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Flammini, Francesco, Senior LecturerORCID iD iconorcid.org/0000-0002-2833-7196
Publications (10 of 118) Show all publications
Saman Azari, M., Flammini, F., Santini, S. & Caporuscio, M. (2023). A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0. IEEE Access, 11, 12887-12910
Open this publication in new window or tab >>A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0
2023 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 11, p. 12887-12910Article, review/survey (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Maintenance engineering, Predictive maintenance, Prognostics and health management, Transfer learning, Artificial intelligence, Fault diagnosis, domain adaptation, fault detection, fault prognosis
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-119802 (URN)10.1109/ACCESS.2023.3239784 (DOI)000933765200001 ()2-s2.0-85147287812 (Scopus ID)
Available from: 2023-03-16 Created: 2023-03-16 Last updated: 2024-01-17Bibliographically approved
De Donato, L., Marrone, S., Flammini, F., Sansone, C., Vittorini, V., Nardone, R., . . . Bernaudin, F. (2023). Intelligent detection of warning bells at level crossings through deep transfer learning for smarter railway maintenance. Engineering applications of artificial intelligence, 123, Article ID 106405.
Open this publication in new window or tab >>Intelligent detection of warning bells at level crossings through deep transfer learning for smarter railway maintenance
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2023 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 123, article id 106405Article in journal (Refereed) Published
Abstract [en]

Level Crossings are among the most critical railway assets, concerning both the risk of accidents and their maintainability, due to intersections with promiscuous traffic and difficulties in remotely monitoring their health status. Failures can be originated from several factors, including malfunctions in the bar mechanisms and warning devices, such as light signals and bells. This paper focuses on the intelligent detection of anomalies in warning bells through non-intrusive acoustic monitoring by: (1) introducing a new concept for autonomous monitoring of level crossings; (2) generating and sharing a specific dataset collecting relevant audio signals from publicly available audio recordings; (3) implementing and evaluating a solution combining deep learning and transfer learning for warning bell detection. The results show a high accuracy in detecting anomalies and suggest viability of the approach in real-world applications, especially where network cameras with on-board microphones are installed for multi-purpose level crossing surveillance.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Audio analytics, Artificial intelligence, Machine learning, Anomaly detection, Predictive maintenance, Railway safety
National Category
Information Systems Transport Systems and Logistics
Research subject
Computer and Information Sciences Computer Science, Information Systems
Identifiers
urn:nbn:se:lnu:diva-123539 (URN)10.1016/j.engappai.2023.106405 (DOI)001013279100001 ()2-s2.0-85160199789 (Scopus ID)
Available from: 2023-08-09 Created: 2023-08-09 Last updated: 2023-08-24Bibliographically approved
De Donato, L., Dirnfeld, R., Somma, A., De Benedictis, A., Flammini, F., Marrone, S., . . . Vittorini, V. (2023). Towards AI-assisted digital twins for smart railways: preliminary guideline and reference architecture. Journal of Reliable Intelligent Environments, 9(3), 303-317
Open this publication in new window or tab >>Towards AI-assisted digital twins for smart railways: preliminary guideline and reference architecture
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2023 (English)In: Journal of Reliable Intelligent Environments, ISSN 2199-4668, Vol. 9, no 3, p. 303-317Article in journal (Refereed) Published
Abstract [en]

In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow.

Place, publisher, year, edition, pages
Springer, 2023
National Category
Computer Sciences Transport Systems and Logistics
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-126325 (URN)10.1007/s40860-023-00208-6 (DOI)2-s2.0-85161629381 (Scopus ID)
Available from: 2024-01-10 Created: 2024-01-10 Last updated: 2024-02-01Bibliographically approved
Tang, R., De Donato, L., Besinovic, N., Flammini, F., Goverde, R. M. P., Lin, Z., . . . Wang, Z. (2022). A literature review of Artificial Intelligence applications in railway systems. Transportation Research Part C: Emerging Technologies, 140, Article ID 103679.
Open this publication in new window or tab >>A literature review of Artificial Intelligence applications in railway systems
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2022 (English)In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 140, article id 103679Article, review/survey (Refereed) Published
Abstract [en]

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges.

Place, publisher, year, edition, pages
Saunders Elsevier, 2022
Keywords
Artificial Intelligence, Railways, Transportation, Machine Learning, Autonomous driving, Maintenance, Smart mobility, Train control, Traffic management
National Category
Transport Systems and Logistics Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:lnu:diva-114332 (URN)10.1016/j.trc.2022.103679 (DOI)000802044600006 ()2-s2.0-85129622349 (Scopus ID)
Available from: 2022-06-17 Created: 2022-06-17 Last updated: 2022-08-23Bibliographically approved
Donato, L. D., Flammini, F., Marrone, S., Mazzariello, C., Nardone, R., Sansone, C. & Vittorini, V. (2022). A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance. IEEE Access, 10, 65376-65400
Open this publication in new window or tab >>A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance
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2022 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 65376-65400Article in journal (Refereed) Published
Abstract [en]

Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
CNN, Computer vision, fault detection, Focusing, inspection, machine learning, Maintenance engineering, Rail transportation, Rails, Sensors, smart railways, Task analysis
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-120898 (URN)10.1109/ACCESS.2022.3183102 (DOI)000815504500001 ()2-s2.0-85132784354 (Scopus ID)
Available from: 2023-05-24 Created: 2023-05-24 Last updated: 2023-11-08Bibliographically approved
Flammini, F., De Donato, L., Fantechi, A. & Vittorini, V. (2022). A Vision of Intelligent Train Control. In: Collart-Dutilleul, S., Haxthausen, A.E., Lecomte, T. (Ed.), Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification. RSSRail 2022.: . Paper presented at Conference of 4th International Conference on Reliability, Safety and Security of Railway Systems, RSSRail 2022 ; Conference Date: 1 June 2022 Through 2 June 2022 (pp. 192-208). Springer, 13294
Open this publication in new window or tab >>A Vision of Intelligent Train Control
2022 (English)In: Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification. RSSRail 2022. / [ed] Collart-Dutilleul, S., Haxthausen, A.E., Lecomte, T., Springer, 2022, Vol. 13294, p. 192-208Conference paper, Published paper (Refereed)
Abstract [en]

The progressive adoption of artificial intelligence and advanced communication technologies within railway control and automation has brought up a huge potential in terms of optimisation, learning and adaptation, due to the so-called “self-x” capabilities; however, it has also raised several dependability concerns due to the lack of measurable trust that is needed for certification purposes. In this paper, we provide a vision of future train control that builds upon existing automatic train operation, protection, and supervision paradigms. We will define the basic concepts for autonomous driving in digital railways, and summarise its feasibility in terms of challenges and opportunities, including explainability, autonomic computing, and digital twins. Due to the clear architectural distinction, automatic train protection can act as a safety envelope for intelligent operation to optimise energy, comfort, and capacity, while intelligent protection based on signal recognition and obstacle detection can improve safety through advanced driving assistance. © 2022, Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Springer, 2022
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 13294
Keywords
Automation, Autonomous vehicles, Machine learning, Obstacle detectors, Automatic train protections, Autonomous driving, Certification, Communicationtechnology, Intelligent trains, Railway control, Safety envelope, Smart railway, Trains control, Trustworthy AI, Railroads
National Category
Robotics
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-122546 (URN)10.1007/978-3-031-05814-1_14 (DOI)2-s2.0-85131150379 (Scopus ID)9783031058134 (ISBN)9783031058141 (ISBN)
Conference
Conference of 4th International Conference on Reliability, Safety and Security of Railway Systems, RSSRail 2022 ; Conference Date: 1 June 2022 Through 2 June 2022
Available from: 2023-06-22 Created: 2023-06-22 Last updated: 2023-06-22Bibliographically approved
Besinovic, N., De Donato, L., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., . . . Vittorini, V. (2022). Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications. IEEE transactions on intelligent transportation systems (Print), 23(9), 14011-14024
Open this publication in new window or tab >>Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications
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2022 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 23, no 9, p. 14011-14024Article in journal (Refereed) Published
Abstract [en]

Artificial Intelligence (AI) is becoming pervasive in most engineering domains, and railway transport is no exception. However, due to the plethora of different new terms and meanings associated with them, there is a risk that railway practitioners, as several other categories, will get lost in those ambiguities and fuzzy boundaries, and hence fail to catch the real opportunities and potential of machine learning, artificial vision, and big data analytics, just to name a few of the most promising approaches connected to AI. The scope of this paper is to introduce the basic concepts and possible applications of AI to railway academics and practitioners. To that aim, this paper presents a structured taxonomy to guide researchers and practitioners to understand AI techniques, research fields, disciplines, and applications, both in general terms and in close connection with railway applications such as autonomous driving, maintenance, and traffic management. The important aspects of ethics and explainability of AI in railways are also introduced. The connection between AI concepts and railway subdomains has been supported by relevant research addressing existing and planned applications in order to provide some pointers to promising directions.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
Rail transportation, Artificial intelligence, Taxonomy, Rails, Maintenance engineering, Software, Safety, railway transport, machine learning, computer vision, traffic management, predictive maintenance
National Category
Transport Systems and Logistics Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-117498 (URN)10.1109/TITS.2021.3131637 (DOI)000858988900008 ()2-s2.0-85121815705 (Scopus ID)
Available from: 2022-11-11 Created: 2022-11-11 Last updated: 2023-04-06Bibliographically approved
Rajabi, S., Saman Azari, M., Santini, S. & Flammini, F. (2022). Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier. Expert systems with applications, 206, Article ID 117754.
Open this publication in new window or tab >>Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier
2022 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 206, article id 117754Article in journal (Refereed) Published
Abstract [en]

