scholarly journals Digital Twin in Electrical Machine Control and Predictive Maintenance: State-of-the-Art and Future Prospects

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5933
Author(s):  
Georgios Falekas ◽  
Athanasios Karlis
Keyword(s):  
State Of The Art ◽  
Predictive Maintenance ◽  
Electrical Machine ◽  
Digital Twin ◽  
Industrial Manufacturing ◽  
Core Concepts ◽  
Processing Techniques ◽  
And Control ◽  
Future Work ◽  
Relevant Work

State-of-the-art Predictive Maintenance (PM) of Electrical Machines (EMs) focuses on employing Artificial Intelligence (AI) methods with well-established measurement and processing techniques while exploring new combinations, to further establish itself a profitable venture in industry. The latest trend in industrial manufacturing and monitoring is the Digital Twin (DT) which is just now being defined and explored, showing promising results in facilitating the realization of the Industry 4.0 concept. While PM efforts closely resemble suggested DT methodologies and would greatly benefit from improved data handling and availability, a lack of combination regarding the two concepts is detected in literature. In addition, the next-generation-Digital-Twin (nexDT) definition is yet ambiguous. Existing DT reviews discuss broader definitions and include citations often irrelevant to PM. This work aims to redefine the nexDT concept by reviewing latest descriptions in broader literature while establishing a specialized denotation for EM manufacturing, PM, and control, encapsulating most of the relevant work in the process, and providing a new definition specifically catered to PM, serving as a foundation for future endeavors. A brief review of both DT research and PM state-of-the-art spanning the last five years is presented, followed by the conjunction of core concepts into a definitive description. Finally, surmised benefits and future work prospects are reported, especially focused on enabling PM state-of-the-art in AI techniques.

Seismic RTDT: Real-Time Digital Twin for Boosting Performance of Seismic Operations

2019 ◽  
Author(s):  
Severin Sadjina ◽  
Stian Skjong ◽  
Armin Pobitzer ◽  
Lars T. Kyllingstad ◽  
Roy-Jostein Fiskerstrand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
State Of The Art ◽  
Seismic Survey ◽  
Digital Twin ◽  
Detection Systems ◽  
Early Results ◽  
Actual Equipment ◽  
Automation Systems ◽  
And Control

Abstract Here, we present the R&D project Real-Time Digital Twin for Boosting Performance of Seismic Operations, which aims at increasing the overall operational efficiency of seismic vessels through digitisation and automation. The cornerstone in this project is the development of a real-time digital twin (RTDT) — a sophisticated mathematical model and state estimator of all the in-sea seismic equipment, augmented with real-time measurements from the actual equipment. This provides users and systems on-board the vessel with a live digital representation of the state of the equipment during operations. By combining the RTDT with state-of-the-art methods in machine learning and control theory, the project will develop new advisory and automation systems that improve the efficiency of seismic survey operations, reduce the risk of equipment damage, improve health monitoring and fault detection systems, and improve the quality of the seismic data. This will lead to less unproductive time, reduced costs, reduced fuel consumption and reduced emissions for a given operational scope. The main focus in this paper is the presentation of today’s challenges in offshore seismic surveys, and how state-of-the-art technology can be adopted to improve various operations. We discuss how simulation technology, machine learning and live sensor measurements can be integrated in on-board decision support and automation systems, and highlight the importance of such systems for designing the complex, autonomous offshore vessels of the future. Finally, we present some early results from the project in the form of two brief case studies.

scholarly journals Building Footprint Extraction from Multispectral, Spaceborne Earth Observation Datasets Using a Structurally Optimized U-Net Convolutional Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2803 ◽  
Author(s):  
Giorgio Pasquali ◽  
Gianni Cristian Iannelli ◽  
Fabio Dell’Acqua
Keyword(s):  
Satellite Data ◽  
State Of The Art ◽  
Urban Expansion ◽  
Difficult Problem ◽  
Detection Accuracy ◽  
Extreme Variability ◽  
Building Footprint ◽  
Processing Techniques ◽  
Spectral Patterns ◽  
Future Work

