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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 30407

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Special Issue Editors

Dr. Seppo Sierla

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Guest Editor

Department of Electrical Engineering and Automation, Aalto University, 02150 Aalto, Finland
Interests: simulation; digital twin; virtual power plant; demand response; industry 4.0
Special Issues, Collections and Topics in MDPI journals

Dr. David Hästbacka

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Guest Editor

Department of Automation Science and Engineering, Tampere University of Technology, 33100 Tampere, Finland
Interests: control systems; industrial automation; automation; system modeling; systems engineering; system integration; service oriented architecture; Internet of Things; software architecture; semantic web; technologies

Dr. Kai Zenger

E-Mail Website
Guest Editor

Department of Electrical Engineering and Automation, Aalto University, 00076 Espoo, Finland
Interests: control systems engineering

Special Issue Information

Dear Colleagues,

The industrial sector is being transformed by the convergence of information technology and operational technology. The latter is another name for automation technology and covers established systems such as supervisory control and data acquisition (SCADA), programmable logic controllers (PLC), fieldbuses and automation and control systems. As this technology is connected to the Internet and 5G networks, some monitoring, control and analytic functionalities are deployed to the edge or cloud, and researchers are challenged to ensure the security, dependability, real-time performance and maintainability of the resulting systems. The big data that is accessible from these systems create opportunities for artificial intelligence applications that can further disrupt the established practices in the automation domain. For example, reinforcement learning is emerging as an alternative technology for industrial process control and optimization, and machine learning is heavily applied to fault diagnostic and predictive maintenance. Real-time connectivity, cloudification, big data and artificial intelligence are all driving the transformation of conventional simulators to digital twins.

In this Special Issue, we welcome contributions on advances in automation technology, especially but not limited to the abovementioned disruptive developments. Survey papers and reviews are also welcomed.

Dr. Seppo Sierla
Dr. David Hästbacka
Dr. Kai Zenger
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). For papers submitted to this special issue through the collaboration with the Automation Days 2023 conference (https://www.automaatioseura.fi/automationdays2023/), the APC is 1955 CHF. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

automation
control technology
simulation
digital twin
cloud
edge
big data
artificial intelligence
machine learning
reinforcement learning

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Published Papers (10 papers)

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Research

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24 pages, 2123 KiB

Open AccessArticle

Mathematical Modeling of SOIC Package Dynamics in Dielectric Fluids during High-Voltage Insulation Testing

by Yohan A. Aparicio and Manuel Jimenez

Appl. Sci. 2024, 14(9), 3693; https://doi.org/10.3390/app14093693 - 26 Apr 2024

Viewed by 1272

Abstract

The efficient testing and validation of the high-voltage (HV) insulation of small-outline integrated circuit (SOIC) packages presents numerous challenges when trying to achieve faster and more accurate processes. The complex behavior these packages when submerged in diverse physical media with varying densities requires a detailed analysis to understand the factors influencing their behavior. We propose a systematic and scalable mathematical model based on trapezoidal motion patterns and a deterministic analysis of hydrodynamic forces to predict SOIC package misalignment during automated high-voltage testing in a dielectric fluid. Our model incorporates factors known to cause misalignment during the maneuvering of packages, such as surface tension forces, sloshing, cavity formation, surface waves, and bubbles during the insertion, extraction, and displacement of devices while optimizing test speed for minimum testing time. Our model was validated via a full-factorial statistical experiment for different SOIC package sizes on a pick-and-place (PNP) machine with preprogrammed software and a zero-insertion force socket immersed in different dielectric fluids under controlled thermal conditions. Results indicate the model achieves 99.64% reliability with a margin of error of less than 4.78%. Our research deepens the knowledge and understanding of the physical and hydrodynamic factors that impact the automated testing processes of high-voltage insulator SOIC packages of different sizes for different dielectric fluids. It enables improved testing times and higher reliability than traditional trial-and-error methods for high-voltage SOIC packages, leading to more efficient and accurate processes in the electronics industry. Full article

(This article belongs to the Special Issue Disruptive Trends in Automation Technology)

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15 pages, 5206 KiB

Open AccessCommunication

A Study on Automated Problem Troubleshooting in Cloud Environments with Rule Induction and Verification

by Arnak Poghosyan, Ashot Harutyunyan, Edgar Davtyan, Karen Petrosyan and Nelson Baloian

