An airframer’s perspective on SHM opportunities and challenges for commercial airplanes
Vice President and Chief Engineer for Mechanical and Structural Engineering
As Structural Health Monitoring (SHM) has become an increasingly fundamental element of structural airworthiness management and maintenance operations, Boeing has been advancing SHM technologies that can reduce overall fleet life cycle cost by reducing inspection times, improving early detection of flaws when they are more easily repairable, help to expedite repair planning via remote diagnosis, provide greater understanding of as-experienced loads and environments, and potentially enable higher performing structural designs. We will discuss an overview of current Boeing SHM projects as well as future SHM implementation opportunities for commercial airplanes while touching upon major technical and non-technical challenges to overcome.
Structural Health Monitoring – The key enabler of Condition Based Maintenance in aviation
Associate Professor at the Aerospace Engineering Faculty
Delft University of Technology
The Advisory Council for Aeronautical Research in Europe (ACARE) envisages that, by 2050, all new aircraft will be designed for condition-based maintenance (CBM). This will result in a significant 40% reduction in Maintenance Repair & Overhaul (MRO) process time and costs, increase in aircraft availability, and maximization of asset utilization. The backbone of CBM is the continuous monitoring of the aircraft performance utilizing permanently installed sensors. Naturally, Structural Health Monitoring (SHM) plays an essential role on the successful implementation of CBM as it provides the needed information for structural assessment of critical aircraft structures. This paper discusses how SHM fits into the framework of CBM and highlights the results of the European project ReMAP – Real-time CBM for adaptive Aircraft Maintenance Planning. More specifically, the consortium efforts for multi-sensing SHM system integration, data synchronization and information fusion will be presented, while emphasis will be given into the conceptual design of a SHM system that is capable of damage anomaly detection, global location identification, damage type assessment, damage severity and prognostics. Innovative data-driven machine-learning algorithms were developed during the project which enabled health diagnostics and prognostics tasks of primary structures using data collected during tests at lower structural levels. This talk will demonstrate that hierarchical testing of SHM systems and scale-up approaches are a key for putting SHM into practice and for making steps towards CBM.
Fielding a SHM system on an aging military aircraft
Anton Norberg Vooren
Chief Engineer and Head of Systems Engineering
Norwegian Defence Material Agency, Air Systems
Aging military aircraft require an increasing amount of structural inspections to safely maintain them. In a case with the Norwegian SAR helicopter fleet, the need for decreased inspections intervals on two of the main gear box fittings threatened to ground the fleet. The fittings are made of forged aluminum and have a very short critical crack length. The usage of the SAR helicopters have changed over 50 years of use, and after more than 17000 flight hours, crack lengths became critical faster than the OEM recommended inspection intervals. New technologies are available that can provide structural inspection more efficiently but often with the caveats of rigorous certification process or long downtime. Structural Health Monitoring (SHM) systems can now be used as a means of structural inspection in aging aircraft. SHM systems that leverage modern robust sensors and data acquisition hardware combined with powerful signal processing algorithms and fast data transfer mechanisms can detect and track damage over time. This allows for timely, targeted and cost-effective maintenance of the monitored structures with minimal human interaction. This presentation will discuss how the requirements for the operative Norwegian SAR helicopters were met and how SHM systems can be used for other similar aircraft and applications.
