A Framework of Dynamic Data Driven Digital Twin for Complex Engineering Products: the Example of Aircraft Engine Health Management

Digital twin is a vital enabling technology for smart manufacturing in the era of Industry 4.0. Digital twin effectively replicates its physical asset enabling easy visualization, smart decision-making and cognitive capability in the system. In this paper, a framework of dynamic data driven digital twin for complex engineering products was proposed. To illustrate the proposed framework, an example of health management on aircraft engines was studied. This framework models the digital twin by extracting information from the various sensors and Industry Internet of Things (IIoT) monitoring the remaining useful life (RUL) of an engine in both cyber and physical domains. Then, with sensor measurements selected from linear degradation models, a long short-term memory (LSTM) neural network is proposed to dynamically update the digital twin, which can estimate the most up-to-date RUL of the physical aircraft engine. Through comparison with other machine learning algorithms, including similarity based linear regression and feed forward neural network, on RUL modelling, this LSTM based dynamical data driven digital twin provides a promising tool to accurately replicate the health status of aircraft engines. This digital twin based RUL technique can also be extended for health management and remote operation of manufacturing systems

United States Air Force Applications of Unmanned Aerial Systems (UAS): A Delphi Study to Examine Current and Future UAS Autonomous Mission Capabilities

As UAS technology continues to grow and enable increased autonomous capabilities, acquisition and operational decision makers must determine paths to pursue for existing and emerging mission areas. The DoD has published a number of 25-year unmanned systems integration roadmaps (USIR) to describe future capabilities and challenges. However, these roadmaps have lacked distinguishable stakeholder perspectives. Following the USIRs concept, this research focused on UAS autonomy through the lens of UAS subject matter experts (SMEs). We used the Delphi method with SMEs from USAF communities performing day-to-day operations, acquisitions, and research in UAS domains to forecast mission capabilities over the next 20 years; specifically, within the context of increased UAS autonomous capabilities. Through two rounds of questions, the study provided insight to the capabilities SMEs viewed as most important and likely to be incorporated as well as how different stakeholders view the many challenges and opportunities autonomy present for future missions

Towards Self-Adaptive Discrete Event Simulation (SADES)

Systems that benefit from the ongoing use of simulation, often require considerable input by the modeller(s) to update and maintain the models. This paper proposes automating the evolution of the modelling process for discrete event simulation (DES) and therefore limiting the majority of the human modeller’s input to the development of the model. This mode of practice could be named Self-Adaptive Discrete Event Simulation (SADES). The research is driven from ideas emerging from simulation model reuse, automations in the modelling process, real time simulation, dynamic data driven application systems, autonomic computing and self-adaptive software systems. This paper explores some of the areas that could inform the development of SADES and proposes a modified version of the MAPE-K feedback control loop as a potential process. The expected outcome from developing SADES would be a simulation environment that is self-managing and more responsive to the analytical needs of real systems

Digital twins that learn and correct themselves

Digital twins can be defined as digital representations of physical entities that employ real‐time data to enable understanding of the operating conditions of these entities. Here we present a particular type of digital twin that involves a combination of computer vision, scientific machine learning, and augmented reality. This novel digital twin is able, therefore, to see, to interpret what it sees—and, if necessary, to correct the model it is equipped with—and presents the resulting information in the form of augmented reality. The computer vision capabilities allow the twin to receive data continuously. As any other digital twin, it is equipped with one or more models so as to assimilate data. However, if persistent deviations from the predicted values are found, the proposed methodology is able to correct on the fly the existing models, so as to accommodate them to the measured reality. Finally, the suggested methodology is completed with augmented reality capabilities so as to render a completely new type of digital twin. These concepts are tested against a proof‐of‐concept model consisting on a nonlinear, hyperelastic beam subjected to moving loads whose exact position is to be determined

A Framework For Process Data Collection, Analysis, And Visualization In Construction Projects

Automated data collection, simulation and visualization can substantially enhance the process of designing, analysis, planning, and control of many engineering processes. In particular, managing processes that are dynamic in nature can significantly benefit from such techniques. Construction projects are good examples of such processes where a variety of equipment and resources constantly interact inside an evolving environment. Management of such settings requires a platform capable of providing decision-makers with updated information about the status of project entities and assisting site personnel making critical decisions under uncertainty. To this end, the current practice of using historical data or expert judgments as static inputs to create empirical formulations, bar chart schedules, and simulation networks to study project activities, resource operations, and the environment under which a project is taking place does not seem to offer reliable results. The presented research investigates the requirements and applicability of a data-driven modeling framework capable of collecting and analyzing real time field data from construction equipment. In the developed data collection scheme, a stream of real time data is continuously transferred to a data analysis module to calculate the input parameters required to create dynamic 3D visualizations of ongoing engineering activities, and update the contents of a discrete event simulation (DES) model representing the real engineering process. The generated data-driven simulation model is iv an effective tool for projecting future progress based on existing performance. Ultimately, the developed framework can be used by project decision-makers for shortterm project planning and control since the resulting simulation and visualization are completely based on the latest status of project entities