Appl. Sci. 2024, 14(3), 1047; https://doi.org/10.3390/app14031047 - 26 Jan 2024

Cited by 5 | Viewed by 1652

Abstract

In a vast majority of cases, remediation of IT issues encoded into domain-specific or user-defined alerts occurring in cloud environments and customer ecosystems suffers from accurate recommendations, which could be supplied in a timely manner for recovery of performance degradations. This is hard to realize by furnishing those abnormality definitions with appropriate expert knowledge, which varies from one environment to another. At the same time, in many support cases, the reported problems under Global Support Services (GSS) or Site Reliability Engineering (SRE) treatment ultimately go down to the product teams, making them waste costly development hours on investigating self-monitoring metrics of our solutions. Therefore, the lack of a systematic approach to adopting AI Ops significantly impacts the mean-time-to-resolution (MTTR) rates of problems/alerts. This would imply building, maintaining, and continuously improving/annotating a data store of insights on which ML models are trained and generalized across the whole customer base and corporate cloud services. Our ongoing study aligns with this vision and validates an approach that learns the alert resolution patterns in such a global setting and explains them using interpretable AI methodologies. The knowledge store of causative rules is then applied to predicting potential sources of the application degradation reflected in an active alert instance. In this communication, we share our experiences with a prototype solution and up-to-date analysis demonstrating how root conditions are discovered accurately for a specific type of problem. It is validated against the historical data of resolutions performed by heavy manual development efforts. We also offer experts a Dempster–Shafer theory-based rule verification framework as a what-if analysis tool to test their hypotheses about the underlying environment. Full article

(This article belongs to the Special Issue Disruptive Trends in Automation Technology)

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19 pages, 6576 KiB

Open AccessArticle

Intelligent Trajectory Tracking Linear Active Disturbance Rejection Control of a Powered Parafoil Based on Twin Delayed Deep Deterministic Policy Gradient Algorithm Optimization

by Yuemin Zheng, Zelin Fei, Jin Tao, Qinglin Sun, Hao Sun, Zengqiang Chen and Mingwei Sun

Appl. Sci. 2023, 13(23), 12555; https://doi.org/10.3390/app132312555 - 21 Nov 2023

Cited by 3 | Viewed by 1601

Abstract

Powered parafoils, known for their impressive load-bearing capacity and extended endurance, have garnered significant interest. However, the parafoil system is a highly complex nonlinear system. It primarily relies on the steering gear to change flight direction and utilizes a thrust motor for climbing. However, achieving precise trajectory tracking control presents a challenge due to the interdependence of direction and altitude control. Furthermore, underactuation and wind disturbances bring additional difficulties for trajectory tracking control. Consequently, realizing trajectory tracking control for powered parafoils holds immense significance. In this paper, we propose a trajectory tracking method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm-optimized Linear Active Disturbance Rejection Control (LADRC). Our method addresses the underactuation issue by incorporating a guiding law while utilizing two LADRC methods to achieve decoupling and compensate for disturbances. Moreover, we employ the TD3 algorithm to dynamically adjust controller parameters, thus enhancing the controller performance. The simulation results demonstrate the effectiveness of our proposed method as a trajectory tracking control approach. Additionally, since the control process is not reliant on system-specific models, our method can also provide guidance for trajectory tracking control in other aircraft. Full article

(This article belongs to the Special Issue Disruptive Trends in Automation Technology)

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22 pages, 2795 KiB

Open AccessArticle

Data-Based Modelling of Chemical Oxygen Demand for Industrial Wastewater Treatment

by Henri Pörhö, Jani Tomperi, Aki Sorsa, Esko Juuso, Jari Ruuska and Mika Ruusunen

Appl. Sci. 2023, 13(13), 7848; https://doi.org/10.3390/app13137848 - 4 Jul 2023

Cited by 5 | Viewed by 2461

Abstract

The aim of wastewater treatment plants (WWTPs) is to clean wastewater before it is discharged into the environment. Real-time monitoring and control will become more essential as the regulations for effluent discharges are likely to become stricter in the future. Model-based soft sensors provide a promising solution for estimating important process variables such as chemical oxygen demand (COD) and help in predicting the performance of WWTPs. This paper explores the possibility of using interpretable model structures for monitoring the influent and predicting the effluent of paper mill WWTPs by systematically finding the best model parameters using an exhaustive algorithm. Experimentation was conducted with regression models such as multiple linear regression (MLR) and partial least squares regression (PLSR), as well as LASSO regression with a nonlinear scaling function to account for nonlinearities. Some autoregressive time series models were also built. The results showed decent modelling accuracy when tested with test data acquired from a wastewater treatment process. The most notable test results included the autoregressive model with exogenous inputs for influent COD (correlation 0.89, mean absolute percentage error 8.1%) and a PLSR model for effluent COD prediction (correlation 0.77, mean absolute percentage error 7.6%) with 20 h prediction horizon. The results show that these models are accurate enough for real-time monitoring and prediction in an industrial WWTP. Full article

(This article belongs to the Special Issue Disruptive Trends in Automation Technology)