Building footprint detection and outlining from satellite imagery represents a very useful tool in many types of applications, ranging from population mapping to the monitoring of illegal development, from urban expansion monitoring to organizing prompter and more effective rescuer response in the case of catastrophic events. The problem of detecting building footprints in optical, multispectral satellite data is not easy to solve in a general way due to the extreme variability of material, shape, spatial, and spectral patterns that may come with disparate environmental conditions and construction practices rooted in different places across the globe. This difficult problem has been tackled in many different ways since multispectral satellite data at a sufficient spatial resolution started making its appearance on the public scene at the turn of the century. Whereas a typical approach, until recently, hinged on various combinations of spectral–spatial analysis and image processing techniques, in more recent times, the role of machine learning has undergone a progressive expansion. This is also testified by the appearance of online challenges like SpaceNet, which invite scholars to submit their own artificial intelligence (AI)-based, tailored solutions for building footprint detection in satellite data, and automatically compare and rank by accuracy the proposed maps. In this framework, after reviewing the state-of-the-art on this subject, we came to the conclusion that some improvement could be contributed to the so-called U-Net architecture, which has shown to be promising in this respect. In this work, we focused on the architecture of the U-Net to develop a suitable version for this task, capable of competing with the accuracy levels of past SpaceNet competition winners using only one model and one type of data. This achievement could pave the way for achieving better performances than the current state-of-the-art. All these results, indeed, have yet to be augmented through the integration of techniques that in the past have demonstrated a capability of improving the detection accuracy of U-net-based footprint detectors. The most notable cases are represented by an ensemble of different U-Net architectures, the integration of distance transform to improve boundary detection accuracy, and the incorporation of ancillary geospatial data on buildings. Our future work will incorporate those enhancements.

scholarly journals A Mixed Reality Interface for a Digital Twin Based Crane

2021 ◽  
Vol 11 (20) ◽  
pp. 9480
Author(s):  
Xinyi Tu ◽  
Juuso Autiosalo ◽  
Adnane Jadid ◽  
Kari Tammi ◽  
Gudrun Klinker
Keyword(s):  
Mixed Reality ◽  
Data Communication ◽  
Measurement Procedure ◽  
Three Dimensions ◽  
Digital Twin ◽  
Spatial Registration ◽  
Enhanced Mobility ◽  
Microsoft Hololens ◽  
And Control ◽  
Future Work

Digital twin technology empowers the digital transformation of the industrial world with an increasing amount of data, which meanwhile creates a challenging context for designing a human–machine interface (HMI) for operating machines. This work aims at creating an HMI for digital twin based services. With an industrial crane platform as a case study, we presented a mixed reality (MR) application running on a Microsoft HoloLens 1 device. The application, consisting of visualization, interaction, communication, and registration modules, allowed crane operators to both monitor the crane status and control its movement through interactive holograms and bi-directional data communication, with enhanced mobility thanks to spatial registration and tracking of the MR environment. The prototype was quantitatively evaluated regarding the control accuracy in 20 measurements following a step-by-step protocol that we defined to standardize the measurement procedure. The results suggested that the differences between the target and actual positions were within the 10 cm range in three dimensions, which were considered sufficiently small regarding the typical crane operation use case of logistics purposes and could be improved with the adoption of robust registration and tracking techniques in our future work.

scholarly journals Maintenance Strategies for Industrial Multi-Stage Machines: The Study of a Thermoforming Machine

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6809
Author(s):  
Francisco Javier Álvarez García ◽  
David Rodríguez Salgado
Keyword(s):  
Operating Conditions ◽  
Predictive Maintenance ◽  
Maintenance Strategies ◽  
Industrial Manufacturing ◽  
Multi Stage ◽  
Ideal Approach ◽  
Preventive Programming ◽  
Early Decision ◽  
And Control ◽  
New Strategies

The study of reliability, availability and control of industrial manufacturing machines is a constant challenge in the industrial environment. This paper compares the results offered by several maintenance strategies for multi-stage industrial manufacturing machines by analysing a real case of a multi-stage thermoforming machine. Specifically, two strategies based on preventive maintenance, Preventive Programming Maintenance (PPM) and Improve Preventive Programming Maintenance (IPPM) are compared with two new strategies based on predictive maintenance, namely Algorithm Life Optimisation Programming (ALOP) and Digital Behaviour Twin (DBT). The condition of machine components can be assessed with the latter two proposals (ALOP and DBT) using sensors and algorithms, thus providing a warning value for early decision-making before unexpected faults occur. The study shows that the ALOP and DBT models detect unexpected failures early enough, while the PPM and IPPM strategies warn of scheduled component replacement at the end of their life cycle. The ALOP and DBT strategies algorithms can also be valid for managing the maintenance of other multi-stage industrial manufacturing machines. The authors consider that the combination of preventive and predictive maintenance strategies may be an ideal approach because operating conditions affect the mechanical, electrical, electronic and pneumatic components of multi-stage industrial manufacturing machines differently.

