Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557502
Ouijdane Guiza, Christoph Mayr-Dorn, G. Weichhart, M. Mayrhofer, Bahman Bahman Zangi, Alexander Egyed, Björn Fanta, Martin Gieler
Unforeseen situations on the shopfloor cause the assembly process to divert from its expected progress. To be able to overcome these deviations in a timely manner, assembly process monitoring and early deviation detection are necessary. However, legal regulations and union policies often limit the direct monitoring of human-intensive assembly processes. Grounded in an industry use case, this paper outlines a novel approach that, based on indirect privacy-respecting monitored data from the shopfloor, enables the near real-time detection of multiple types of process deviations. In doing so, this paper specifically addresses uncertainties stemming from indirect shopfloor observations and how to reason in their presence.
{"title":"Automated Deviation Detection for Partially-Observable Human-Intensive Assembly Processes","authors":"Ouijdane Guiza, Christoph Mayr-Dorn, G. Weichhart, M. Mayrhofer, Bahman Bahman Zangi, Alexander Egyed, Björn Fanta, Martin Gieler","doi":"10.1109/INDIN45523.2021.9557502","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557502","url":null,"abstract":"Unforeseen situations on the shopfloor cause the assembly process to divert from its expected progress. To be able to overcome these deviations in a timely manner, assembly process monitoring and early deviation detection are necessary. However, legal regulations and union policies often limit the direct monitoring of human-intensive assembly processes. Grounded in an industry use case, this paper outlines a novel approach that, based on indirect privacy-respecting monitored data from the shopfloor, enables the near real-time detection of multiple types of process deviations. In doing so, this paper specifically addresses uncertainties stemming from indirect shopfloor observations and how to reason in their presence.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134203533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557545
Yunchuan Sun, Zixiu Ma, Xiaoping Zeng, Yao Guo
Accounting fraud, usually difficult to detect, can cause significant harm to stakeholders and serious damage to the market. Effective methods of accounting fraud detection are needed for the prevention and governance of accounting fraud.In this study, we develop a novel accounting fraud prediction model using XGBoost, a powerful ensemble learning approach. We respectively select 12 financial ratios, 28 raw accounting numbers and 99 raw accounting numbers available from Chinese listed firms’ financial statements, as the model input. To assess the performance of fraud prediction models, we select two evaluation metrics - AUC and NDCG@k, and two benchmark models - the Dechow et al. (2011) logistic regression model based on financial ratios, and the Bao et al. (2020) AdaBoost model based on raw accounting numbers.Results show that: 1) our XGBoost-based prediction model outperforms two benchmark models by a large margin whatever model inputs and evaluation metrics; 2) the XGBoost-based prediction model with raw accounting numbers input outperforms the one with financial ratios input; 3) the XGoost-based prediction model with 99 raw accounting numbers input outperforms the one with 28 raw accounting numbers input.
{"title":"A Predicting Model For Accounting Fraud Based On Ensemble Learning","authors":"Yunchuan Sun, Zixiu Ma, Xiaoping Zeng, Yao Guo","doi":"10.1109/INDIN45523.2021.9557545","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557545","url":null,"abstract":"Accounting fraud, usually difficult to detect, can cause significant harm to stakeholders and serious damage to the market. Effective methods of accounting fraud detection are needed for the prevention and governance of accounting fraud.In this study, we develop a novel accounting fraud prediction model using XGBoost, a powerful ensemble learning approach. We respectively select 12 financial ratios, 28 raw accounting numbers and 99 raw accounting numbers available from Chinese listed firms’ financial statements, as the model input. To assess the performance of fraud prediction models, we select two evaluation metrics - AUC and NDCG@k, and two benchmark models - the Dechow et al. (2011) logistic regression model based on financial ratios, and the Bao et al. (2020) AdaBoost model based on raw accounting numbers.Results show that: 1) our XGBoost-based prediction model outperforms two benchmark models by a large margin whatever model inputs and evaluation metrics; 2) the XGBoost-based prediction model with raw accounting numbers input outperforms the one with financial ratios input; 3) the XGoost-based prediction model with 99 raw accounting numbers input outperforms the one with 28 raw accounting numbers input.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"27 20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132072830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, a deep residual network named GFresNet-2D is proposed for a point-focusing shear horizontal guided wave electromagnetic acoustic transducer, which can be used to quantify different types of defects, such as pinholes, cracks, and corrosion, in materials. As the traditional feature extraction and statistical machine learning methods are too complex and rely on artificial recognition, an automatic feature extraction model based on deep learning is applied for defect detection and quantification. Owing to their similarity with the ultrasonic guided wave signals, the measured 1D signals from the experiments cannot be directly applied to train neural networks. Therefore, we used the normalization, minimum suppression, and continuous wavelet transform methods to convert the initial measured 1D signals into processed 2D images, and constructed a data set containing 1,440,000,000 signal/image data. The performance of the proposed GFresNet-2D model for this new data set was also compared with those of traditional models, and sensitivity analyses were performed for some of the representative parameters. The results confirm that the proposed method can contribute to the development of deep-learning-based defect quantification using the ultrasonic guided wave focusing method.
