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D'Cruze, R. S., Ahmed, M. U., Bengtsson, M., Rehman, A. U., Funk, P. & Sohlberg, R. (2024). A Case Study on Ontology Development for AI Based Decision Systems in Industry. In: Lecture Notes in Mechanical Engineering: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023 (pp. 693-706). Springer Science and Business Media Deutschland GmbH
Åpne denne publikasjonen i ny fane eller vindu >>A Case Study on Ontology Development for AI Based Decision Systems in Industry
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2024 (engelsk)Inngår i: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, s. 693-706Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Ontology development plays a vital role as it provides a structured way to represent and organize knowledge. It has the potential to connect and integrate data from different sources, enabling a new class of AI-based services and systems such as decision support systems and recommender systems. However, in large manufacturing industries, the development of such ontology can be challenging. This paper presents a use case of an application ontology development based on machine breakdown work orders coming from a Computerized Maintenance Management System (CMMS). Here, the ontology is developed using a Knowledge Meta Process: Methodology for Ontology-based Knowledge Management. This ontology development methodology involves steps such as feasibility study, requirement specification, identifying relevant concepts and relationships, selecting appropriate ontology languages and tools, and evaluating the resulting ontology. Additionally, this ontology is developed using an iterative process and in close collaboration with domain experts, which can help to ensure that the resulting ontology is accurate, complete, and useful for the intended application. The developed ontology can be shared and reused across different AI systems within the organization, facilitating interoperability and collaboration between them. Overall, having a well-defined ontology is critical for enabling AI systems to effectively process and understand information.

sted, utgiver, år, opplag, sider
Springer Science and Business Media Deutschland GmbH, 2024
Emneord
Custom NER, Industrial AI, Machine failures prediction, Ontology development, Artificial intelligence, Decision support systems, Interoperability, Iterative methods, Knowledge management, AI systems, Case-studies, Decision systems, Failures prediction, Machine failure, Machine failure prediction, Ontology's, Ontology
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-65369 (URN)10.1007/978-3-031-39619-9_51 (DOI)2-s2.0-85181980940 (Scopus ID)9783031396182 (ISBN)
Konferanse
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Tilgjengelig fra: 2024-01-17 Laget: 2024-01-17 Sist oppdatert: 2024-01-17bibliografisk kontrollert
Giacomossi, L., Maximo, M. R., Sundelius, N., Funk, P., Brancalion, J. F. & Sohlberg, R. (2024). Cooperative Search and Rescue with Drone Swarm. In: Lecture Notes in Mechanical Engineering: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023 (pp. 381-393). Springer Science and Business Media Deutschland GmbH
Åpne denne publikasjonen i ny fane eller vindu >>Cooperative Search and Rescue with Drone Swarm
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2024 (engelsk)Inngår i: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, s. 381-393Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Unmanned Aerial Vehicle (UAV) swarms, also known as drone swarms, have been a subject of extensive research due to their potential to enhance monitoring, surveillance, and search missions. Coordinating several drones flying simultaneously presents a challenge in increasing their level of automation and intelligence to improve strategic organization. To address this challenge, we propose a solution that uses hill climbing, potential fields, and search strategies in conjunction with a probability map to coordinate a UAV swarm. The UAVs are autonomous and equipped with distributed intelligence to facilitate a cooperative search application. Our results show the effectiveness of the swarm, indicating that this approach is a promising approach to addressing this problem.

