, Angelos; Fan, Ip-Shing; Avdelidis, Nicolas P.
    In this paper, we describe the data transfer techniques used so far for landing gear andother critical aircraft systems predictive maintenance applications, as well as the available wireless data transfer techniques. Moreover, the architecture of a wireless transfer system forsending aircraft’s landing gear sensory data to a ground station is described, proposing the main guidelines in terms of middleware development and data security and fidelity.