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.

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.

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.

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.

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.

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.

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.