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Soft Issues of Industry 4.0: A study on human-machine interactions
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0001-8693-3431
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Autonomous industrial operations are becoming the norm due to advancements in technology, which has led to both advantages and disadvantages for the organisations involved. The use of intelligent systems has resulted in higher system reliability, a higher quality product, and reduced risk for human error. These systems collect large amounts of information, analyse them, make predictions, and take decisions, of which humans cannot do in the same capacity, have led to new and expanded levels of interactions. One key aspect concerns the fact that human interaction has decreased although has become more critical than before. Even if the systems are advanced and automated, human intervention is still necessary: such as maintenance actions, selection of data to train the system, and advanced decision making. Human intervention is especially crucial when dealing with complex and safety critical systems, where and when immediate interventions are required. Moreover, an expert human can improvise and make novel decisions in a capacity that present intelligent systems cannot. The problem is that both humans and machines need assistance to perform well. Autonomous operation is not perfect and when problems arise, humans must react. Although it is common that humans when not actively interacting with the system tend to lose perspective and find it difficult to quickly analyse a situation when it arises. Which means that they “fall out of the loop”. Their ability to gain a good understanding of the situation and make good decisions when the system suddenly needs their interaction is lost. In other words, humans have lost their situation awareness (SA) and a good SA it is needed in dynamic environments if they are to intervene quickly and successfully. If, and when a system can assist a human to quickly assess the situation and get back “into the loop” then the human can make educated decisions in a much quicker fashion. The purpose of this research was to explore and describe the importance of SA in maintenance and to recommend how to develop and provide better SA for intelligent maintenance systems (IMS).

This thesis consists of a literature study conducted to develop the theoretical framework and two case studies were used to test the theoretical concepts. The thesis work tested five systematic methodologies to find suitable interventions to fulfil the SA requirements. The first case study focused on SA requirements during maintenance execution in a manufacturing organisation; there a quick return to production was the focus. The second case study was SA requirements in maintenance in the aviation domain, where safety is a top priority. The case study data were collected using interviews, observations, focus groups, and archival records. These qualitative data were analysed using qualitative content analysis, cognitive task analysis, and case taxonomic analysis.

This work resulted in the identification of seven key SA requirements for maintenance: consisting of detection of abnormalities; diagnosing and predicting their behaviour; making changes in system configuration; compliance with maintenance standards; conducting effective maintenance judgements; maintenance teams; and for safe maintenance work. Five strategies to maintain SA were identified: explicit knowledge status, sense making, recognition primed decision making, skilled intuition, and heuristics. We also argue why IMS will make it difficult for humans to use most of these strategies to maintain SA in future. Finally, a new theoretical model for decision support (Distributed Collaborative Awareness Model) was developed. The study also shows how to apply these interventions in the railway maintenance sector. In conclusion, this study shows that in the maintenance domain, keeping humans in the loop requires a novel collaborative approach where the integration of the strengths of intelligent systems and human cognition is necessary. We also argue that a better understanding of SA strategies will lead to the further development of SA support for the human operator and maintenance technician.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2020.
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-77561ISBN: 978-91-7790-528-8 (print)ISBN: 978-91-7790-529-5 (electronic)OAI: oai:DiVA.org:ltu-77561DiVA, id: diva2:1390007
Public defence
2020-03-26, F1031, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2020-01-31 Created: 2020-01-30 Last updated: 2020-03-11Bibliographically approved
List of papers
1. Ergonomics for enhancing detection of machine abnormalities
Open this publication in new window or tab >>Ergonomics for enhancing detection of machine abnormalities
2016 (English)In: Work: A journal of Prevention, Assesment and rehabilitation, ISSN 1051-9815, E-ISSN 1875-9270, Vol. 55, no 2, p. 271-280Article in journal (Refereed) Published
Abstract [en]

BACKGROUND:

Detecting abnormal machine conditions is of great importance in an autonomous maintenance environment. Ergonomic aspects can be invaluable when detection of machine abnormalities using human senses is examined.

OBJECTIVES:

This research outlines the ergonomic issues involved in detecting machine abnormalities and suggests how ergonomics would improve such detections.

METHODS:

Cognitive Task Analysis was performed in a plant in Sri Lanka where Total Productive Maintenance is being implemented to identify sensory types that would be used to detect machine abnormalities and relevant Ergonomic characteristics.

RESULTS AND CONCLUSIONS:

As the outcome of this research, a methodology comprising of an Ergonomic Gap Analysis Matrix for machine abnormality detection is presented.

