scholarly journals Digital Twin-Enabled Online Battlefield Learning with Random Finite Sets

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Peng Wang ◽  
Mei Yang ◽  
Jiancheng Zhu ◽  
Yong Peng ◽  
Ge Li
Keyword(s):  
Online Learning ◽  
System Architecture ◽  
Research Work ◽  
Operational Mode ◽  
Probability Hypothesis Density ◽  
Digital Twin ◽  
Sensor Model ◽  
Twin Models ◽  
Finite Set ◽  
Random Finite Sets

The digital twin is becoming the most promising emerging technology in the field of unmanned combat and has the potential to innovate future combat styles. Online battlefield learning is one of the key technologies for supporting the successful application of digital twin in unmanned combat. Since there is an urgent need for effective algorithms for online learning the battlefield states in real time, a new random finite set- (RFS-) based algorithm is proposed in the presence of detection uncertainty including clutters, missed detection, and noises. The system architecture and operational mode for implementing the digital twin-enabled online battlefield learning are provided. The unmanned ground vehicle (UGV) is employed as the experimental subject for systematically describing the proposed algorithm. The system architecture for implementing the digital twin-enabled online battlefield learning is firstly given, and its operational mode is also described in detail. The RFS-based digital twin models including the battlefield state model, UGV motion model, and sensor model are designed. The Bayesian inference is adopted, and the probability hypothesis density (PHD) filter is modified to implement the online learning process. At last, a group of experiments are conducted to verify the performance and effectiveness of the proposed algorithm. The research work in this paper will provide a good demonstration of the application of digital twin in unmanned combat.

Digital Twin Based Interactive Mechatronics Lab Development for Remote Lab Offering and Evaluation

2021 ◽  
Author(s):  
Jairo Viola ◽  
Furkan Guc ◽  
YangQuan Chen ◽  
Mauricio Calderon
Keyword(s):  
Flipped Classroom ◽  
Controller Design ◽  
Learning Experience ◽  
Remote Access ◽  
Mechatronic Systems ◽  
Digital Twin ◽  
Remote Lab ◽  
Twin Models ◽  
Laboratory Component ◽  
Control Education

Abstract Mechatronics and control education is supported by laboratory intensive assignments that allow students acquire software and hardware skills to solve real world problems. However, COVID-19 force many schools to switch into remote learning complicating the instruction of practical assignments. This paper presents a novel proposal for interactive remote teaching of the laboratory component of the course ME-142: Mechatronics at the University of California, Merced using Digital Twins (DT) and the flipped classroom methodology. Each lab experience is composed by a set of on-demand supporting materials with the foundations of mechatronics simulation using MATLAB/Simulink to enhance and adapt the learning experience of the students. Once the students acquire advanced simulation skills, a set of Digital Twin models are provided to the students in order to begin their interaction with virtual representations of real systems for identification, analysis, controller design and validation, which are available online for remote access. By the end of the course, students were able not only to gain valuable experience with mechatronic systems but also interact and build advanced modelling techniques as Digital Twin, contributing to compensate the lack of remote hardware interaction.

Digital Twin-Driven Condition-Based Maintenance

2022 ◽  
pp. 109-136
Author(s):  
Adolfo Crespo del Castillo ◽  
Marco Macchi ◽  
Laura Cattaneo
Keyword(s):  
Real Time ◽  
Industry 4.0 ◽  
Simulation Models ◽  
Condition Based Maintenance ◽  
Industrial Management ◽  
Digital Twin ◽  
Operation And Maintenance ◽  
Twin Models ◽  
Technical Professionals ◽  
Case Development

The world is witnessing an all-level digitalization that guides the industry and business to a restructuration in order to adapt to the new requirements of the surrounding environment. That change also concerns the labour of the technical professionals and their formation. As a consequence of this deep consciousness-raising, this chapter will investigate and develop simulation models based on the current digitalization. The aim of this chapter is the exposition of a real case development of “digital twin” models framed as part of the condition-based maintenance paradigm to improve real-time assets operation and maintenance. This model contributes by providing real-time results that could turn into a basis for the industrial management decisions and place them in the Industry 4.0 paradigm environment.

scholarly journals Time-Matching Random Finite Set-Based Filter for Radar Multi-Target Tracking

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4416 ◽  
Author(s):  
Defu Jiang ◽  
Ming Liu ◽  
Yiyue Gao ◽  
Yang Gao ◽  
Wei Fu ◽  
...  
Keyword(s):  
Target Tracking ◽  
Gaussian Mixture ◽  
Bayesian Filtering ◽  
Probability Hypothesis Density ◽  
Random Finite Set ◽  
Bayesian Formulation ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Density Filter ◽  
Bernoulli Filter