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18 pages, 1350 KiB

Open AccessArticle

Analysis and Design of Direct Force Control for Robots in Contact with Uneven Surfaces

by Antonio Rosales and Tapio Heikkilä

Appl. Sci. 2023, 13(12), 7233; https://doi.org/10.3390/app13127233 - 16 Jun 2023

Cited by 2 | Viewed by 2069

Abstract

Robots executing contact tasks are essential in a wide range of industrial processes such as polishing, welding, debugging, drilling, etc. Force control is indispensable in these type of tasks since it is required to keep the interaction force (between the robot and the environment/surface) within acceptable values. In this paper, we present a methodology to analyze and to design the force control system needed to regulate the force as close as possible to the desired value. The proposed methods are presented using a widely used generic contact task consisting of exerting a desired force on the normal direction to the surface while a desired velocity/position is tracked on the tangent direction to the surface. The analysis considers environments/surfaces with certain uneven characteristics, i.e., not perfectly flat. The uneven characteristic is studied using ramp or sinusoidal signals disturbing the position on the normal direction to the surface, and we present how the velocity on the tangent direction is related with the slope of the ramp or the frequency of the sinusoidal disturbance. Then, we provide a method to design the force controller that keeps the force error within desired limits and preserves stability, despite the uneven surface. Furthermore, considering the relation between the disturbance (ramp or sinusoidal) and the tangent velocity, we present a method to compute the maximum velocity for which the task can be executed. Simulations exemplifying and verifying the proposed methods are presented. Full article

(This article belongs to the Special Issue Disruptive Trends in Automation Technology)

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13 pages, 927 KiB

Open AccessArticle

Digital Twin of Food Supply Chain for Cyber Exercises

by Tuomo Sipola, Tero Kokkonen, Markku Puura, Kalle-Eemeli Riuttanen, Kari Pitkäniemi, Elina Juutilainen and Teemu Kontio

Appl. Sci. 2023, 13(12), 7138; https://doi.org/10.3390/app13127138 - 14 Jun 2023

Cited by 5 | Viewed by 2219

Abstract

The food supply chain is a critical part of modern societies. As with other facets of life, it is thoroughly digitalized, and uses network connections. Consequently, the cyber security of the supply chain becomes a major concern as new threats emerge. Cyber ranges can be used to prepare for such cyber security threats by creating realistic scenarios mimicking real-world systems and setups. Organizations can participate in cyber security training and exercises that present them with these scenarios. Cyber ranges can also be used efficiently for research and development activities, because cyber ranges are realistic environments and can be used for the generation of realistic data. The aim of this study is to describe a digital twin of the food supply chain built for cyber range-based cyber security exercises. The digital twin mirrors the real-world situation with sufficient detail, as required by the cyber exercise. This research uses the design science methodology, which describes the construction and evaluation of the proposed system. The study explains the general capabilities of the food supply chain digital twin and its use in the cyber range environment. Different parts of the supply chain are implemented as Node.js services that run on the Realistic Global Cyber Environment (RGCE) platform. The flow of ingredients and products is simulated using an apparatus model and message queues. The digital twin was demonstrated in a real live cyber exercise. The results indicate that the apparatus approach was a scalable and realistic enough way to implement the digital twin. The main limitations of the implemented system are the implementation on one specific platform, and the need for more feedback from multiple exercises. Creation of a digital twin enables the use of cyber ranges to train organizations related to the food supply chain. Full article

(This article belongs to the Special Issue Disruptive Trends in Automation Technology)

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18 pages, 5688 KiB

Open AccessArticle

Comparison of Single Control Loop Performance Monitoring Methods

by Teemu Pätsi, Markku Ohenoja, Harri Kukkasniemi, Tero Vuolio, Petri Österberg, Seppo Merikoski, Henry Joutsijoki and Mika Ruusunen

Appl. Sci. 2023, 13(12), 6945; https://doi.org/10.3390/app13126945 - 8 Jun 2023

Viewed by 2487

Abstract

Well-performing control loops have an integral role in efficient and sustainable industrial production. Control performance monitoring (CPM) tools are necessary to establish further process optimization and preventive maintenance. Data-driven, model-free control performance monitoring approaches are studied in this research by comparing the performance of nine CPM methods in an industrially relevant process simulation. The robustness of some of the methods is considered with varying fault intensities. The methods are demonstrated on a simulator which represents a validated state-space model of a supercritical carbon dioxide fluid extraction process. The simulator is constructed with a single-input single-output unit controller for part of the process and a combination of relevant faults in the industry are introduced into the simulation. Of the demonstrated methods, Kullback–Leibler divergence, Euclidean distance, histogram intersection, and Overall Controller Efficiency performed the best in the first simulation case and could identify all the simulated fault scenarios. In the second case, integral-based methods Integral Squared Error and Integral of Time-weighted Absolute Error had the most robust performance with different fault intensities. The results highlight the applicability and robustness of some model-free methods and construct a solid foundation in the application of CPM in industrial processes. Full article