{"title":"Quantification of Defects with Point-Focusing Shear Horizontal Guided Wave EMAT Using Deep Residual Network","authors":"Hongyu Sun, Songling Huang, Shen Wang, Wei Zhao, Lisha Peng","doi":"10.1109/INDIN45523.2021.9557567","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557567","url":null,"abstract":"In this work, a deep residual network named GFresNet-2D is proposed for a point-focusing shear horizontal guided wave electromagnetic acoustic transducer, which can be used to quantify different types of defects, such as pinholes, cracks, and corrosion, in materials. As the traditional feature extraction and statistical machine learning methods are too complex and rely on artificial recognition, an automatic feature extraction model based on deep learning is applied for defect detection and quantification. Owing to their similarity with the ultrasonic guided wave signals, the measured 1D signals from the experiments cannot be directly applied to train neural networks. Therefore, we used the normalization, minimum suppression, and continuous wavelet transform methods to convert the initial measured 1D signals into processed 2D images, and constructed a data set containing 1,440,000,000 signal/image data. The performance of the proposed GFresNet-2D model for this new data set was also compared with those of traditional models, and sensitivity analyses were performed for some of the representative parameters. The results confirm that the proposed method can contribute to the development of deep-learning-based defect quantification using the ultrasonic guided wave focusing method.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129604266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557406
J. Zinn, B. Vogel‐Heuser, Fabian Schuhmann, Luis Alberto Cruz Salazar
The training of Deep Reinforcement Learning algorithms on robotic devices is challenging due to their large number of actuators and limited number of feasible action sequences. This paper addresses this challenge by extending and transferring existing approaches for waypoint-based exploration with Hierarchical Reinforcement Learning to the domain of robotic devices. The resulting algorithm utilizes a top-level policy, which suggests waypoints to a bottom-level policy that controls the system actuators. The waypoints can either be provided to the top-level policy as domain knowledge or be learned from scratch. The algorithm explicitly accounts for the low number of feasible waypoints and waypoint transitions that are characteristic of robotic devices. The effectiveness of the approach is evaluated on the simulation of a research demonstrator, and a separate ablation study proves the importance of its components.
{"title":"Hierarchical Reinforcement Learning for Waypoint-based Exploration in Robotic Devices","authors":"J. Zinn, B. Vogel‐Heuser, Fabian Schuhmann, Luis Alberto Cruz Salazar","doi":"10.1109/INDIN45523.2021.9557406","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557406","url":null,"abstract":"The training of Deep Reinforcement Learning algorithms on robotic devices is challenging due to their large number of actuators and limited number of feasible action sequences. This paper addresses this challenge by extending and transferring existing approaches for waypoint-based exploration with Hierarchical Reinforcement Learning to the domain of robotic devices. The resulting algorithm utilizes a top-level policy, which suggests waypoints to a bottom-level policy that controls the system actuators. The waypoints can either be provided to the top-level policy as domain knowledge or be learned from scratch. The algorithm explicitly accounts for the low number of feasible waypoints and waypoint transitions that are characteristic of robotic devices. The effectiveness of the approach is evaluated on the simulation of a research demonstrator, and a separate ablation study proves the importance of its components.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116664232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557485
M. E. I. Martínez, J. Antonino-Daviu, C. Platero, L. Dunai, J. Conejero, P. F. Córdoba
In this work, the application of multifractal spectrum of higher order cumulants slice and bicoherence of stator current signals is proposed as a way to detect field winding faults in wound field synchronous motors. These signals are analyzed both under starting and under steady-state regimes. Likewise, a quantitative indicator based on the summation of the first three log cumulants of the scaling exponents obtained from the multifractal analysis is proposed. In addition, a comparative study is carried out during starting and at steady-state, obtaining satisfactory results that prove the potential of the proposed methodology for its implementation in real applications.