Data-Driven Digital Twinning for Structural Health Monitoring
Vivian Church Hoff Professor of Aircraft Structures
James and Anna Marie Spilker Chairman of Aeronautics and Astronautics (Inaugural Holder)
Director, Stanford-King Abdulaziz City of Science and Technology Center of Excellence for Aeronautics and Astronautics
The idea of building a digital replica of a physical platform to optimize in near real-time its operation and/or life cycle management – or more specifically, to perform its structural health monitoring – is not new. It has been the focus of many research efforts during the mid 1990s and early 2000s, particularly in the AIAA and GARTEUR communities, where the idea was intimately linked to the fields of finite element model updating and damage detection. In those days however, a digital replica was primarily limited to a computational model (e.g., a finite element model) – which was recently rebranded as a digital twin prototype (DTP); and model updating was rarely connected to the field of uncertainty quantification (UQ) and performed predominantly using deterministic approaches. Today, the concept of a digital replica has evolved to include the digital twin instance (DTI) – that is, the digital twin of an individual instance of a platform, once it has been manufactured and deployed; and the digital twin aggregate (DTA), which is an aggregation of DTIs that allows for a larger set of data to be collected and processed for interrogation about the physical product. Hence, both DTI and DTA concepts differ from the older DTP concept in their emphasis on data and particularly sensor data. Preliminary forms of such digital twins are often described as the result of the integration of data analytics with the model-based prediction of a few, scalar, quantities of interest (QoIs). This lecture however will first question whether a few QoIs can always be identified to represent the critical state of a newly deployed physical platform in view of monitoring its structural health. Next, it will present a more robust approach for realizing digital twins for structural health monitoring that is based on adaptable, stochastic, low-dimensional but high-fidelity computational models grounded in physics. The proposed approach features novel mathematical ideas for integrating model-form UQ with probabilistic reasoning, projection-based model order reduction, and machine learning. It constructs stochastic, physics-based computational models that self-adapt using information extracted from sensor data; operate in real time; and can be exploited to uncover operational anomalies and/or perform structural health monitoring. Finally, the lecture will demonstrate the potential of the proposed approach for such applications by illustrating it for a maintenance problem pertaining to a mockup fighter aircraft jet.
Embodying Intelligence In Structures For Revolutionary Improvements Of Reliability, Survivability And Maintainability
Program Manager of Mechanics of Multifunctional Materials and Microsystems, Air Force Office of Scientific Research (AFOSR)
In quest for revolutionary improvement in reliability, survivability and maintainability of load-carrying structures, particularly aerospace platforms, our structural health monitoring community increasingly relies on: (i) new design paradigm for “multifunctionality” which aims to achieve judicious combinations of structural performance and specific functional capabilities (e.g. electromagnetic, optical, thermal, chemical) dictated by the system requirements and (ii) “multiscale” integration of newly emerging materials (e.g. active, smart, programmable) as well as nano- and micro-scale devices into macro-scale load-carrying structures and their subsystems. Multifunctional design is often inspired by optimum combinations of properties found in biological systems where the survival of species through millions of years of evolutionary cycles has led to highly efficient design and production of complex materials and systems. Among various visionary contexts for developing such advanced multifunctional structures, the concepts of particular interest are: “autonomic” structures which can sense, diagnose and respond for adjustment with self-learning capabilities (patterned after “brain and neurological” systems), “adaptive” structures allowing reconfiguration or readjustment of shape, functionality and mechanical properties on demand (patterned after “muscular-skeletal” systems), “self-sustaining” systems enabling self-healing, regeneration and self-regulating thermal management capabilities with integrated power sources (patterned after “circulatory” systems). Significant progress has been made by our multifunctional design community for the proof of these concepts through specific case studies of autonomic, adaptive and self-sustaining systems. Well-known examples from earlier work include (i) neurological system-inspired sensory network for structural health monitoring, (ii) self-healing and in-situ repair capabilities for air vehicles as well as space platforms, and (iii) self-regulating thermal management of aerospace structures. The more recent examples of key emerging technologies are: (1) avian-inspired “fly-by-feel” morphing wing for the next generation of air vehicles, and (2) neuromorphic circuits with high-speed parallel signal processing and self-learning capabilities for autonomic navigation of uncrewed air vehicles. Along with other exciting developments in manufacturing technologies as well as simulation methods, these advances place the state of affairs at a tipping point where entirely new classes of multifunctional structures can be designed in multiscale by high-fidelity computational modeling methods and are concurrently fabricated by multi-material additive manufacturing techniques. This overview presentation will review the recent status of technology and address key scientific issues underpinning further advancement of multifunctional materials and structures.