The random finite set (RFS) approach provides an elegant Bayesian formulation of the multi-target tracking (MTT) problem without the requirement of explicit data association. In order to improve the performance of the RFS-based filter in radar MTT applications, this paper proposes a time-matching Bayesian filtering framework to deal with the problem caused by the diversity of target sampling times. Based on this framework, we develop a time-matching joint generalized labeled multi-Bernoulli filter and a time-matching probability hypothesis density filter. Simulations are performed by their Gaussian mixture implementations. The results show that the proposed approach can improve the accuracy of target state estimation, as well as the robustness.

scholarly journals A Referenced Cyber Physical System for Compressor Manufacturing

2020 ◽  
Vol 306 ◽  
pp. 02005
Author(s):  
Jin Cao ◽  
Junliang Wang ◽  
Junqing Lu
Keyword(s):  
System Architecture ◽  
Manufacturing System ◽  
Physical Space ◽  
Virtual Space ◽  
Multidimensional Model ◽  
Digital Twin ◽  
Twin Model ◽  
Industrial Internet ◽  
Internet Platform ◽  
Model Layer

Compressor is a typical high-end discrete product，with the shortening of product life cycle and the enhancement of the degree of product customization, the traditional compressor manufacturing system architecture cannot meet the requirements of comprehensive digital management of compressor from body scheme design to parts production line, logistics management, operation and maintenance monitoring and evaluation. This paper presents a compressor manufacturing system architecture based on digital twinning, and establishes an Internet platform for compressor industry oriented to remote coordination from three aspects of compressor design, production, operation and maintenance. The platform includes industrial Internet infrastructure layer, physical space entity model layer, virtual space multidimensional model layer, physical space and virtual space multidimensional model correlation and mapping layer, big data intelligent analysis decision-making layer, and digital twin application layer. Through the establishment of the compressor product design and simulation model of digital twin, compressor production process digital twin model, compressor fault diagnosis and remote operations digital twin model, implementation is based on the number of compressor collaboration in manufacturing industrial Internet platform twin system, leading the transformation and upgrading of intelligent manufacturing industry, compressor industry sustainable development ability and international competitiveness.

Innovative digital inspection methodology

2020 ◽  
Vol 60 (1) ◽  
pp. 77
Author(s):  
Stephen A Anderson
Keyword(s):  
Laser Scanning ◽  
Design Parameters ◽  
Destructive Testing ◽  
Inspection Method ◽  
Digital Twin ◽  
3D Space ◽  
Twin Models ◽  
Quality Programs ◽  
Non Destructive ◽  
Digital Record

The paper describes an innovative digital inspection methodology that combines 3D laser scanning, metrology and advanced non-destructive testing data that is merged in 3D space to provide a digital record of the condition and mechanical integrity of critical assets. This advanced inspection method supports condition-based maintenance programs and digital twin models to determine future equipment condition, work scope and inspection schedules, while maintaining a digital record throughout the equipment lifecycle. Testing of the methodology includes 3D scanning of drill platforms, baseline scanning of blowout preventers and sheaves, for quality purposes, and the use of augmented reality for viewing scans. Phased array testing has been conducted on sub-components such as slew ring bolting. Data are combined into digital reports that show 3D images of the equipment with precise dimensional data and identified inspection areas. Such reports can be combined with digital twin models to confirm integrity of the equipment for certificate of conformance and baseline data for future integrity comparisons as equipment ages. This innovative inspection methodology will set a new standard for how equipment data are captured, stored and represented. The process provides a range of benefits for OEMs, drilling contractors and operators alike, including digital quality programs to baseline new equipment condition and compare with design parameters, delivering condition and integrity assessments of critical equipment items in-situ or on deck, providing a consistent methodology for inspection and dimensional control of operational equipment items, and providing precise equipment data that can complement digital twin and real time monitoring programs.