National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-60020 (URN)10.3233/WOR-162416 (DOI)000386411800005 ()27689599 (PubMedID)2-s2.0-84992027309 (Scopus ID)
Note

Validerad; 2016; Nivå 2; 2016-10-28 (andbra)

Available from: 2016-10-28 Created: 2016-10-28 Last updated: 2020-01-30Bibliographically approved
2. Lockout and Tagout in a Manufacturing Setting from a Situation Awareness Perspective
Open this publication in new window or tab >>Lockout and Tagout in a Manufacturing Setting from a Situation Awareness Perspective
Show others...
2019 (English)In: Safety, ISSN 2313-576X, Vol. 5, no 2Article in journal (Refereed) Published
Abstract [en]

Applying lockouts during maintenance is intended to avoid accidental energy release, whereas tagging them out keeps employees aware of what is going on with the machine. In spite of regulations, serious accidents continue to occur due to lapses during lockout and tagout (LOTO) applications. Few studies have examined LOTO effectiveness from a user perspective. This article studies LOTO processes at a manufacturing organization from a situation awareness (SA) perspective. Technicians and machine operators were interviewed, a focus group discussion was conducted, and operators were observed. Qualitative content analysis revealed perceptual, comprehension and projection challenges associated with different phases of LOTO applications. The findings can help lockout/tagout device manufacturers and organizations that apply LOTO to achieve maximum protection.

Place, publisher, year, edition, pages
Basel: MDPI, 2019
Keywords
lockout, tagout, qualitative content analysis, situation awareness
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-73795 (URN)10.3390/safety5020025 (DOI)000474934600008 ()2-s2.0-85071440225 (Scopus ID)
Note

Validerad;2019;Nivå 2;2019-05-29 (oliekm)

Available from: 2019-04-30 Created: 2019-04-30 Last updated: 2020-01-30Bibliographically approved
3. A prospective study of maintenance deviations using HFACS-ME
Open this publication in new window or tab >>A prospective study of maintenance deviations using HFACS-ME
2019 (English)In: International Journal of Industrial Ergonomics, ISSN 0169-8141, E-ISSN 1872-8219, Vol. 74, article id 102852Article in journal (Refereed) Published
Abstract [en]

The factors initiating aviation accidents are usually hidden behind various steps, systems, and tasks, and systematic root-cause analysis is required to uncover the initial factor(s). To reduce the risk of unfavourable events, it is more appropriate to study their causal factors. We argue that an in-depth study on maintenance process deviations could assist in uncovering hidden causal factors. We therefore analyse reported maintenance deviations from an aviation organisation using the Human Factor Analysis and Classification System-Maintenance Extension (HFACS-ME) taxonomy to aggregate and map hidden causal factors. We find attention and memory errors and inadequacy of processes and documentation are major causal factors. We argue a well-run organisation can capture hidden causal factors and reduce the risk of incidents and accidents. More specifically, we show how situation awareness (SA) interventions can assist in the mitigation of maintenance deviations and capture hidden causal factors.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Aviation maintenance, Incidents, Active error, Latent condition, Situation awareness
National Category
Production Engineering, Human Work Science and Ergonomics Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-76338 (URN)10.1016/j.ergon.2019.102852 (DOI)000503086000016 ()
Funder
Luleå Railway Research Centre (JVTC), 167522
Note

Validerad;2019;Nivå 2;2019-10-15 (johcin)

Available from: 2019-10-10 Created: 2019-10-10 Last updated: 2020-02-25Bibliographically approved
4. Judgemental errors in aviation maintenance
Open this publication in new window or tab >>Judgemental errors in aviation maintenance
2019 (English)In: Cognition, Technology & Work, ISSN 1435-5558, E-ISSN 1435-5566Article in journal (Refereed) Epub ahead of print
Abstract [en]

Aircraft maintenance is a critical success factor in the aviation sector, and incorrect maintenance actions themselves can be the cause of accidents. Judgemental errors are the top causal factors of maintenance-related aviation accidents. This study asks why judgemental errors occur in maintenance. Referring to six aviation accidents, we show how various biases contributed to those accidents. We first filtered aviation accident reports, looking for accidents linked to errors in maintenance judgements. We analysed the investigation reports, as well as the relevant interview transcriptions. Then we set the characteristics of the actions behind the accidents within the context of the literature and the taxonomy of reasons for judgemental biases. Our results demonstrate how various biases, such as theory-induced blindness, optimistic bias, and substitution bias misled maintenance technicians and eventually become the main cause of a catastrophe. We also find these biases are interrelated, with one causing another to develop. We discuss how these judgemental errors could relate to loss of situation awareness, and suggest interventions to mitigate them.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Judgemental error, Heuristics, Aviation maintenance, Situation awareness
National Category
Production Engineering, Human Work Science and Ergonomics Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-76544 (URN)10.1007/s10111-019-00609-9 (DOI)000492918800001 ()2-s2.0-85074656365 (Scopus ID)
Funder
Luleå Railway Research Centre (JVTC)
Available from: 2019-10-28 Created: 2019-10-28 Last updated: 2020-01-30
5. Modelling human cognition of abnormal machine behaviour
Open this publication in new window or tab >>Modelling human cognition of abnormal machine behaviour
2019 (English)In: Human-Intelligent Systems Integration, ISSN 2524-4876, Vol. 1, no 1, p. 3-26Article in journal (Refereed) Published
Abstract [en]

