Exploiting Augmented Reality to Enhance Piping and Instrumentation Diagrams for Information Retrieval Tasks in Industry 4.0 Maintenance

Gattullo, Michele; Evangelista, Alessandro; Uva, Antonio Emmanuele; Fiorentino, Michele; Boccaccio, Antonio; Manghisi, Vito Modesto

doi:10.1007/978-3-030-31908-3_11

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…We expect that this study contributes to the digitization of tons of paper-based P&IDs in the energy industry. In addition, this study can be employed in advanced maintenance technology using augmented reality, which recognizes objects from paper-based P&IDs [38].…”

Section: Discussionmentioning

confidence: 99%

Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network

Cha

Lee

et al. 2019

Energies

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A piping and instrumentation diagram (P&ID) is a key drawing widely used in the energy industry. In a digital P&ID, all included objects are classified and made amenable to computerized data management. However, despite being widespread, a large number of P&IDs in the image format still in use throughout the process (plant design, procurement, construction, and commissioning) are hampered by difficulties associated with contractual relationships and software systems. In this study, we propose a method that uses deep learning techniques to recognize and extract important information from the objects in the image-format P&IDs. We define the training data structure required for developing a deep learning model for the P&ID recognition. The proposed method consists of preprocessing and recognition stages. In the preprocessing stage, diagram alignment, outer border removal, and title box removal are performed. In the recognition stage, symbols, characters, lines, and tables are detected. The objects for recognition are symbols, characters, lines, and tables in P&ID drawings. A new deep learning model for symbol detection is defined using AlexNet. We also employ the connectionist text proposal network (CTPN) for character detection, and traditional image processing techniques for P&ID line and table detection. In the experiments where two test P&IDs were recognized according to the proposed method, recognition accuracies for symbol, characters, and lines were found to be 91.6%, 83.1%, and 90.6% on average, respectively.

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Section: Discussionmentioning

confidence: 99%

Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network

Cha

Lee

et al. 2019

Energies

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“…The use of AR to help operators in the correct understanding of a plant and to retrieve useful information about the plant (e.g., machines layout, history of maintenance) is given in [ 227 ]. The main task is to augment the Piping and Instrumentation Diagrams (P&ID) of a selected plant/group of plants.…”

Section: Systematic Literature Review Of the Selected Papers Within T...mentioning

confidence: 99%

“…The main task is to augment the Piping and Instrumentation Diagrams (P&ID) of a selected plant/group of plants. This problem is later extended in work [ 227 ], where the authors compare the AR application with the currently applied practice, based on paper documentation, for an information retrieval task within a maintenance procedure.…”

Section: Systematic Literature Review Of the Selected Papers Within T...mentioning

confidence: 99%

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Werbińska-Wojciechowska

Winiarska

2023

Sensors

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Recently, there has been a growing interest in issues related to maintenance performance management, which is confirmed by a significant number of publications and reports devoted to these problems. However, theoretical and application studies indicate a lack of research on the systematic literature reviews and surveys of studies that would focus on the evolution of Industry 4.0 technologies used in the maintenance area in a cross-sectional manner. Therefore, the paper reviews the existing literature to present an up-to-date and content-relevant analysis in this field. The proposed methodology includes bibliometric performance analysis and a review of the systematic literature. First, the general bibliometric analysis was conducted based on the literature in Scopus and Web of Science databases. Later, the systematic search was performed using the Primo multi-search tool following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The main inclusion criteria included the publication dates (studies published from 2012–2022), studies published in English, and studies found in the selected databases. In addition, the authors focused on research work within the scope of the Maintenance 4.0 study. Therefore, papers within the following research fields were selected: (a) augmented reality, (b) virtual reality, (c) system architecture, (d) data-driven decision, (e) Operator 4.0, and (f) cybersecurity. This resulted in the selection of the 214 most relevant papers in the investigated area. Finally, the selected articles in this review were categorized into five groups: (1) Data-driven decision-making in Maintenance 4.0, (2) Operator 4.0, (3) Virtual and Augmented reality in maintenance, 4) Maintenance system architecture, and 5) Cybersecurity in maintenance. The obtained results have led the authors to specify the main research problems and trends related to the analyzed area and to identify the main research gaps for future investigation from academic and engineering perspectives.

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“…International Journal of Industrial Engineering and Management Vol 11 No 1 (2020) the worker does not know the correct way of carrying out a task either because it is a new task or because he is not properly trained, or both [2]. Recent studies showed that in industrial sectors more than 80% of defects due to assembly and visual inspection tasks depend on human behaviour [3].…”

Section: Introduction Issn 2683-345xmentioning

confidence: 99%

“…physical and mental fatigue, work break schedule, cognitive workloads, etc. ); (2) the existing models allows to evaluate the HEP according to tasks characteristics, work log(LR) log (2) environment and workers capabilities, neglecting the effects due to 'time-dependent' aspects; in the Learning-Forgetting-Fatigue-Recovery these effects are considered, but independently from tasks characteristics, work environment, and workers capabilities;…”

Section: Introduction Issn 2683-345xmentioning

confidence: 99%

Model of Human Error Probability based on dual-phase approach for learning process in cognitive-oriented tasks

Facchini¹,

Digiesi²,

Mummolo³

2020

Int J Ind Eng Manag

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New accidents or incidents due to human error do not "just happen" and in most of the cases the cause relies not only on human incompetence or negligence [1]. In scientific literature the Human Error Probability (HEP) is defined, in case of discrete tasks, as the number of errors divided by the number of opportunities for making errors. The HEP is a key element in system accidents and disasters, especially in high-risk fields, such as industrial plants, nuclear plants, and aerospace. According to Health and Safety Executive agency (HSE), the errors can be classified in two categories. The first category includes the so-called "slips or lapses" errors: they are considered "actions that were not as planned" or unintended actions. These kinds of errors occur during the completion of a familiar task and include slips (e.g. pressing the wrong button or reading the wrong gauge) and lapses (e.g. forgetting to carry out a step in a known procedure). Therefore, they cannot be avoided by means of training, but an improved workplace design can reduce their likelihood and provide a more error tolerant system. The second category of errors includes the errors of judgement or decision-making, so-called "mistakes". They occur when the worker accomplishes the wrong action believing it to be right. In most cases, a mistake occurs in situations where Human Error Probability (HEP) is a key element in the chain of events that could lead up to system accidents and despite the efforts of recently studies to evaluate the human reliability, many of the limitations and problems have not yet been solved. The model proposed tries to overcome these limitations by means of a method that combines the advantages of a dual-phase learning approach with that of a multi-attribute utility analysis, in accordance with Dar-El' s theory. Results of numerical simulations show the effectiveness of the model in quantifying, over time, the HEP and in evaluating the human task error proneness by varying the work breaks schedule.

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Exploiting Augmented Reality to Enhance Piping and Instrumentation Diagrams for Information Retrieval Tasks in Industry 4.0 Maintenance

Cited by 12 publications

References 18 publications

Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network

Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network

Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review

Model of Human Error Probability based on dual-phase approach for learning process in cognitive-oriented tasks

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