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Hassan, M., Svadling, M. & Björsell, N. (2024). Experience from implementing digital twins for maintenance in industrial processes. Journal of Intelligent Manufacturing, 35, 875-884
Open this publication in new window or tab >>Experience from implementing digital twins for maintenance in industrial processes
2024 (English)In: Journal of Intelligent Manufacturing, ISSN 0956-5515, E-ISSN 1572-8145, Vol. 35, p. 875-884Article in journal (Refereed) Published
Abstract [en]

The capability of estimating future maintenance needs in advance and in a timely manner is a prerequisite for reliable manufacturing with high availability in a production unit. Additionally, conducting planned maintenance efforts regularly and prematurely increases the service lifetimes and utilization rates of parts, which leads to more sustainable production. The benefits of predictive maintenance are obvious, but introducing it into a facility poses various challenges. In this study, digital twins of well-functioning machines are used for predictive maintenance. The discrepancies between each physical unit and its digital twin are used to detect the maintenance needs. A thorough evaluation of the method over a period of 18 months by comparing digital twin detection results with maintenance and control system logs shows promising results. The method is successful in detecting discrepancies, and the paper describes the techniques that are used. However, not all discrepancies are related to the maintenance needs, and the evaluation identifies and discusses the most common sources of error. These are often the results of human interaction, such as parameter changes, maintenance activities and component replacement. 

Place, publisher, year, edition, pages
Springer, 2024
Keywords
Anomaly detection; Data processing; Decision support systems; Digital twin; Industrial process; Predictive maintenance; Remaining useful life
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-41107 (URN)10.1007/s10845-023-02078-4 (DOI)000929498900001 ()2-s2.0-85147769313 (Scopus ID)
Available from: 2023-02-20 Created: 2023-02-20 Last updated: 2024-02-05Bibliographically approved
Osa, J., Björsell, N., Ängskog, P., Val, I. & Mendicute, M. (2023). 60 GHz mmWave Signal Propagation Characterization in Workshop and Steel Industry. In: IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS: . Paper presented at 19th IEEE International Workshop on Factory Communication Systems, WFCS 2023, Pavia, 26-28 April 2023. IEEE
Open this publication in new window or tab >>60 GHz mmWave Signal Propagation Characterization in Workshop and Steel Industry
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2023 (English)In: IEEE International Workshop on Factory Communication Systems - Proceedings, WFCS, IEEE , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Communication systems are a key element for the industry 4.0 revolution, where the remote access to the machinery is a fundamental part for the automation of tasks related to monitoring and control of the different industrial processes. There is an increasing interest in performing such communications using a wireless medium, as they offer several advantages as a lower cost, greater flexibility or the ability to operate in moving elements. However, existing works have showed that the achievable performance in the sub-6 GHz frequency bands is insufficient to cope with all the requirements, which motivated the analysis of the millimeter wave spectrum for these use cases. Industrial environments present a harsh condition for electromagnetic wave propagation, where the abundance of reflective surfaces can present difficulties to properly exchange information. Thus, a thorough analysis and characterization of the propagation through this kind of environment is necessary to develop protocols and standards accordingly. This work provides the results of measurements carried out in two industrial facilities, which are a university workshop and a pit oven building from a steel company. Metrics of the results are computed and discussed as well, where a significantly larger losses can be seen for the pit oven measurements compared to other industrial scenarios. 

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Millimeter wave measurements; millimeter wave propagation; steel industry
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-42731 (URN)10.1109/wfcs57264.2023.10144240 (DOI)001012871100014 ()2-s2.0-85162663392 (Scopus ID)978-1-6654-6432-1 (ISBN)
Conference
19th IEEE International Workshop on Factory Communication Systems, WFCS 2023, Pavia, 26-28 April 2023
Available from: 2023-07-10 Created: 2023-07-10 Last updated: 2023-07-27Bibliographically approved
Hosseinzadeh Dadash, A. & Björsell, N. (2023). Adaptive Finite Horizon Degradation-Aware Regulator. In: Janusz Kacprzyk (Ed.), Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis: (pp. 123-132). Springer
Open this publication in new window or tab >>Adaptive Finite Horizon Degradation-Aware Regulator
2023 (English)In: Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis / [ed] Janusz Kacprzyk, Springer , 2023, p. 123-132Chapter in book (Refereed)
Abstract [en]

Predicting the failure and estimating the machine’s state of health is information that supports the production planning and maintenance management systems to increase productivity and reduce maintenance and downtime costs. However, controlling the degradation in the machines will improve the system’s reliability and resilience and make high-level decisions more accurate and reliable. To control the degradation in the machines, time should be included in the cost function as a variable, which alters the markovian properties of the system dynamic. In this article, we include the degradation cost in the quadratic cost function of the infinite horizon controller and calculate the optimal feedback according to the dynamics of the degradation using dynamic programming. It will be shown that the infinite horizon control will convert to the finite horizon, and the controller will be able to control the degradation according to the desired degradation at the desired time. In the end, with the help of simulation, we show that the degradation controller can control the degradation in the MIMO systems.

