| 1. |
- Begum, Shahina, 1977-, et al.
(författare)
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Artificial Intelligence in Predictive Maintenance : A Systematic Literature Review on Review Papers
- 2024
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Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 251-261
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Konferensbidrag (refereegranskat)abstract
- The fourth industrial revolution, colloquially referred to as “industry 4.0”, has garnered substantial global attention in recent years. There, Artificial intelligence (AI) driven industrial intelligence has been increasingly deployed in predictive maintenance (PdM), emerging as a vital enabler of smart manufacturing and industry 4.0. Since in recent years the number of articles focusing on Artificial Intelligence (AI) in PdM is high a review on the available literature reviews in this domain would be useful for the future researchers who would like to advance the research in this area and also for the persons who would like to apply PdM in their application domains. Therefore, this study identifies the AI revolution in PdM and focuses on the next stages available in the literature reviews in this area by quality assessment of secondary study. A well-known structured review approach (Systematic Literature Review, or SLR) was employed to perform this tertiary study. In addition, the Scale for the Assessment of Narrative Review Articles (SANRA) approach for evaluating the quality of review papers has been employed to support a few of the research questions. Here, This tertiary study scrutinizes four crucial aspects of secondary articles: (1) their specific research domains, (2) the annual trends in the quantity, variety, and quality (3) a footsteps of top researchers, and (4) the research constraints that review articles face during the time frame of 2015 to 2022. The results show that the majority of the application areas are applied to the manufacturing industry. It also leads to the identification of the revolution of AI in PdM as well. Our final findings indicate that Dr. Cheng et al.’s (2022) review has emerged as the predominant source of information in this field. As newcomers or industrial practitioners, we can benefit greatly from following his insights. The final outcome is that there is a lack of progress in SLR formulation and in adding explainable or interpretive AI methodologies in secondary studies.
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| 2. |
- Bengtsson, Marcus, 1977-, et al.
(författare)
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The Importance of Using Domain Knowledge When Designing and Implementing Data-Driven Decision Models for Maintenance : Insights from Industrial Cases
- 2024
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Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 601-614
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Konferensbidrag (refereegranskat)abstract
- The advanced technologies available in the development of Smart Maintenance within Industry 4.0 have the potential to significantly improve the efficiency of industrial maintenance. However, it is important to be careful when deciding which technologies to implement for a given application and when evaluating the quality of the data generated. Otherwise, what should be cost-effective solutions may end up being cost-driving. The use of domain knowledge in selecting, developing, implementing, setting up, and utilizing these technologies is increasingly important for achieving success. In this paper, we will elaborate on this topic by presenting and analyzing insights from industrial cases, drawing on the authors’ extensive experience in the field.
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| 3. |
- D'Cruze, Ricky Stanley, et al.
(författare)
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A Case Study on Ontology Development for AI Based Decision Systems in Industry
- 2024
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Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 693-706
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 4. |
- Giacomossi, L., et al.
(författare)
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Cooperative Search and Rescue with Drone Swarm
- 2024
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Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 381-393
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 5. |
- Giliyana, San, et al.
(författare)
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A Testbed for Smart Maintenance Technologies
- 2024
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Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 437-450
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Konferensbidrag (refereegranskat)abstract
- Industry 4.0 presents nine technologies including Industrial Internet of Things (IIoT), Big Data and Analytics, Augmented Reality (AR), etc. Some of the technologies play an important role in the development of smart maintenance technologies. Previous research presents several technologies for smart maintenance. However, one problem is that the manufacturing industry still finds it challenging to implement smart maintenance technologies in a value-adding way. Open questionnaires and interviews have been used to collect information about the current needs of the manufacturing industry. Both the empirical findings of this paper, as well as previous research, show that knowledge is the most common challenge when implementing new technologies. Therefore, in this paper, we develop and present a testbed for how to approach smart maintenance technologies and to share technical knowledge to the manufacturing industry.
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| 6. |
- Kumar, Uday, et al.
(författare)
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Editorial
- 2024
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Ingår i: International Congress and Workshop on Industrial AI and eMaintenance 2023. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 - 9783031396199 ; , s. v-vi
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 7. |
- Mehra, Priyanka, et al.
(författare)
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HFedRF : Horizontal Federated Random Forest
- 2024
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Ingår i: International Congress and Workshop on Industrial AI and eMaintenance 2023. IAI 2023. Lecture Notes in Mechanical Engineering.. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 - 9783031396199 ; , s. 409-422
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Konferensbidrag (refereegranskat)abstract
- Real-world data is typically dispersed among numerous businesses or governmental agencies, making it difficult to integrate them into data privacy laws like the General Data Protection Regulation of the European Union (GDPR). Two significant obstacles to the use of machine learning models in applications are the existence of such data islands and privacy issues. In this paper, we address these issues and propose ‘HFedRF: Horizontal Federated Random Forest’, a privacy-preserving federated model which is approximately lossless. Our proposed algorithm merges d random forests computed on d different devices and returns a global random forest which is used for prediction on local devices. In our methodology, we compare IIDs (Independent and Identically Distributed) and non-IIDs variant of our algorithm HFedRF with traditional machine learning (ML) methods i.e., decision tree and random forest. Our results show that we achieve benchmark comparable results with our algorithm for IID as well as non-IID settings of federated learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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| 8. |
- Olsson, E., et al.
(författare)
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Using a Drone Swarm/Team for Safety, Security and Protection Against Unauthorized Drones
- 2024
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Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 263-277
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 9. |
- Salonen, Antti
(författare)
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On the Need for Human Centric Maintenance Technologies
- 2024
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Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 465-475
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Konferensbidrag (refereegranskat)abstract
- The digitalization of manufacturing industry, known as e.g., Industry 4.0 or smart production, has opened new opportunities for real-time optimization of production systems. Also, this technological leap has provided new possibilities for the maintenance of production equipment to become data driven and in many cases predictive. This fourth industrial revolution is changing the role of humans at the shop floor. Visions of the dark factory arises, meaning fully automated factories where humans are redundant, both for physical processing and for decision making. The research on Smart maintenance shows great advances in predictive diagnostics and prognostic techniques. However, in manufacturing industry, studies have shown that up to 50–60% of equipment breakdowns are due to human errors. Some of these errors are partly addressed through the development of improved information aid, such as e.g., instructions through Augmented Reality and training in Virtual Reality. Still, the root cause of human errors in manufacturing industry haven’t been properly categorized in terms of e.g., neglect, lack of competence, unclear processes, or poor leadership. In this paper the potential of data driven maintenance is discussed from a human centric perspective. Considering the large part of failures being due to human factors and the possibilities of improvement through implementation of smart technologies, this paper argues for exploring the root causes of human errors in discrete item manufacturing systems and address the proper human centric technologies as a means of reducing these failures.
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| 10. |
- Sundelius, Nils, et al.
(författare)
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Simulation Environment Evaluating AI Algorithms for Search Missions Using Drone Swarms
- 2024
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Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Science and Business Media Deutschland GmbH. - 9783031396182 ; , s. 191-204
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Konferensbidrag (refereegranskat)abstract
- 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.
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