Rotating equipment is considered as a key component in several industrial sectors. In fact, the continuous operation of many industrial machines such as sub-sea pumps and gas turbines relies on the correct performance of their rotating equipment. In order to reduce the probability of malfunctions in this equipment, condition monitoring, and fault diagnosis systems are essential. In this work, a novel approach is proposed to perform fault diagnosis in rotating equipment based on permutation entropy, signal processing, and artificial intelligence. To that aim, vibration signals are employed for an indication of bearing performance. In order to facilitate fault diagnosis, fault detection and isolation are performed in two separate steps. As first, once a vibration signal is received, the faulty state of the bearing is determined by permutation entropy. In case a faulty state is detected, the fault type is determined using an approach based on signal processing and artificial intelligence. Wavelet packet transform and envelope analysis of the vibration signals are utilized to extract the frequency components of the fault. The proposed approach allows for the automatic selection of a frequency band that includes the characteristic resonance frequency of the fault, which is subject to change in different operational conditions. The method works by extracting the proper features of the signals that are used to decide about the faulty bearing’s condition by a multi-output adaptive neuro-fuzzy inference system classifier. The effectiveness of the approach is assessed by the Case Western Reserve University dataset: the analysis demonstrates the proposed method’s capabilities in accurately diagnosing faults in rotating equipment as compared to existing approaches.

Place, publisher, year, edition, pages
Elsevier, 2022
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-115923 (URN)10.1016/j.eswa.2022.117754 (DOI)000832966000003 ()2-s2.0-85132533688 (Scopus ID)
Available from: 2022-08-26 Created: 2022-08-26 Last updated: 2023-04-06Bibliographically approved
Saman Azari, M., Flammini, F. & Santini, S. (2022). Improving Resilience in Cyber-Physical Systems based on Transfer Learning. In: 2022 IEEE International Conference on Cyber Security and Resilience (CSR): . Paper presented at 2022 IEEE International Conference on Cyber Security and Resilience (CSR), 27-29 July 2022, Rhodes, Greece (pp. 203-208). IEEE
Open this publication in new window or tab >>Improving Resilience in Cyber-Physical Systems based on Transfer Learning
2022 (English)In: 2022 IEEE International Conference on Cyber Security and Resilience (CSR), IEEE, 2022, p. 203-208Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2022
National Category
Computer and Information Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-115927 (URN)10.1109/CSR54599.2022.9850282 (DOI)000857435100031 ()2-s2.0-85137355912 (Scopus ID)9781665499521 (ISBN)9781665499538 (ISBN)
Conference
2022 IEEE International Conference on Cyber Security and Resilience (CSR), 27-29 July 2022, Rhodes, Greece
Available from: 2022-08-26 Created: 2022-08-26 Last updated: 2023-04-06Bibliographically approved
Dirnfeld, R., De Donato, L., Flammini, F., Saman Azari, M. & Vittorini, V. (2022). Railway Digital Twins and Artificial Intelligence: Challenges and Design Guidelines. In: Stefano Marrone, Martina De Sanctis, Imre Kocsis, Rasmus Adler, Richard Hawkins, Philipp Schleiß, Francesco Flammini, Valeria Vittorini (Ed.), Dependable Computing – EDCC 2022 Workshops. EDCC 2022: . Paper presented at 18th European Dependable Computing Conference, EDCC 2022, Zaragoza12-15 September 2022 (pp. 102-113). Springer
Open this publication in new window or tab >>Railway Digital Twins and Artificial Intelligence: Challenges and Design Guidelines
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2022 (English)In: Dependable Computing – EDCC 2022 Workshops. EDCC 2022 / [ed] Stefano Marrone, Martina De Sanctis, Imre Kocsis, Rasmus Adler, Richard Hawkins, Philipp Schleiß, Francesco Flammini, Valeria Vittorini, Springer, 2022, p. 102-113Conference paper, Published paper (Refereed)
Abstract [en]

In the last years, there has been a growing interest in the emerging concept of Digital Twins (DTs) among software engineers and researchers. DTs represent a promising paradigm to enhance the predictability, safety, and reliability of cyber-physical systems. They can play a key role in different domains, as it is also witnessed by several ongoing standardisation activities. However, several challenging issues have to be faced in order to effectively adopt DTs, in particular when dealing with critical systems. This work provides a review of the scientific literature on DTs in the railway sector, with a special focus on their relationship with Artificial Intelligence. Challenges and opportunities for the usage of DTs in railways have been identified, with interoperability being the most discussed challenge. One difficulty is to transmit operational data in real-time from edge systems to the cloud in order to achieve timely decision making. We also provide some guidelines to support the design of DTs with a focus on machine learning for railway maintenance. 

Place, publisher, year, edition, pages
Springer, 2022
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1656
National Category
Computer Sciences
Research subject
Computer and Information Sciences Computer Science, Computer Science
Identifiers
urn:nbn:se:lnu:diva-118120 (URN)10.1007/978-3-031-16245-9_8 (DOI)000871946900008 ()2-s2.0-85138777223 (Scopus ID)9783031162442 (ISBN)9783031162459 (ISBN)
Conference
18th European Dependable Computing Conference, EDCC 2022, Zaragoza12-15 September 2022
Available from: 2023-01-03 Created: 2023-01-03 Last updated: 2023-04-06Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-2833-7196

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