Place, publisher, year, edition, pages
Springer, 2023
Series
Studies in Systems, Decision and Control (SSDC), ISSN 2198-4182, E-ISSN 2198-4190 ; 467
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-42667 (URN)10.1007/978-3-031-27540-1_11 (DOI)2-s2.0-85162212399 (Scopus ID)978-3-031-27540-1 (ISBN)978-3-031-27539-5 (ISBN)
Available from: 2023-07-03 Created: 2023-07-03 Last updated: 2023-07-03Bibliographically approved
Hosseinzadeh Dadash, A. & Björsell, N. (2023). Adaptive Finite Horizon Degradation-Aware Regulator. In: Prof. Didier Theilliol (Ed.), 16th European Workshop on Advanced Control and Diagnosis (ACD 2022), Nancy, France, November 16-18, 2022: . Paper presented at 16th European Workshop on Advanced Control and Diagnosis (ACD 2022).
Open this publication in new window or tab >>Adaptive Finite Horizon Degradation-Aware Regulator
2023 (English)In: 16th European Workshop on Advanced Control and Diagnosis (ACD 2022), Nancy, France, November 16-18, 2022 / [ed] Prof. Didier Theilliol, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Predicting the failure and estimating the machine's state of health is information that supports the production planning and maintenance management systems to increase productivity and reduce maintenance and downtime costs. However, controlling the degradation in the machines will improve the system's reliability and resilience and make high-level decisions more accurate and reliable. To control the degradation in the machines, time should be included in the cost function as a variable, which alters the markovian properties of the system dynamic. In this article, we include the degradation cost in the quadratic cost function of the infinite horizon controller and calculate the optimal feedback according to the dynamics of the degradation using dynamic programming. It will be shown that the infinite horizon control will convert to the finite horizon, and the controller will be able to control the degradation according to the desired degradation at the desired time. In the end, with the help of simulation, we show that the degradation controller can control the degradation in the MIMO systems.

National Category
Control Engineering
Identifiers
urn:nbn:se:hig:diva-41045 (URN)
Conference
16th European Workshop on Advanced Control and Diagnosis (ACD 2022)
Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2023-12-31Bibliographically approved
Hosseinzadeh Dadash, A. & Björsell, N. (2023). Degradation Simulator for Infinite Horizon Controlled Linear Time-Invariant Systems. In: Prof. Didier Theilliol (Ed.), 16th European Workshop on Advanced Control and Diagnosis (ACD 2022), Nancy, France,  November 16-18, 2022: . Paper presented at 16th European Workshop on Advanced Control and Diagnosis (ACD 2022).
Open this publication in new window or tab >>Degradation Simulator for Infinite Horizon Controlled Linear Time-Invariant Systems
2023 (English)In: 16th European Workshop on Advanced Control and Diagnosis (ACD 2022), Nancy, France,  November 16-18, 2022 / [ed] Prof. Didier Theilliol, 2023Conference paper, Published paper (Refereed)
Abstract [en]

Diagnosis, fault prediction, and Remaining Useful Life (RUL) estimation are among the predictive maintenance research subjects used for maintenance cost reduction. Using the available data with different machine learning methods, especially deep learning methods, the accuracy of estimation and prediction of faults and RUL have increased dramatically. However, due to the statistical nature of the machine learning methods and the limitations of available datasets, physically interpreting this information might be impossible. On the other hand, controlling the degradation and faults in the machines as the optimum predictive maintenance solution needs the physical interpretation of the method's outcome. In order to test the new process-based methods for degradation and fault control, datasets with more information are required (compared to available datasets). In this article, we introduce an open-source degradation simulator for linear systems. This simulator can simulate the degradation in closed-loop machines whose dynamics are known. It is also possible to simulate different degradation models for different system parts simultaneously by adding different processes and output noise to the system. This simulator can generate enough data to test new machine learning-based predictive maintenance methods.

