Towards Intelligent Maintenance Systems: Rescuing Human Operator and Context Factors

Botelho, Silvia Silva da Costa; Rettberg, Achim; Filho, Nelson Duarte; Hellingrath, Bernd; Amaral, Marcos; Espíndola, Danúbia; Pereira, Carlos Eduardo; Cordes, Ann-Kristin; Ventura, Renato; Frazzon, Enzo Morosini

doi:10.3182/20140824-6-za-1003.02367

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…Some studies suggest augmenting tacit knowledge with information derived from physical parameters to enhance the cognition of an abnormal condition (e.g. Griffiths 2009;Botelho et al 2014; Dudek and Patalas-Maliszewska 2016). However, these studies also point out that the intuitive cognition of an abnormal condition comprises a measure of personal interpretation and understanding relative to the operating environment.…”

Section: Skilled Intuitionmentioning

confidence: 99%

“…We define an abnormal machine behaviour as any circumstance when a machine deviate from normal behaviour; it can be but not limited to an abnormal response to controls, abnormal combination of alarms and/or alerts or an unusual emission of noise or heat. Intelligent systems offer a plethora of information; in the maintenance field, this includes equipment health status (thus replacing the use of human sensory systems), diagnosis through cause analysis and prognosis through mathematical and physical models (Botelho et al 2014). In contrast, humans acquire knowledge and understanding through thought, experience and the senses when they interact with machines and systems.…”

Section: Introductionmentioning

confidence: 99%

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Modelling human cognition of abnormal machine behaviour

Illankoon

Tretten

Kumar

2019

Hum.-Intell. Syst. Integr.

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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.

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Section: Skilled Intuitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modelling human cognition of abnormal machine behaviour

Illankoon

Tretten

Kumar

2019

Hum.-Intell. Syst. Integr.

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“…Contemporary MDS focus heavily on supporting physical manipulations, only a few studies focus on a potential reverse approach; how to capture and learn from human implicit involvements. For example, one study (Botelho et al 2014) proposes a maintenance strategy that combines and coordinates human physical abilities and computational elements. Another study (Vasenev et al 2013) uses virtual reality (VR) to follow machine operators' physical activities (in road construction) and extract their implicit knowledge.…”

Section: Mds Should Not Hinder Implicit Learning About Machine Behaviormentioning

confidence: 99%

Collaborating AI and human experts in the maintenance domain

Illankoon

Tretten

2020

AI & Soc

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Maintenance decision errors can result in very costly problems. The 4th industrial revolution has given new opportunities for the development of and use of intelligent decision support systems. With these technological advancements, key concerns focus on gaining a better understanding of the linkage between the technicians’ knowledge and the 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. The foundation of the new model builds upon 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 the identification of two main gaps: firstly, there must be a greater focus upon the technicians’ knowledge; secondly, technicians need assistance to maintain their focus on the big picture. We used the cognitive fit theory, and the theory of distributed situation awareness to propose the new theoretical model called “distributed collaborative awareness model.” The model considers both explicit and implicit knowledge and accommodates the dynamic challenges involved in operational level maintenance. As an application of this model, we identify and recommend some technological developments required in augmented reality based maintenance decision support.

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“…As a tangent to this Ruiz-Arenas et al (2014) explores many of the e-maintenance issues that pertain to CPS systems themselves and provide the case study of a CPS enabled greenhouse as an example. Penna et al (2014) and Botelho et al (2014) describe an approach for the visualisation of CPS integration in maintenance systems and the development of maintenance scenarios using 3D modelling tools. The aforementioned visualisation approach focusses on Human Computer Interaction issues taking into account and designing interfaces for the support of human operators within the maintenance process (Penna et al, 2014).…”

Section: E-maintenancementioning

confidence: 99%

“…Data description and visualisation Botelho et al (2014) Maintenance skill capture for use in intelligent maintenance systems for improved automated decision making with regard to assets degradation monitoring using virtual reality environment and simulation system.…”

Section: Mcfarlane Et Al (2013)mentioning

confidence: 99%

Intelligent decision support for maintenance: an overview and future trends

Turner

Emmanouilidis

Tomiyama

et al. 2019

International Journal of Computer Integrated Manufacturing

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The changing nature of manufacturing, in recent years, is evident in industry's willingness to adopt network connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision making systems. While e-maintenance practice provides a framework for internet connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of a comprehensive framework for its processing, analysis and use should be a valuable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data and the provision of comprehensive framework for its processing analysis and use, allowing future systems to enable 'Human in the loop' interactions.

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Towards Intelligent Maintenance Systems: Rescuing Human Operator and Context Factors

Cited by 8 publications

References 14 publications

Modelling human cognition of abnormal machine behaviour

Modelling human cognition of abnormal machine behaviour

Collaborating AI and human experts in the maintenance domain

Intelligent decision support for maintenance: an overview and future trends

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