sted, utgiver, år, opplag, sider
Springer Science and Business Media Deutschland GmbH, 2024
Emneord
Cooperative, Drones, Search and rescue, Swarm, UAV, Antennas, Aerial vehicle, Cooperative search, Levels of automation, Search missions, Strategic organizations, Surveillance missions, Unmanned aerial vehicle
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-65361 (URN)10.1007/978-3-031-39619-9_28 (DOI)2-s2.0-85181981333 (Scopus ID)9783031396182 (ISBN)
Konferanse
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Tilgjengelig fra: 2024-01-17 Laget: 2024-01-17 Sist oppdatert: 2024-01-17bibliografisk kontrollert
Sundelius, N., Funk, P. & Sohlberg, R. (2024). Simulation Environment Evaluating AI Algorithms for Search Missions Using Drone Swarms. In: Lecture Notes in Mechanical Engineering: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023 (pp. 191-204). Springer Science and Business Media Deutschland GmbH
Åpne denne publikasjonen i ny fane eller vindu >>Simulation Environment Evaluating AI Algorithms for Search Missions Using Drone Swarms
2024 (engelsk)Inngår i: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, s. 191-204Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Search missions for objects are relevant in both industrial and civilian context, such as searching for a missing child in a forest or to locating equipment in a building or large factory. To send out a drone swarm to quickly locate a misplaced item in a factory, a missing machine on a building site or a missing child in a forest is very similar. Image-based Machine Learning algorithms are now so powerful that they can be trained to identify objects with high accuracy in real time. The next challenge is to perform the search as efficiently as possible, using as little time and energy as possible. If we have information about the area to search, we can use heuristic and probabilistic methods to perform an efficient search. In this paper, we present a case study where we developed a method and approach to evaluate different search algorithms enabling the selection of the most suitable, i.e., most efficient search algorithm for the task at hand. A couple of probabilistic and heuristic search methods were implemented for testing purposes, and they are the following: Bayesian Search together with a Hill Climbing search algorithm and Bayesian Search together with an A-star search algorithm. A swarm adapted lawn mower search strategy is also implemented. In our case study, we see that the performance of the search heavily depends on the area to search in and domain knowledge, e.g., knowledge about how a child is expected to move through a forest area when lost. In our tests, we see that there are significant gains to be made by selecting a search algorithm suitable for the search context at hand.

sted, utgiver, år, opplag, sider
Springer Science and Business Media Deutschland GmbH, 2024
Emneord
AI, Drone Swarm, Drones, Optimization, Search and rescue, Search missions, Simulation environment, Swarm, Heuristic algorithms, Heuristic methods, Lawn mowers, Learning algorithms, Machine learning, Statistical tests, Bayesian, Case-studies, Missing children, Optimisations, Search Algorithms
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-65359 (URN)10.1007/978-3-031-39619-9_14 (DOI)2-s2.0-85181984345 (Scopus ID)9783031396182 (ISBN)
Konferanse
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Tilgjengelig fra: 2024-01-17 Laget: 2024-01-17 Sist oppdatert: 2024-01-17bibliografisk kontrollert
Olsson, E., Funk, P. & Sohlberg, R. (2024). Using a Drone Swarm/Team for Safety, Security and Protection Against Unauthorized Drones. In: Lecture Notes in Mechanical Engineering: . Paper presented at 7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023 (pp. 263-277). Springer Science and Business Media Deutschland GmbH
Åpne denne publikasjonen i ny fane eller vindu >>Using a Drone Swarm/Team for Safety, Security and Protection Against Unauthorized Drones
2024 (engelsk)Inngår i: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2024, s. 263-277Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

There is an increased need for protection against unauthorized entry of drones as there has been an increased number of reports of UAV’s entering restricted areas. In this paper we explore an approach of using a swarm/team of drones that are able to cooperate, to autonomously engage and disable one or more unauthorized drones entering a restricted area. In our approach, we have investigated technologies for distributed decision-making and task allocation in real-time, in a dynamic simulated environment and developed descriptive models for how such technologies may be exploited in a mission designed for a drone swarm. This includes the definition of discrete tasks, how they interact and how they are composed to form such a mission, as well as the realization and execution of these tasks using machine learning models combined with behaviour trees. To evaluate our approach, we use a simulated environment for mission execution where relevant KPI’s related to the design of the mission have been used to measure how efficient our approach is in deterring or incapacitating unauthorized drones. The evaluation has been performed using Monte-Carlo simulations on a batch of randomized scenarios and measures of effectiveness has been used to measure each scenario instance and later compiled into a final assessment for the main scenario as well as each ingoing task. The results show a mission success in 93% of the simulated scenarios. Of these 93%, 58% of the scenarios resulted in the threat being neutralized and in 35% of the scenarios the threat was driven away from the critical area. We believe that the application of such measurements aids to validate the applicability of this capability in a real-world scenario and in order to assert the relevance of these parameters, future validations in real-world operational scenarios are warranted.