NDT/SHM Meets Digitalization
Managing Director, Testia GmbH in Germany
Co-Founder of the SHM-AISC (Structural Health Monitoring - Aerospace Industry Steering Committee) and CAWG (Commercial Aviation Working Group)
The need for aerospace to be more efficient, carry heavier loads and transport more passengers lead to more innovative approaches like the Fail-Safe and Damage Tolerance Design. To enable such concepts, new and improved NDT techniques were required. It was the time when Eddy Current (ET) appeared and Ultrasonic (UT), Penetrant Testing (PT) and Magnetic Particle Testing (MT) were used more and more. In addition, “fancy” technologies like Thermography (IRT) and Shearography appeared. By utilising such technologies, a more lightweight design was possible and thus, concepts like Initial-Flaw and Damage-Growth started to be the design principles for metal structures. The use and application of CFRP for even lighter structures brought NDT to its next level. In the 1990s, the approach of using permanently installed sensors pushed NDT even further as airlines increasingly demanded weight reductions to improve their cost efficiency. Due to that, Structural Health Monitoring (SHM) was reborn and has since evolved into an alternative to conventional NDT. Surely, digitalization was and is the ultimate measure to drive innovation and opportunities regarding efficient inspection and monitoring of structures, allowing the use of data during the entire product life cycle. Hence, the dream of the NDT/SHM community to use data, generated at any given time during a life cycle as well as available and usable at any time in the remaining life of a product, becomes reality: Inspection 4.0. Data collection in data lakes, analysis of data by image correlation and artificial intelligence and cloud-based provision of data at any place & time are on its way to revolutionize NDT/SHM and especially the use of its outputs.
Learning by Monitoring: Twinning and Model Discovery for Engineered
Associate Professor and Chair of Structural Mechanics and Monitoring
Department of Civil, Environmental and Geomatic Engineering, ETH Zürich
Modern engineering structures form complex - often interconnected - assemblies that operate under highly varying loads and adverse environments. To ensure a resource-efficient and resilient operation of such systems, it is imperative to understand their performance as-is; a task which can be effectuated through Structural Health Monitoring (SHM). SHM comprises a hierarchy across levels of increasing complexity aiming to i) detect, ii) localize and iii) quantify damage, and iv) finally offer a prognosis over the system's residual life. When considering higher levels in this hierarchy, including damage assessment and even performance prognosis, purely data-driven methods are found to be lacking. For higher-level SHM tasks, or for furnishing a digital twin of a monitored structure, it is necessary to integrate the knowledge stemming from physics-based representations, relying on the underlying mechanics. This talk discusses implementation of such a hybrid approach to SHM for tackling the aforementioned challenges for robust monitoring of engineered systems. We offer a view to establishing augmented twin representations, capable of representing the structure as-is, anticipating performance under future stressors, and advising on preventive and remedial actions.
Computation-enabled Digital Twin in the Built Environment
Professor and Robert W. Abbett Distinguished Chair in Civil Engineering,
Director of the Center for Intelligent Infrastructure,
Director of INSPIRE University Transportation Center
Missouri University of Science and Technology
This study makes an original attempt to equip a digital twin (DT) for an information construct with computational capabilities to achieve an information-computation construct in the built environment. This attempt is achieved by developing a DT methodology and framework for a university campus environment; defining modulated DTs and their connections, hierarchy, and architecture; enabling a twin representation of real-world construction with spatiotemporal analysis in multiple scales based on multimodal data from in-situ sensors, aerial nondestructive testing, and remote sensing; integrating computation, information, and machine learning models into a cyber-physical-social system (buildings, infrastructure, and affected community) for seamless decision-making from design through construction to operation phases; and evaluating structural behaviors under extreme loads. Potential value of the campus-scale DT includes the understanding of student aggregation, traffic flow, structural stability, building constructability, damage/cost scenario of existing and new buildings, and community impacts in the wake of a postulated earthquake event.