Keywords
Degradation Simulator, Degradation Control, Reliability Analysis Tool
National Category
Control Engineering
Research subject
Intelligent Industry
Identifiers
urn:nbn:se:hig:diva-41044 (URN)
Conference
16th European Workshop on Advanced Control and Diagnosis (ACD 2022)
Available from: 2023-02-08 Created: 2023-02-08 Last updated: 2023-12-31Bibliographically approved
Hosseinzadeh Dadash, A. & Björsell, N. (2023). Degradation Simulator for Infinite Horizon Controlled Linear Time-Invariant Systems. In: Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis: (pp. 155-164). Springer
Open this publication in new window or tab >>Degradation Simulator for Infinite Horizon Controlled Linear Time-Invariant Systems
2023 (English)In: Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis, Springer , 2023, p. 155-164Chapter in book (Refereed)
Abstract [en]

Diagnosis, fault prediction, and Remaining Useful Life (RUL) estimation are among the predictive maintenance research subjects used for maintenance cost reduction. Using the available data with different machine learning methods, especially deep learning methods, the accuracy of estimation and prediction of faults and RUL have increased dramatically. However, due to the statistical nature of the machine learning methods and the limitations of available datasets, physically interpreting this information might be impossible. On the other hand, controlling the degradation and faults in the machines as the optimum predictive maintenance solution needs the physical interpretation of the method’s outcome. In order to test the new process-based methods for degradation and fault control, datasets with more information are required (compared to available datasets). In this article, we introduce an open-source degradation simulator for linear systems. This simulator can simulate the degradation in closed-loop machines whose dynamics are known. It is also possible to simulate different degradation models for different system parts simultaneously by adding different processes and output noise to the system. This simulator can generate enough data to test new machine learning-based predictive maintenance methods. 

Place, publisher, year, edition, pages
Springer, 2023
Series
Studies in Systems, Decision and Control (SSDC), ISSN 2198-4182, E-ISSN 2198-4190 ; 467
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-42668 (URN)10.1007/978-3-031-27540-1_14 (DOI)2-s2.0-85162200654 (Scopus ID)978-3-031-27540-1 (ISBN)978-3-031-27539-5 (ISBN)
Available from: 2023-07-03 Created: 2023-07-03 Last updated: 2023-07-03Bibliographically approved
Bemani, A. & Björsell, N. (2023). Low-Latency Collaborative Predictive Maintenance: Over-the-Air Federated Learning in Noisy Industrial Environments. Sensors, 23(18), Article ID 7840.
Open this publication in new window or tab >>Low-Latency Collaborative Predictive Maintenance: Over-the-Air Federated Learning in Noisy Industrial Environments
2023 (English)In: Sensors, E-ISSN 1424-8220, Vol. 23, no 18, article id 7840Article in journal (Refereed) Published
Abstract [en]

The emergence of Industry 4.0 has revolutionized the industrial sector, enabling the development of compact, precise, and interconnected assets. This transformation has not only generated vast amounts of data but also facilitated the migration of learning and optimization processes to edge devices. Consequently, modern industries can effectively leverage this paradigm through distributed learning to define product quality and implement predictive maintenance (PM) strategies. While computing speeds continue to advance rapidly, the latency in communication has emerged as a bottleneck for fast edge learning, particularly in time-sensitive applications such as PM. To address this issue, we explore Federated Learning (FL), a privacy-preserving framework. FL entails updating a global AI model on a parameter server (PS) through aggregation of locally trained models from edge devices. We propose an innovative approach: analog aggregation over-the-air of updates transmitted concurrently over wireless channels. This leverages the waveform-superposition property in multi-access channels, significantly reducing communication latency compared to conventional methods. However, it is vulnerable to performance degradation due to channel properties like noise and fading. In this study, we introduce a method to mitigate the impact of channel noise in FL over-the-air communication and computation (FLOACC). We integrate a novel tracking-based stochastic approximation scheme into a standard federated stochastic variance reduced gradient (FSVRG). This effectively averages out channel noise's influence, ensuring robust FLOACC performance without increasing transmission power gain. Numerical results confirm our approach's superior communication efficiency and scalability in various FL scenarios, especially when dealing with noisy channels. Simulation experiments also highlight significant enhancements in prediction accuracy and loss function reduction for analog aggregation in over-the-air FL scenarios.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
analog aggregation; channel noise; low latency; over-the-air federated learning; predictive maintenance
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-43081 (URN)10.3390/s23187840 (DOI)001074274300001 ()37765899 (PubMedID)2-s2.0-85172735809 (Scopus ID)
Available from: 2023-09-29 Created: 2023-09-29 Last updated: 2024-01-10Bibliographically approved
Osa, J., Björsell, N., Val, I. & Mendicute, M. (2023). Measurement based stochastic channel model for 60 GHz mmWave industrial communications. IEEE Open Journal of the Industrial Electronics Society, 4, 603-617
Open this publication in new window or tab >>Measurement based stochastic channel model for 60 GHz mmWave industrial communications
2023 (English)In: IEEE Open Journal of the Industrial Electronics Society, E-ISSN 2644-1284, Vol. 4, p. 603-617Article in journal (Refereed) Published
Abstract [en]