Self-aware software architecture style and patterns for cloud-based applications

Modern cloud-reliant software systems are faced with the problem of cloud service providers violating their Service Level Agreement (SLA) claims. Given the large pool of cloud providers and their instability, cloud applications are expected to cope with these dynamics autonomously. This thesis investigates an approach for designing self-adaptive cloud architectures using a systematic methodology that guides the architect while designing cloud applications. The approach termed $Self-aware$ $Architecture$ $Pattern$ promotes fine-grained representation of architectural concerns to aid design-time analysis of risks and trade-offs. To support the coordination and control of architectural components in decentralised self-aware cloud applications, we propose a $Reputation-aware$ $posted$ $offer$ $market$ $coordination$ $mechanism$. The mechanism builds on the classic posted offer market mechanism and extends it to track behaviour of unreliable cloud services. The self-aware cloud architecture and its reputation-aware coordination mechanism are quantitatively evaluated within the context of an Online Shopping application using synthetic and realistic workload datasets under various configurations (failure, scale, resilience levels etc.). Additionally, we qualitatively evaluated our self-aware approach against two classic self-adaptive architecture styles using independent experts' judgment, to unveil its strengths and weaknesses relative to these styles

A thermodynamics-informed active learning approach to perception and reasoning about fluids

Learning and reasoning about physical phenomena is still a challenge in robotics development, and computational sciences play a capital role in the search for accurate methods able to provide explanations for past events and rigorous forecasts of future situations. We propose a thermodynamics-informed active learning strategy for fluid perception and reasoning from observations. As a model problem, we take the sloshing phenomena of different fluids contained in a glass. Starting from full-field and high-resolution synthetic data for a particular fluid, we develop a method for the tracking (perception) and simulation (reasoning) of any previously unseen liquid whose free surface is observed with a commodity camera. This approach demonstrates the importance of physics and knowledge not only in data-driven (gray-box) modeling but also in real-physics adaptation in low-data regimes and partial observations of the dynamics. The presented method is extensible to other domains such as the development of cognitive digital twins able to learn from observation of phenomena for which they have not been trained explicitly

A thermodynamics-informed active learning approach to perception and reasoning about fluids

Learning and reasoning about physical phenomena is still a challenge in robotics development, and computational sciences play a capital role in the search for accurate methods able to provide explanations for past events and rigorous forecasts of future situations. We propose a thermodynamics-informed active learning strategy for fluid perception and reasoning from observations. As a model problem, we take the sloshing phenomena of different fluids contained in a glass. Starting from full-field and high-resolution synthetic data for a particular fluid, we develop a method for the tracking (perception) and simulation (reasoning) of any previously unseen liquid whose free surface is observed with a commodity camera. This approach demonstrates the importance of physics and knowledge not only in data-driven (gray-box) modeling but also in real-physics adaptation in low-data regimes and partial observations of the dynamics. The presented method is extensible to other domains such as the development of cognitive digital twins able to learn from observation of phenomena for which they have not been trained explicitly

HARDWARE AND SOFTWARE ARCHITECTURES FOR ENERGY- AND RESOURCE-EFFICIENT SIGNAL PROCESSING SYSTEMS

For a large class of digital signal processing (DSP) systems, design and implementation of hardware and software is challenging due to stringent constraints on energy and resource requirements. In this thesis, we develop methods to address this challenge by proposing new constraint-aware system design methods for DSP systems, and energy- and resource-optimized designs of key DSP subsystems that are relevant across various application areas. In addition to general methods for optimizing energy consumption and resource utilization, we present streamlined designs that are specialized to efficiently address platform-dependent constraints. We focus on two specific aspects in development of energy- and resource-optimized design techniques: (1) Application-specific systems and architectures for energy- and resource- efficient design. First, we address challenges in efficient implementation of wireless sensor network building energy monitoring systems (WSNBEMSs). We develop new energy management schemes in order to maximize system lifetime for WSNBEMSs, and demonstrate that system lifetime can be improved significantly without affecting monitoring accuracy. We also present resource efficient, field programmable gate array (FPGA) architecture for implementation of orthogonal frequency division multiplexing (OFDM) systems. We have demonstrated that our design provides at least 8.8% enhancement in terms of resource efficiency compared to Xilinx FFT v7.1 within the same OFDM configuration. (2) Dataflow-based methods for structured design and implementation of energy- and resource- efficient DSP systems. First, we introduce a dataflow-based design approach based on integrating interrupt-based signal acquisition in context of parameterized synchronous dataflow (PSDF) modeling. We demonstrate that by applying our approach, energy- and resource-efficient embedded software can be derived systematically from high level models of dynamic, data-driven applications systems (DDDASs) functional structure. Also, we present an in-depth development of lightweight dataflow-Verilog (LWDF-V), which is an integration of the LWDF programming model with the Verilog hardware description language (HDL), and we demonstrate the utility of LWDF-V for design and implementation of digital systems for signal processing. We emphasize efficient of LWDF with HDLs, and emphasize the application of LWDF-V to design DSP systems with dynamic parameters on FPGA platforms