sted, utgiver, år, opplag, sider
Springer Science and Business Media Deutschland GmbH, 2024
Emneord
Drone swarm, Drones, Protection, Safety, Security, Decision making, Intelligent systems, Monte Carlo methods, Petroleum reservoir evaluation, Decision task, Distributed decision making, Distributed-decision makings, Real- time, Security and protection, Simulated environment, Task allocation
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-65370 (URN)10.1007/978-3-031-39619-9_19 (DOI)2-s2.0-85181980978 (Scopus ID)9783031396182 (ISBN)
Konferanse
7th International Congress and Workshop on Industrial AI and eMaintenance, IAI 2023, Luleå, Sweden, 13 June 2023 through 15 June 2023
Tilgjengelig fra: 2024-01-17 Laget: 2024-01-17 Sist oppdatert: 2024-01-17bibliografisk kontrollert
Ahmed, M. U., Bengtsson, M., Salonen, A. & Funk, P. (2022). Analysis of Breakdown Reports Using Natural Language Processing and Machine Learning. In: Lecture Notes in Mechanical Engineering: . Paper presented at International Congress and Workshop on Industrial AI, IAI 2021 Virtual, Online 6 October 2021 through 7 October 2021 Code 272219 (pp. 40-52). Springer Science and Business Media Deutschland GmbH
Åpne denne publikasjonen i ny fane eller vindu >>Analysis of Breakdown Reports Using Natural Language Processing and Machine Learning
2022 (engelsk)Inngår i: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2022, s. 40-52Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Proactive maintenance management of world-class standard is close to impossible without the support of a computerized management system. In order to reduce failures, and failure recurrence, the key information to log are failure causes. However, Computerized Maintenance Management System (CMMS) seems to be scarcely used for analysis for improvement initiatives. One part of this is due to the fact that many CMMS utilizes free-text fields which may be difficult to analyze statistically. The aim of this study is to apply Natural Language Processing (NPL), Ontology and Machine Learning (ML) as a means to analyze free-textual information from a CMMS. Through the initial steps of the study, it was concluded though that none of these methods were able to find any suitable hidden patterns with high-performance accuracy that could be related to recurring failures and their root causes. The main reason behind that was that the free-textual information was too unstructured, in terms of for instance: spelling- and grammar mistakes and use of slang. That is the quality of the data are not suitable for the analysis. However, several improvement potentials in reporting and to develop the CMMS further could be provided to the company so that they in the future more easily will be able to analyze its maintenance data.

sted, utgiver, år, opplag, sider
Springer Science and Business Media Deutschland GmbH, 2022
Emneord
Computerized maintenance management system, Machine learning, Natural language processing, Recurring breakdowns, Root cause failure analysis
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-57576 (URN)10.1007/978-3-030-93639-6_4 (DOI)000777604600004 ()2-s2.0-85125258793 (Scopus ID)9783030936389 (ISBN)
Konferanse
International Congress and Workshop on Industrial AI, IAI 2021 Virtual, Online 6 October 2021 through 7 October 2021 Code 272219
Tilgjengelig fra: 2022-03-09 Laget: 2022-03-09 Sist oppdatert: 2022-06-07bibliografisk kontrollert
Rahman, H., D'Cruze, R. S., Ahmed, M. U., Sohlberg, R., Sakao, T. & Funk, P. (2022). Artificial Intelligence-Based Life Cycle Engineering in Industrial Production: A Systematic Literature Review. IEEE Access, 10, 133001-133015
Åpne denne publikasjonen i ny fane eller vindu >>Artificial Intelligence-Based Life Cycle Engineering in Industrial Production: A Systematic Literature Review
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2022 (engelsk)Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 10, s. 133001-133015Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

For the last few years, cases of applying artificial intelligence (AI) to engineering activities towards sustainability have been reported. Life Cycle Engineering (LCE) provides a potential to systematically reach higher and productivity levels, owing to its holistic perspective and consideration of economic and environmental targets. To address the current gap to more systematic deployment of AI with LCE (AI-LCE) we have performed a systematic literature review emphasizing the three aspects:(1) the most prevalent AI techniques, (2) the current AI-improved LCE subfields and (3) the subfields with highly enhanced by AI. A specific set of inclusion and exclusion criteria were used to identify and select academic papers from several fields, i.e. production, logistics, marketing and supply chain and after the selection process described in the paper we ended up with 42 scientific papers. The study and analysis show that there are many AI-LCE papers addressing Sustainable Development Goals mainly addressing: Industry, Innovation, and Infrastructure; Sustainable Cities and Communities; and Responsible Consumption and Production. Overall, the papers give a picture of diverse AI techniques used in LCE. Production design and Maintenance and Repair are the top explored LCE subfields whereas logistics and Procurement are the least explored subareas. Research in AI-LCE is concentrated in a few dominating countries and especially countries with a strong research funding and focus on Industry 4.0; Germany is standing out with numbers of publications. The in-depth analysis of selected and relevant scientific papers are helpful in getting a more correct picture of the area which enables a more systematic approach to AI-LCE in the future.