Communications in the mmWave spectrum are gaining relevance in the last years as they are a promising candidate to cope with the increasing demand of throughput and latency in different use cases. Nowadays several efforts have been carried out to characterize the propagation medium of these signals with the aim of designing their corresponding communication protocols accordingly, and a wide variety of both outdoor/indoor locations have already been studied. However, very few works endorse industrial scenarios, which are particularly demanding due to their stringent requirements in terms of reliability, determinism and latency. This work aims to provide an insight of the propagation of 60 GHz mmWave signals in a typical industrial workshop in order to explore the particularities of this kind of scenario. In order to achieve this, an extensive measurement campaign has been carried out in this environment and a stochastic channel model has been proposed and validated.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Industrial communication, millimeter wave measurements, millimeter wave propagation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-43315 (URN)10.1109/ojies.2023.3334299 (DOI)2-s2.0-85178032718 (Scopus ID)
Available from: 2023-11-23 Created: 2023-11-23 Last updated: 2024-01-02Bibliographically approved
Hosseinzadeh Dadash, A. & Björsell, N. (2023). Optimal Degradation-Aware Control Using Process-Controlled Sparse Bayesian Learning. Processes, 11(11), Article ID 3229.
Open this publication in new window or tab >>Optimal Degradation-Aware Control Using Process-Controlled Sparse Bayesian Learning
2023 (English)In: Processes, ISSN 2227-9717, Vol. 11, no 11, article id 3229Article in journal (Refereed) Published
Abstract [en]

Efficient production planning hinges on reducing costs and maintaining output quality, with machine degradation management as a key factor. The traditional approaches to control this degradation face two main challenges: high costs associated with physical modeling and a lack of physical interpretability in machine learning methods. Addressing these issues, our study presents an innovative solution focused on controlling the degradation, a common cause of machine failure. We propose a method that integrates machine degradation as a virtual state within the system model, utilizing relevance vector machine-based identification designed in a way that offers physical interpretability. This integration maximizes the machine’s operational lifespan. Our approach merges a physical machine model with a physically interpretable data-driven degradation model, effectively tackling the challenges in physical degradation modeling and accessibility to the system disturbance model. By embedding degradation into the system’s state-space model, we simplify implementation and address stability issues. The results demonstrate that our method effectively controls degradation and significantly increases the machine’s mean time to failure. This represents a significant advancement in production planning, offering a cost-effective and interpretable method for managing machine degradation.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
degradation control; fault control; improve production reliability; process-guided learning
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hig:diva-43422 (URN)10.3390/pr11113229 (DOI)001115191500001 ()2-s2.0-85177874866 (Scopus ID)
Available from: 2023-12-11 Created: 2023-12-11 Last updated: 2023-12-21Bibliographically approved
Andersson, R. & Björsell, N. (2023). The Technical Challenges in Orthotic Exoskeleton Robots with Future Directions: a Review Paper. In: : . Paper presented at ITIKD-2023: International Conference on IT Innovations and Knowledge Discovery, Manama, Bahrain, March 15-16, 2023. IEEE
Open this publication in new window or tab >>The Technical Challenges in Orthotic Exoskeleton Robots with Future Directions: a Review Paper
2023 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The robotic wearable exoskeletons have been developed due to various advantages offered by these devices. These advantages are manifested by integrating the human and a robot into one system under the user's control, which motivated researchers to develop different exoskeletons through years. However, several advances in exoskeleton; still, dealing with the various technical challenges in designing these impressive devices is inevitably. This paper aims to introduce informative resources and quick guidance of various technical challenges as such information is critical for exoskeleton development. The constructive discussion is intended to encourage researchers, innovators and academia to be aware of these challenges. Finally, the contemporary research gaps with various challenges have been highlighted, which remain to be solved as well as some future directions in this field that will have far-reaching effects on developing exoskeletons.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Design Aspects; Exoskeleton Future Directions; Orthoses Exoskeletons; Technical Challenges; Wearable Robots
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Health-Promoting Work, Digital shapeshifting
Identifiers
urn:nbn:se:hig:diva-40338 (URN)10.1109/ITIKD56332.2023.10099850 (DOI)2-s2.0-85158116378 (Scopus ID)978-1-6654-6372-0 (ISBN)
Conference
ITIKD-2023: International Conference on IT Innovations and Knowledge Discovery, Manama, Bahrain, March 15-16, 2023
Available from: 2022-11-01 Created: 2022-11-01 Last updated: 2023-09-15Bibliographically approved
Projects
Flexible Models for Smart Maintenance [2017-04807_Vinnova]; University of Gävle; Publications
Mattsson, P., Zachariah, D. & Björsell, N. (2019). Flexible Models for Smart Maintenance. In: Proceedings 2019 IEEE International Conference on Industrial Technology (ICIT): . Paper presented at 20th IEEE International Conference on Industrial Technology (ICIT), 13-15 February 2019, Melbourne, Australia (pp. 1772-1777). IEEE
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5429-7223

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