| 1. |
- De Donato, Lorenzo, et al.
(author)
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Artificial intelligence in railways : current applications, challenges, and ongoing research
- 2023
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In: Handbook on Artificial Intelligence and Transport. - : Edward Elgar Publishing. - 9781803929538 - 9781803929545 ; , s. 249-283
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Book chapter (peer-reviewed)abstract
- This chapter presents applications, challenges, and opportunities for the integration of artificial intelligence in rail transport, based on the current results of the European project Roadmaps for AI integration in the rail sector (RAILS). Past and ongoing research directions are briefly outlined, and then the regulatory landscape is presented as well as the main barriers to overcome. Some technical aspects are addressed to provide some valuable references, and a high-level description of ongoing research work is given, spanning from innovative studies on smart maintenance, collision avoidance, delay prediction, and incident attribution analysis to visionary scenarios such as intelligent control and virtual coupling.
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| 2. |
- De Donato, Lorenzo, et al.
(author)
-
Artificial intelligence in railways : Current applications, challenges, and ongoing research
- 2023
-
In: Handbook on Artificial Intelligence and Transport. - : Edward Elgar Publishing. - 9781803929545 - 9781803929538 ; , s. 249-283
-
Book chapter (peer-reviewed)abstract
- This chapter presents applications, challenges, and opportunities for the integration of artificial intelligence in rail transport, based on the current results of the European project Roadmaps for AI integration in the rail sector (RAILS). Past and ongoing research directions are briefly outlined, and then the regulatory landscape is presented as well as the main barriers to overcome. Some technical aspects are addressed to provide some valuable references, and a high-level description of ongoing research work is given, spanning from innovative studies on smart maintenance, collision avoidance, delay prediction, and incident attribution analysis to visionary scenarios such as intelligent control and virtual coupling.
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| 3. |
- Falcone, Paolo, 1977, et al.
(author)
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A Model of Torque Generation Process in Direct Injection Diesel Engines
- 2003
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In: International Conference on Engines for Automobile, Capri, Napoli, Italy, September 2003.
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Conference paper (peer-reviewed)abstract
- Aiming at integrating a reliable combustion torque estimator into engine controllers, we present encouraging identification and validation results for a combustion model of DI diesel engine. The model is sufficiently simple to be possibly included into future engine control strategies and estimate techniques. The experimental data refer to a turbocharged engine BMW MD47 1900cc during a transient phase.
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| 4. |
- Falcone, Paolo, 1977, et al.
(author)
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Torque Generation Model for Diesel Engine
- 2003
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In: Conference on Decision and Control, Maui, Hawaii USA, December 2003. ; , s. 1771-1776
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Conference paper (peer-reviewed)abstract
- In this paper a combustion model of Direct Injection Diesel engine is proposed to calculate the in-cylinder pressure and a crank-slider mechanism model to calculate instantaneous indicated torque. The crankshaft is modelled as a rigid body. The parameters of both models are identified via non-linear least square optimization algorithm. The data, used for the identification procedure, are purposely obtained through experiments on a diesel turbocharged BMW MD47 1900cc with a dynamic test-bench.
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| 5. |
- Flammini, Francesco, Senior Lecturer, 1978-, et al.
(author)
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Towards Railway Virtual Coupling
- 2018
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In: 2018 IEEE International Conference on Electrical Systems for Aircraft, Railway, Ship Propulsion and Road Vehicles & International Transportation Electrification Conference (ESARS-ITEC). - : IEEE. - 9781538641927
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Conference paper (peer-reviewed)
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| 6. |
- Hult, Robert, 1984, et al.
(author)
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Design and experimental validation of a cooperative driving control architecture for the Grand Cooperative Driving Challenge 2016
- 2018
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In: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 19:4, s. 1290-1301
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Journal article (peer-reviewed)abstract
- In this paper, we present the cooperative driving sys- tem developed by the Chalmers Car team for the Grand Cooper- ative Driving Challenge 2016. The paper gives an overview of the system architecture and describes in detail the communication, signal processing and decision-making sub-systems. Experimental results demonstrate the system’s performance and operation according to the rules and requirements of the competition.