sted, utgiver, år, opplag, sider
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022
Emneord
Artificial intelligence, life cycle engineering, machine learning, sustainable development, sustainable development goal
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-62977 (URN)10.1109/ACCESS.2022.3230637 (DOI)000905683300001 ()2-s2.0-85146250639 (Scopus ID)
Tilgjengelig fra: 2023-06-08 Laget: 2023-06-08 Sist oppdatert: 2023-06-08bibliografisk kontrollert
Olsson, E., Candell, O., Funk, P. & Sohlberg, R. (2022). Assessment and Modelling of Joint Command and Control in Aircraft Maintenance Contexts Using Enterprise Models and Knowledge Graph Representations. International Journal of COMADEM, 25(2), 13-22
Åpne denne publikasjonen i ny fane eller vindu >>Assessment and Modelling of Joint Command and Control in Aircraft Maintenance Contexts Using Enterprise Models and Knowledge Graph Representations
2022 (engelsk)Inngår i: International Journal of COMADEM, ISSN 1363-7681, Vol. 25, nr 2, s. 13-22Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

The increasingly complex context of dynamic, high-tempo military air operations raise new needs for aircraft maintenance and logistic support systems to more rapidly respond to changes in operational needs and available resources, with retained support resource efficiency. The future maintenance and support system are thus envisioned with improved net-centric capabilities to facilitate matching of tactical needs with aircraft maintenance capabilities. The study addresses this challenge by creating abstract representations and definitions of relevant tactical structures and maintenance structures, processes, and resources using enterprise modelling. By addressing this in a holistic perspective, a better understanding of the matching problem is achieved, enabling efficient matching of operational needs with available resources. Based on these findings, graph models are created from a domain-centric view of two adjacent domain contexts, which includes command and control and aircraft maintenance contexts. The result has the ability to leverage interoperability and collaboration between air- and ground-based systems by facilitating interactions between tactical needs and aircraft maintenance resources.

sted, utgiver, år, opplag, sider
COMADEM International, 2022
Emneord
Aircraft Maintenance, Artificial Intelligence, Enterprise Modelling, System-of-Systems, Aircraft, Command and control systems, Maintenance, System of systems, Enterprise models, Graph representation, Joint command and controls, Knowledge graphs, Maintenance resources, Matchings, Operational needs, Tactical needs, Interoperability
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-64174 (URN)2-s2.0-85168775604 (Scopus ID)
Tilgjengelig fra: 2023-09-06 Laget: 2023-09-06 Sist oppdatert: 2023-09-06bibliografisk kontrollert
Olsson, E., Candell, O., Funk, P. & Sohlberg, R. (2022). Enterprise Modeling for Dynamic Matching of Tactical Needs and Aircraft Maintenance Capabilities. In: Lecture Notes in Mechanical Engineering: . Paper presented at International Congress and Workshop on Industrial AI, IAI 2021 Virtual, Online 6 October 2021 through 7 October 2021 Code 272219 (pp. 370-383). Springer Science and Business Media Deutschland GmbH
Åpne denne publikasjonen i ny fane eller vindu >>Enterprise Modeling for Dynamic Matching of Tactical Needs and Aircraft Maintenance Capabilities
2022 (engelsk)Inngår i: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2022, s. 370-383Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The increasingly complex context of dynamic, high-tempo military air operations raises new needs for airbase aircraft maintenance and logistic support systems to more rapidly respond to changes in operational needs, with retained support resource efficiency. The future maintenance and support system are thus envisioned with improved net-centric capabilities to facilitate matching of tactical needs with aircraft maintenance capabilities. Today military Command and Control (C2) of tactical needs against airbase aircraft maintenance capabilities contain many manual activities. This constrains speed of execution as well as drives manning requirements, and there is a need to further develop existing IS and IT support. Thus the studied matching capability address these limitations through an approach based on improved integration of the air vehicle on-board health management system with corresponding ground-based functions, and exploitation of technologies such as big-data analytics, diagnostic-prognostics, Artificial Intelligence (AI), machine learning and reasoning systems in a system-of-system-wide service architecture. However, understanding the concept of matching of tactical needs and aircraft maintenance capabilities requires insights of complex multi-domain C2 interactions and interrelations between the tactical domain and the aircraft maintenance domain. Whereas each domain is quite well understood, the more detailed interrelations between the domains is less studied. This paper present an approach to this problem by creating useful representations of the underpinning insights, by enterprise modeling of abstract representations and definitions of relevant tactical and maintenance structures, processes, and resources, in the airbase context, to better understand the matching problem, and to address this problem through a holistic perspective.

