Virtual Reality Based Maintenance Training Effectiveness Measures – a Novel Approach for Rail Industry

Autor: S A Ranjha, Guoxing Lu, Nalin Randeniya, Ambarish Kulkarni
Rok vydání: 2019
Předmět:
Zdroj: ISIE
DOI: 10.1109/isie.2019.8781351
Popis: This paper investigates engineering education and technical training optimization techniques based on augmented and virtual reality (AVR) technologies. Different learning methods are discussed in detail, with respect to identified enhanced learning outcomes such as higher engagement level, knowledge retention, and applied practical competency. Integration of AVR technology has a proven potential to elevate engagement and memory retention capacity according to the distinguished literature discussed in this paper. The primary objective of this research is establishing an evaluation framework that effectively measure the learning experience. Two experimental groups of novice trainee students were exposed to a traditional, class room based training program and virtual reality (VR) based training program. Both programs deliver a maintenance training activity of a chosen mechanism (sub-system), which will be used as preliminary results. An evaluation procedure was developed to assess each trainee to gather important data such as time consumption, accuracy, aptitude, performance level and intuition. A mathematical model was formulated to assess and index the individuals from the collected data, to establish their individual cognitive and psychomotor skill index (CPSI). This numerical rating allows assessment of workers over various tasks, and it can be used as an indicator of the competencies of trainees. Through integrated VR training, individuals were shown to have higher engagement during the training process, as their understanding of the scenario improved. This allowed them to work faster, more efficiently and more accurately with a shorter overall training time.
Databáze: OpenAIRE