Development and Assessment of a Nearly Autonomous Management and Control System During a Single Loss of Flow Accident

2020 ◽  
Author(s):  
Linyu Lin ◽  
Paridhi Athe ◽  
Pascal Rouxelin ◽  
Nam Dinh ◽  
Jeffrey Lane
Keyword(s):  
Knowledge Base ◽  
Confusion Matrix ◽  
Data Generation ◽  
Digital Twin ◽  
Control Functions ◽  
Digital Twins ◽  
Twin Models ◽  
Autonomous Management ◽  
And Performance ◽  
And Control

Abstract In this work, a Nearly Autonomous Management and Control (NAMAC) system is designed to diagnose the reactor state and provide recommendations to the operator for maintaining the safety and performance of the reactor. A three layer-hierarchical workflow is suggested to guide the design and development of the NAMAC system. The three layers in this workflow corresponds to knowledge base, digital twin developmental layer (for different NAMAC functions), and NAMAC operational layer. Digital twin in NAMAC is described as knowledge acquisition system to support different autonomous control functions. Therefore, based on the knowledge base, a set of digital twin models is trained to determine the plant state, predict behavior of physical components or systems, and rank available control options. The trained digital twin models are assembled according to NAMAC operational workflow to support decision-making process in selecting the optimal control actions during an accident scenario. To demonstrate the capability of the NAMAC system, a case study is designed, where a baseline NAMAC is implemented for operating a simulator of the Experimental Breeder Reactor II (EBR-II) during a single loss of flow accident. Training database for development of digital twin models is obtained by sampling the control parameters in the GOTHIC data generation engine. After the training and testing, the digital twins are assembled into a NAMAC system according to the operational workflow. This NAMAC system is coupled with the GOTHIC plant simulator, and a confusion matrix is generated to illustrate the accuracy and robustness of implemented NAMAC system. It is found that within the training databases, NAMAC can make reasonable recommendations with zero confusion rate. However, when the scenario is beyond the training cases, the confusion rate increases, especially when the scenarios are more severe. Therefore, a discrepancy checker is added to detect unexpected reactor states and alert operators for safety-minded actions.

scholarly journals An Ant–Based Filtering Random–Finite–Set Approach to Simultaneous Localization and Mapping

2018 ◽  
Vol 28 (3) ◽  
pp. 505-519
Author(s):  
Demeng Li ◽  
Jihong Zhua ◽  
Benlian Xu ◽  
Mingli Lu ◽  
Mingyue Li
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Posterior Density ◽  
Operation System ◽  
Feature Map ◽  
Localization And Mapping ◽  
Finite Set ◽  
Feature Based ◽  
Vehicle Trajectory ◽  
Slam Algorithm ◽  
Random Finite Sets

Abstract Inspired by ant foraging, as well as modeling of the feature map and measurements as random finite sets, a novel formulation in an ant colony framework is proposed to jointly estimate the map and the vehicle trajectory so as to solve a feature-based simultaneous localization and mapping (SLAM) problem. This so-called ant-PHD-SLAM algorithm allows decomposing the recursion for the joint map-trajectory posterior density into a jointly propagated posterior density of the vehicle trajectory and the posterior density of the feature map conditioned on the vehicle trajectory. More specifically, an ant-PHD filter is proposed to jointly estimate the number of map features and their locations, namely, using the powerful search ability and collective cooperation of ants to complete the PHD-SLAM filter time prediction and data update process. Meanwhile, a novel fast moving ant estimator (F-MAE) is utilized to estimate the maneuvering vehicle trajectory. Evaluation and comparison using several numerical examples show a performance improvement over recently reported approaches. Moreover, the experimental results based on the robot operation system (ROS) platform validate the consistency with the results obtained from numerical simulations.

Centralized multi-sensor multi-target tracking with labeled random finite set

2016 ◽  
Vol 231 (4) ◽  
pp. 669-676 ◽  
Author(s):  
Hao Qiu ◽  
Gaoming Huang ◽  
Jun Gao
Keyword(s):  
Probability Hypothesis Density ◽  
Multiple Sensors ◽  
Parallel Track ◽  
Truncation Scheme ◽  
Moment Approximation ◽  
Multi Target Tracking ◽  
Finite Set ◽  
Density Filter ◽  
Single Sensor ◽  
First Moment

Tracking multiple objects with multiple sensors is widely recognized to be much more complex than the single-sensor scenario. This contribution proposes a computationally tractable multi-sensor multi-target tracker. Based on Bayes equation and multi-senor observation model, a new corrector for multi-senor is derived. To lower the complexity of update operation, a parallel track-to-measurement association strategy is applied to the corrector. Hypotheses truncation scheme along with first-moment approximation of multi-target density are also employed to improve the tracking efficiency. The tracker is applied to a couple-sensor scenario. Experiment results validate the advantages of proposed method compared to the standard single-sensor δ-generalized labeled multi-Bernoulli filter and the iterated-corrector probability hypothesis density filter.

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