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| 7. |
- Saman Azari, Mehdi, et al.
(author)
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A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0
- 2023
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In: IEEE Access. - : IEEE. - 2169-3536. ; 11, s. 12887-12910
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Research review (peer-reviewed)abstract
- The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implied the usage of more effective data-driven predictive maintenance approaches, including those based on machine learning. Although those approaches are becoming increasingly popular, most of the traditional machine learning and deep learning algorithms experience the following three major challenges: 1) lack of training data (especially faulty data), 2) incompatible computation power, and 3) discrepancy in data distribution. A new data-driven technique, such as transfer learning, can be developed to overcome the issues related to traditional machine learning and deep learning for predictive maintenance. Motivated by the recent big interest towards transfer learning within computer science and artificial intelligence, in this paper we provide a systematic literature review addressing related research with a focus on predictive maintenance. The review aims to define transfer learning in the context of predictive maintenance by introducing a specific taxonomy based on relevant perspectives. We also discuss current advances, challenges, open-source datasets, and future directions of transfer learning applications in predictive maintenance from both theoretical and practical viewpoints.
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| 8. |
- Saman Azari, Mehdi, et al.
(author)
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Data-Driven Fault Diagnosis of Once-through Benson Boilers
- 2019
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In: 2019 4th International Conference on System Reliability and Safety (ICSRS). - : IEEE Press. - 9781728147819 - 9781728147802 ; , s. 345-354
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Conference paper (peer-reviewed)abstract
- Fault diagnosis (FD) of once-through Benson boilers, as a crucial equipment of many thermal power plants, is of paramount importance to guarantee continuous performance. In this study, a new fault diagnosis methodology based on data-driven methods is presented to diagnose faults in once-through Benson boilers. The present study tackles this issue by adopting a combination of data-driven methods to improve the robustness of FD blocks. For this purpose, one-class versions of minimum spanning tree and K-means algorithms are employed to handle the strong interaction between measurements and part load operation and also to reduce computation time and system training error. Furthermore, an adaptive neuro-fuzzy inference system algorithm is adopted to improve accuracy and robustness of the proposed fault diagnosing system by fusion of the output of minimum spanning tree (MST) and K-means algorithms. Performance of the presented scheme against six major faults is then assessed by analyzing several test scenario.
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| 9. |
- Saman Azari, Mehdi, et al.
(author)
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Improving Resilience in Cyber-Physical Systems based on Transfer Learning
- 2022
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In: 2022 IEEE International Conference on Cyber Security and Resilience (CSR). - : IEEE. - 9781665499521 - 9781665499538 ; , s. 203-208
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Conference paper (peer-reviewed)abstract
- An essential aspect of resilience within Cyber-Physical Systems stands in their capacity of early detection of faults before they generate failures. Faults can be of any origin, either natural or intentional. Detection of faults enables predictive maintenance, where faults are managed through diagnosis and prognosis. In this paper we focus on intelligent predictive maintenance based on a class of machine learning techniques, namely transfer learning, which overcomes some limitations of traditional approaches in terms of availability of appropriate training datasets and discrepancy of data distribution. We provide a conceptual approach and a reference architecture supporting transfer learning within intelligent predictive maintenance applications for cyber-physical systems. The approach is based on the emerging paradigms of Industry 4.0, the industrial Internet of Things, and Digital Twins hosting run-time models for providing the training data set for the target domain. Although we mainly focus on health monitoring and prognostics of industrial machinery as a reference application, the general approach is suitable to both physical- and cyber-threat detection, and to any combination of them within the same system, or even in complex systems-of-systems such as critical infrastructures. We show how transfer learning can aid predictive maintenance with intelligent fault detection, diagnosis and prognosis, and describe some the challenges that need to be addressed for its effective adoption in real industrial applications.
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