sted, utgiver, år, opplag, sider
Springer Science and Business Media Deutschland GmbH, 2022
Emneord
Aircraft maintenance, Artificial Intelligence, Enterprise modeling, System-of-systems
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-57577 (URN)10.1007/978-3-030-93639-6_32 (DOI)000777604600032 ()2-s2.0-85125240320 (Scopus ID)9783030936389 (ISBN)
Konferanse
International Congress and Workshop on Industrial AI, IAI 2021 Virtual, Online 6 October 2021 through 7 October 2021 Code 272219
Tilgjengelig fra: 2022-03-09 Laget: 2022-03-09 Sist oppdatert: 2022-06-07bibliografisk kontrollert
Olsson, E., Candell, O., Funk, P., Sohlberg, R., Castaño, M., Gustafsson, M. A. & Bladh, P. (2022). Graph-Based Knowledge Representation and Algorithms for Air and Maintenance Operations. In: 33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022: . Paper presented at 33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022, Stockholm, 4 September 2022 through 9 September 2022 (pp. 6925-6943). International Council of the Aeronautical Sciences
Åpne denne publikasjonen i ny fane eller vindu >>Graph-Based Knowledge Representation and Algorithms for Air and Maintenance Operations
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2022 (engelsk)Inngår i: 33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022, International Council of the Aeronautical Sciences , 2022, s. 6925-6943Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This work presents an approach for information exchange between adjacent air operations domains by means of graph technologies. The approach has the ability to leverage interoperability and collaboration between air- and ground-based systems and stakeholders in respective domains. In its foundation, it provides a means for relevant actors to access and assess relevant data, information and knowledge, and thus provide input in terms of viable action alternatives in a complex and dynamic operational context. As a proof-of-concept, we have utilized a full-stack application framework to implement a decision support demonstrator for operational aircraft maintenance. Our solution facilitates a lightweight and dynamic representation of relevant domain knowledge, readily available for exploitation by graph algorithms, adapted to our domain. We have based our implementation on the full-stack application framework Grand-Stack, which is an architecture designed to exploit the power of graphs throughout its stack. © 2022 ICAS. All Rights Reserved.

sted, utgiver, år, opplag, sider
International Council of the Aeronautical Sciences, 2022
Emneord
Aircraft Maintenance, Grand-Stack, Graph Algorithms, Graph Database, Interoperability
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-62706 (URN)2-s2.0-85159708436 (Scopus ID)9781713871163 (ISBN)
Konferanse
33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022, Stockholm, 4 September 2022 through 9 September 2022
Tilgjengelig fra: 2023-05-31 Laget: 2023-05-31 Sist oppdatert: 2023-05-31bibliografisk kontrollert
de Jesus, L. D., Filho, A. J., de Almeida, M. A., Martins, W. M., Pimenta, T. C., Ramos, A. C., . . . Funk, P. (2022). Use of multiple low cost autonomous drones for high voltage line inspection. In: 33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022: . Paper presented at 33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022, Stockholm, 4 September 2022, through 9 September 2022 (pp. 5591-5596). International Council of the Aeronautical Sciences
Åpne denne publikasjonen i ny fane eller vindu >>Use of multiple low cost autonomous drones for high voltage line inspection
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2022 (engelsk)Inngår i: 33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022, International Council of the Aeronautical Sciences , 2022, s. 5591-5596Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Many high-voltage line monitoring companies have been using drones to carry out preventive inspection. The inspection procedure usually makes use of pilots that control drones which films the transmission lines. Afterwards, the images need to be analyzed in order to detect problems. This work proposes the usage of drones that make autonomous flights to perform the inspection. The proposed approach exploits three drones to inspect each important part of a transmission line, with the first inspecting the wires, the second inspecting the towers and the third checking the safety area under the line. All three drones are controlled by an embedded system with artificial intelligence which is able to identify a potential problem and issue an alert to locations that might present issues. © 2022 ICAS. All Rights Reserved.

sted, utgiver, år, opplag, sider
International Council of the Aeronautical Sciences, 2022
Emneord
Artificial Intelligence, Drone, High Line Tension, Multiple Drones, Real-time Detection
HSV kategori
Identifikatorer
urn:nbn:se:mdh:diva-62707 (URN)2-s2.0-85159665128 (Scopus ID)9781713871163 (ISBN)
Konferanse
33rd Congress of the International Council of the Aeronautical Sciences, ICAS 2022, Stockholm, 4 September 2022, through 9 September 2022
Tilgjengelig fra: 2023-05-31 Laget: 2023-05-31 Sist oppdatert: 2023-05-31bibliografisk kontrollert
Organisasjoner
Identifikatorer
ORCID-id: ORCID iD iconorcid.org/0000-0002-5562-1424