Sustaining implicit learning in locomotive operation [Elektronisk resurs]

• Modern trains are capable of monitoring health status in real time and infer behaviour ofvarious systems. This trend will grow with advancements of machine learning those willproduce feedback for continuously improving the prediction models. Despite reducedphysical connectivity of human with locomotive systems, human interference will berequired for critical decision-making. Human implicit learning involves the largelyunconscious learning of dynamic statistical patterns and features, which leads to thedevelopment of tacit knowledge1. Pirsig2 argued that “each machine has its own, uniquepersonality which probably could be defined as the intuitive sum total of everything youknow and feel about it”. Theses suggest that humans employ an intuitive cognition abilitythat leads to developing implicit knowledge and interactions with machines. In this study,we focus on signifying the implicit knowledge in locomotive operation context and seekways to facilitate effective decision-making. 

Ämnesord

Engineering and Technology  (hsv)

Mechanical Engineering  (hsv)

Production Engineering, Human Work Science and Ergonomics  (hsv)

Teknik och teknologier  (hsv)

Maskinteknik  (hsv)

Produktionsteknik, arbetsvetenskap och ergonomi  (hsv)

Genre

government publication  (marcgt)