{"title":"Multifractal Spectrum and Higher Order Statistics for the Detection of Field Winding Faults in Wound Field Synchronous Motors","authors":"M. E. I. Martínez, J. Antonino-Daviu, C. Platero, L. Dunai, J. Conejero, P. F. Córdoba","doi":"10.1109/INDIN45523.2021.9557485","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557485","url":null,"abstract":"In this work, the application of multifractal spectrum of higher order cumulants slice and bicoherence of stator current signals is proposed as a way to detect field winding faults in wound field synchronous motors. These signals are analyzed both under starting and under steady-state regimes. Likewise, a quantitative indicator based on the summation of the first three log cumulants of the scaling exponents obtained from the multifractal analysis is proposed. In addition, a comparative study is carried out during starting and at steady-state, obtaining satisfactory results that prove the potential of the proposed methodology for its implementation in real applications.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"262 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123021479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557359
S. Sinha, P. Franciosa, D. Ceglarek
The paper proposes a novel approach, Object Shape Error Correction (OSEC), to determine corrective action in order to mitigate root cause(s) (RCs) of dimensional and geometric product shape errors. It leverages Deep Deterministic Policy Gradient (DDPG) algorithm to learn optimal process parameters update policies based on high dimensional state estimates of multi-station assembly systems (MAS). These policies can be interpreted in engineering terms as sequential corrective adjustments of process parameters that are necessary to mitigate RCs of product shape errors. The approach has the capability to estimate adjustments of process parameters related to fixturing and joining while simultaneously accounting for (i) RC uncertainty estimation, (ii) Key Performance Indicator (KPI) improvement, (iii) MAS design architecture; and, (iv) MAS inherent stochasticity. In addition, the OSEC methodology leverages a reward function parameterized by user interpretable functional coefficients for optimal tradeoff involving various corrections requirements. Benchmarking using an industrial, automotive cross-member assembly system demonstrates a 40% increase in the effectiveness of corrective actions when compared to current approaches.
{"title":"Object Shape Error Correction using Deep Reinforcement Learning for Multi-Station Assembly Systems","authors":"S. Sinha, P. Franciosa, D. Ceglarek","doi":"10.1109/INDIN45523.2021.9557359","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557359","url":null,"abstract":"The paper proposes a novel approach, Object Shape Error Correction (OSEC), to determine corrective action in order to mitigate root cause(s) (RCs) of dimensional and geometric product shape errors. It leverages Deep Deterministic Policy Gradient (DDPG) algorithm to learn optimal process parameters update policies based on high dimensional state estimates of multi-station assembly systems (MAS). These policies can be interpreted in engineering terms as sequential corrective adjustments of process parameters that are necessary to mitigate RCs of product shape errors. The approach has the capability to estimate adjustments of process parameters related to fixturing and joining while simultaneously accounting for (i) RC uncertainty estimation, (ii) Key Performance Indicator (KPI) improvement, (iii) MAS design architecture; and, (iv) MAS inherent stochasticity. In addition, the OSEC methodology leverages a reward function parameterized by user interpretable functional coefficients for optimal tradeoff involving various corrections requirements. Benchmarking using an industrial, automotive cross-member assembly system demonstrates a 40% increase in the effectiveness of corrective actions when compared to current approaches.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128622587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557429
Ricardo Silva Peres, José Barata
The advent of Industry 4.0 has made it crucial to improve the accessibility of higher education and to ensure that the future generation of engineers is able to acquire interdisciplinary competences to face the challenges of the data-driven era, including hands-on experience with real scenarios. We present an approach for teaching Cyber-Physical Production Systems remotely to graduate students, along with a discussion of sample projects, recommendations and the lessons learned in the effort to break down geographical barriers to education, reduce the cost associated with material and mitigate the impact of unforeseen disruptions such as the one caused by the pandemic scenario of COVID-19 in 2020. A combination of physical learning factory demonstrators, digital twin and simulation scenarios was developed to provide students with the resources to remotely implement an end-to-end Cyber-Physical Production System, along with its integration with other key technologies of Industry 4.0. A case study at the NOVA University of Lisbon showed that average attendance improved by 26.6%, retention in lab component improved by 12.9% and lab grades improved on average by 7.33% compared to on-site iterations of the same course in the two previous years.