(This article belongs to the Special Issue Disruptive Trends in Automation Technology)

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31 pages, 7728 KiB

Open AccessArticle

From DevOps to MLOps: Overview and Application to Electricity Market Forecasting

by Rakshith Subramanya, Seppo Sierla and Valeriy Vyatkin

Appl. Sci. 2022, 12(19), 9851; https://doi.org/10.3390/app12199851 - 30 Sep 2022

Cited by 48 | Viewed by 9926

Abstract

In the Software Development Life Cycle (SDLC), Development and Operations (DevOps) has been proven to deliver reliable, scalable software within a shorter time. Due to the explosion of Machine Learning (ML) applications, the term Machine Learning Operations (MLOps) has gained significant interest among ML practitioners. This paper explains the DevOps and MLOps processes relevant to the implementation of MLOps. The contribution of this paper towards the MLOps framework is threefold: First, we review the state of the art in MLOps by analyzing the related work in MLOps. Second, we present an overview of the leading DevOps principles relevant to MLOps. Third, we derive an MLOps framework from the MLOps theory and apply it to a time-series forecasting application in the hourly day-ahead electricity market. The paper concludes with how MLOps could be generalized and applied to two more use cases with minor changes. Full article

(This article belongs to the Special Issue Disruptive Trends in Automation Technology)

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22 pages, 2662 KiB

Open AccessArticle

Whitening CNN-Based Rotor System Fault Diagnosis Model Features

by Jesse Miettinen, Riku-Pekka Nikula, Joni Keski-Rahkonen, Fredrik Fagerholm, Tuomas Tiainen, Seppo Sierla and Raine Viitala

Appl. Sci. 2022, 12(9), 4411; https://doi.org/10.3390/app12094411 - 27 Apr 2022

Cited by 5 | Viewed by 2555

Abstract

Intelligent fault diagnosis (IFD) models have the potential to increase the level of automation and the diagnosis accuracy of machine condition monitoring systems. Many of the latest IFD models rely on convolutional layers for feature extraction from vibration data. The majority of these models employ batch normalisation (BN) for centring and scaling the input for each neuron. This study includes a novel examination of a competitive approach for layer input normalisation in the scope of fault diagnosis. Network deconvolution (ND) is a technique that further decorrelates the layer inputs reducing redundancy among the learned features. Both normalisation techniques are implemented on three common 1D-CNN-based fault diagnosis models. The models with ND mostly outperform the baseline models with BN in three experiments concerning fault datasets from two different rotor systems. Furthermore, the models with ND significantly outperform the baseline models with BN in the common CWRU bearing fault tests with load domain shifts, if the data from drive-end and fan-end sensors are employed. The results show that whitened features can improve the performance of CNN-based fault diagnosis models. Full article

(This article belongs to the Special Issue Disruptive Trends in Automation Technology)

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Review

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16 pages, 3712 KiB

Open AccessReview

Is the Artificial Pollination of Walnut Trees with Drones Able to Minimize the Presence of Xanthomonas arboricola pv. juglandis? A Review

by Ioannis Manthos, Thomas Sotiropoulos and Ioannis Vagelas

Appl. Sci. 2024, 14(7), 2732; https://doi.org/10.3390/app14072732 - 25 Mar 2024

Cited by 5 | Viewed by 2046

Abstract

Walnut (Juglans regia L.) is a monoecious species and although it exhibits self-compatibility, it presents incomplete overlap of pollen shed and female receptivity. Thus, cross-pollination is prerequisite for optimal fruit production. Cross-pollination can occur naturally by wind, insects, artificially, or by hand. Pollen has been recognized as one possible pathway for Xanthomonas arboricola pv. juglandis infection, a pathogenic bacterium responsible for walnut blight disease. Other than the well-known cultural and chemical control practices, artificial pollination technologies with the use of drones could be a successful tool for walnut blight disease management in orchards. Drones may carry pollen and release it over crops or mimic the actions of bees and other pollinators. Although this new pollination technology could be regarded as a promising tool, pollen germination and knowledge of pollen as a potential pathway for the dissemination of bacterial diseases remain crucial information for the development and production of aerial pollinator robots for walnut trees. Thus, our purpose was to describe a pollination model with fundamental components, including the identification of the “core” pollen microbiota, the use of drones for artificial pollination as a successful tool for managing walnut blight disease, specifying an appropriate flower pollination algorithm, design of an autonomous precision pollination robot, and minimizing the average errors of flower pollination algorithm parameters through machine learning and meta-heuristic algorithms. Full article

(This article belongs to the Special Issue Disruptive Trends in Automation Technology)

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