Despite the advances in intelligent systems, there is no guarantee that those systems will always behave normally. Machine abnormalities, unusual responses to controls or false alarms, are still common; therefore, a better understanding of how humans learn and respond to abnormal machine behaviour is essential. Human cognition has been researched in many domains. Numerous theories such as utility theory, three-level situation awareness and theory of dual cognition suggest how human cognition behaves. These theories present the varieties of human cognition including deliberate and naturalistic thinking. However, studies have not taken into consideration varieties of human cognition employed when responding to abnormal machine behaviour. This study reviews theories of cognition, along with empirical work on the significance of human cognition, including several case studies. The different propositions of human cognition concerning abnormal machine behaviour are compared to dual cognition theories. Our results show that situation awareness is a suitable framework to model human cognition of abnormal machine behaviour. We also propose a continuum which represents varieties of cognition, lying between explicit and implicit cognition. Finally, we suggest a theoretical approach to learn how the human cognition functions when responding to abnormal machine behaviour during a specific event. In conclusion, we posit that the model has implications for emerging waves of human-intelligent system collaboration.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Cognition, Machine abnormities, Situation awareness, Explicit, Implicit, Cognitive continuum
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-73796 (URN)10.1007/s42454-019-00002-x (DOI)
Funder
Luleå Railway Research Centre (JVTC)
Available from: 2019-04-30 Created: 2019-04-30 Last updated: 2020-01-30Bibliographically approved
6. Identifying significance of human cognition in future maintenance operations
Open this publication in new window or tab >>Identifying significance of human cognition in future maintenance operations
2018 (English)In: Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365, Vol. 722, p. 550-556Article in journal (Refereed) Published
Abstract [en]

Industrial maintenance in future will operate heavily with intelligent systems. Advanced sensor networks on machines will enable them communicate and learn about failure types, predict consequences and share solutions. Humans on the other hand are equipped with intuitive cognition that facilitates acquisition of knowledge about unique characteristics of individual machines, and use this knowledge in maintenance problem solving. In this article, we identify two major opportunities to collaborate human intuitive cognition with intelligent systems for future maintenance solutions.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Industry 4.0, Maintenance, Intelligent systems, Intuitive cognition
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-67026 (URN)10.1007/978-3-319-73888-8_86 (DOI)2-s2.0-85040218554 (Scopus ID)978-3-319-73887-1 (ISBN)
Conference
1st International Conference on Intelligent Human Systems Integration: Integrating People and Intelligent Systems, IHSI 2018; Dubai; United Arab Emirates; 7 - 9 January 2018
Note

Konferensartikel i tidskrift

Available from: 2017-12-15 Created: 2017-12-15 Last updated: 2020-01-30Bibliographically approved
7. Distributed, Collaborative Awareness Model for Maintenance Decision Support.
Open this publication in new window or tab >>Distributed, Collaborative Awareness Model for Maintenance Decision Support.
2020 (English)Article in journal (Refereed) Submitted
Abstract [en]

Maintenance decision errors can result in very costly problems. With the rise of 4th industrial revolution, Intelligent Decision Support Systems are growing quickly. However, a key concern has been to better understand the linkage between the technicians’ knowledge and Intelligent Decision Support Systems. The research reported in this study has two primary objectives. (1) to propose a theoretical model that links technicians’ knowledge and intelligent decision support systems, and (2) to present a use case how to apply the theoretical model. As the foundation of the new model, is the assentation of two main streams of study in the decision support literature: “distribution” of knowledge among different agents, and “collaboration” of knowledge for reaching a shared goal. This study resulted in identification of two main gaps: first, there is no enough recognition of the technicians’ knowledge; second, there is little assistance for technician by providing the bigger picture. We used cognitive fit theory, and the theory of distributed situation awareness to build the new theoretical model we call the “Distributed Collaborative Awareness Model.” The model considers both explicit and implicit knowledge, and accommodates the dynamic challenges involved in operational level maintenance. As a means of applying this model, we identify and recommend some technological developments required in Augmented Reality based maintenance decision support.

Keywords
Industry 4.0, Maintenance, Decision support, Situation awareness, Collaboration, Augmented Reality
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Operation and Maintenance
Identifiers
urn:nbn:se:ltu:diva-77558 (URN)
Available from: 2020-01-30 Created: 2020-01-30 Last updated: 2020-03-06

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