{"title":"Remote E-Learning for Cyber-Physical Production Systems in Higher Education","authors":"Ricardo Silva Peres, José Barata","doi":"10.1109/INDIN45523.2021.9557429","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557429","url":null,"abstract":"The advent of Industry 4.0 has made it crucial to improve the accessibility of higher education and to ensure that the future generation of engineers is able to acquire interdisciplinary competences to face the challenges of the data-driven era, including hands-on experience with real scenarios. We present an approach for teaching Cyber-Physical Production Systems remotely to graduate students, along with a discussion of sample projects, recommendations and the lessons learned in the effort to break down geographical barriers to education, reduce the cost associated with material and mitigate the impact of unforeseen disruptions such as the one caused by the pandemic scenario of COVID-19 in 2020. A combination of physical learning factory demonstrators, digital twin and simulation scenarios was developed to provide students with the resources to remotely implement an end-to-end Cyber-Physical Production System, along with its integration with other key technologies of Industry 4.0. A case study at the NOVA University of Lisbon showed that average attendance improved by 26.6%, retention in lab component improved by 12.9% and lab grades improved on average by 7.33% compared to on-site iterations of the same course in the two previous years.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128909086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557517
Anna-Kristin Behnert, Felix Rinker, A. Lüder, S. Biffl
Efficient and effective engineering data exchange is increasingly considered a key success factor in the life cycle of production systems, leading to the intensified development of data logistic solutions. As small and medium-size companies (SMEs) play important roles in modern engineering organization structures, SMEs have to improve their capabilities to take part in these data logistics solutions. Unfortunately, SMEs have strong human and financial resource limitations. In this paper, we introduce a modular and easy-to-use data logistics architecture that aims at enabling SMEs to implement proof-of-concept software structures, applicable to validate benefits and challenges of data logistic solutions. This data logistics architecture provides a migration path towards the full participation of SMEs in data logistic solutions for engineering data exchange. We demonstrate the application of the architecture on use cases in automotive, steel, and machining industries.
{"title":"Migrating Engineering Tools Towards an AutomationML-Based Engineering Pipeline","authors":"Anna-Kristin Behnert, Felix Rinker, A. Lüder, S. Biffl","doi":"10.1109/INDIN45523.2021.9557517","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557517","url":null,"abstract":"Efficient and effective engineering data exchange is increasingly considered a key success factor in the life cycle of production systems, leading to the intensified development of data logistic solutions. As small and medium-size companies (SMEs) play important roles in modern engineering organization structures, SMEs have to improve their capabilities to take part in these data logistics solutions. Unfortunately, SMEs have strong human and financial resource limitations. In this paper, we introduce a modular and easy-to-use data logistics architecture that aims at enabling SMEs to implement proof-of-concept software structures, applicable to validate benefits and challenges of data logistic solutions. This data logistics architecture provides a migration path towards the full participation of SMEs in data logistic solutions for engineering data exchange. We demonstrate the application of the architecture on use cases in automotive, steel, and machining industries.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115484196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557582
F. Harrou, B. Taghezouit, Benamar Bouyeddou, Ying Sun, A. Arab
The demand for solar energy has rapidly increased throughout the world in recent years. However, anomalies in photovoltaic (PV) plants can reduce performances and result in serious consequences. Developing reliable statistical approaches able to detect anomalies in PV plants is vital to improving the management of these plants. Here, we present a statistical approach for detecting anomalies in the DC part of PV plants and partial shading. Firstly, we model the monitored PV plant. Then, we employ a generalized likelihood ratio test, which is a powerful anomaly detection tool, to check the residuals from the model and reveal anomalies in the supervised PV array. The proposed strategy is illustrated via actual measurements from a 9.54 PV plant.