A state-of-the-art review on implementation of digital twin in additive manufacturing to monitor and control parts quality

2021 ◽  
Author(s):  
Rakesh Kumar Phanden ◽  
S.V. Aditya ◽  
Aaryan Sheokand ◽  
Kapil Kumar Goyal ◽  
Pardeep Gahlot ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
State Of The Art ◽  
Digital Twin ◽  
And Control ◽  
Monitor And Control

Parametric and Non-parametric Mathematical Modelling Techniques: A Practical Approach of an Electrical Machine Identification

2021 ◽  
Vol 5 (1) ◽  
pp. 30-36
Author(s):  
Oscar Gonzales
Keyword(s):  
Experimental Data ◽  
Dynamic Systems ◽  
State Of The Art ◽  
Electrical Machine ◽  
Comprehensive Review ◽  
Mathematical Expressions ◽  
Analysis Of Results ◽  
Modelling Techniques ◽  
Identification Techniques ◽  
And Control

Mathematical modeling is an important feature concerning the analysis and control of dynamic systems. Also, system identification is an approach for building mathematical expressions from experimental data taken from processes performance. In this context, the contemporaneous state of the art describes several modelling and identification techniques which are excellent alternatives to determine systems behavior through time. This paper presents a comprehensive review of the main techniques for modeling and identification from a parametric and no parametric perspective. Experimental data are taken from an electrical machine that is a DC motor from a didactic platform. The paper concludes with the analysis of results taken from different identification procedures.

The AMP-Foot 2.1 : actuator design, control and experiments with an amputee

Robotica ◽  
2014 ◽  
Vol 32 (8) ◽  
pp. 1347-1361 ◽  
Author(s):  
Pierre Cherelle ◽  
Karen Junius ◽  
Victor Grosu ◽  
Heidi Cuypers ◽  
Bram Vanderborght ◽  
...  
Keyword(s):  
Control Strategy ◽  
Energy Efficient ◽  
Experimental Validation ◽  
Stance Phase ◽  
State Of The Art ◽  
New Energy ◽  
Full Power ◽  
And Control ◽  
Future Work ◽  
Actuator Design

SUMMARYThe Ankle Mimicking Prosthetic (AMP-) Foot 2 is a new energy efficient, powered transtibial prosthesis mimicking intact ankle behavior. The author's research is focused on the use of a low power actuator which stores energy in springs during the complete stance phase. At push-off, this energy can be released hereby providing propulsion forces and torques to the amputee. With the use of the so-called catapult actuator, the size and weight of the drive can be decreased compared to state-of-the-art powered prostheses, while still providing the full power necessary for walking.In this article, the authors present a detailed description of the catapult actuator followed by a comparison with existing actuator technology in powered prosthetic feet with regard to torque and power requirements. The implication on the actuator's design will then be outlined. Further, a description of the control strategy behind the AMP-Foot 2 and 2.1 will be given. In the last section of the article, the actuation principle and control are illustrated by experimental validation with a transfemoral amputee. Conclusions and future work complete the paper.

Failure Monitoring and Predictive Maintenance of Hydraulic Cylinder - State-of-the-Art Review

2021 ◽  
pp. 1-1 ◽  
Author(s):  
Vignesh Vishnudas Shanbhag ◽  
Thomas J. J. Meyer ◽  
Leo W. Caspers ◽  
Rune Schlanbusch
Keyword(s):  
State Of The Art ◽  
Hydraulic Cylinder ◽  
Predictive Maintenance ◽  
Failure Monitoring

scholarly journals A Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 400 ◽  
Author(s):  
Zelin Nie ◽  
Feng Gao ◽  
Chao-Bo Yan
Keyword(s):  
Optimization Model ◽  
Air Conditioning ◽  
State Of The Art ◽  
Hvac Systems ◽  
Practical Significance ◽  
Hvac System ◽  
Bilinear Model ◽  
Optimization And Control ◽  
And Control ◽  
Slow Timescale

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

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