{"title":"Fault Detection in Solar PV Systems Using Hypothesis Testing","authors":"F. Harrou, B. Taghezouit, Benamar Bouyeddou, Ying Sun, A. Arab","doi":"10.1109/INDIN45523.2021.9557582","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557582","url":null,"abstract":"The demand for solar energy has rapidly increased throughout the world in recent years. However, anomalies in photovoltaic (PV) plants can reduce performances and result in serious consequences. Developing reliable statistical approaches able to detect anomalies in PV plants is vital to improving the management of these plants. Here, we present a statistical approach for detecting anomalies in the DC part of PV plants and partial shading. Firstly, we model the monitored PV plant. Then, we employ a generalized likelihood ratio test, which is a powerful anomaly detection tool, to check the residuals from the model and reveal anomalies in the supervised PV array. The proposed strategy is illustrated via actual measurements from a 9.54 PV plant.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127741509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-21DOI: 10.1109/INDIN45523.2021.9557372
Flávia Pires, B. Ahmad, A. Moreira, P. Leitão
The research about the digital twin concept is growing worldwide, especially in the industrial sector, due to the increasing digitisation level associated to Industry 4.0. The application of the digital twin concept improves performance of a system by implementing monitoring, diagnosis, optimisation, and decision support actions. In particular, the decision-making process is very time consuming since the decision-maker is presented with hundreds of different scenarios that can be simulated and assessed in a what-if perspective. Bearing this in mind, this paper proposes to integrate a digital twin-based what-if simulation with a recommendation system to improve the decision-making cycle. The recommendation system is based on a reinforcement learning technique and takes user knowledge of the system into consideration and trust in the system recommendation. The applicability of the proposed approach is presented in an assembly line case study for recommending the best configurations for the system operation, in terms of the optimal number of AGVs (Autonomous Guided Vehicles) in various scenarios. The achieved results show its successful application and highlight the benefits of using AI-based recommendation systems for what-if simulation in digital twin systems.
{"title":"Recommendation System using Reinforcement Learning for What-If Simulation in Digital Twin","authors":"Flávia Pires, B. Ahmad, A. Moreira, P. Leitão","doi":"10.1109/INDIN45523.2021.9557372","DOIUrl":"https://doi.org/10.1109/INDIN45523.2021.9557372","url":null,"abstract":"The research about the digital twin concept is growing worldwide, especially in the industrial sector, due to the increasing digitisation level associated to Industry 4.0. The application of the digital twin concept improves performance of a system by implementing monitoring, diagnosis, optimisation, and decision support actions. In particular, the decision-making process is very time consuming since the decision-maker is presented with hundreds of different scenarios that can be simulated and assessed in a what-if perspective. Bearing this in mind, this paper proposes to integrate a digital twin-based what-if simulation with a recommendation system to improve the decision-making cycle. The recommendation system is based on a reinforcement learning technique and takes user knowledge of the system into consideration and trust in the system recommendation. The applicability of the proposed approach is presented in an assembly line case study for recommending the best configurations for the system operation, in terms of the optimal number of AGVs (Autonomous Guided Vehicles) in various scenarios. The achieved results show its successful application and highlight the benefits of using AI-based recommendation systems for what-if simulation in digital twin systems.","PeriodicalId":370921,"journal":{"name":"2021 IEEE 19th International Conference on Industrial Informatics (INDIN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127882729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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