Improving maintenance efficiency and safety through a human-centric approach

Abstract

This paper presents an adaptive human-machine interface (HMI) that can provide appropriate sets of digital maintenance information and guidance to an operator during maintenance. It takes into consideration the expertise level of the operator and the maintenance context and progress. The proposed human-centric methodology considers the heart rate, intention, and expertise level of the operator, which can be captured using sensors during maintenance. A set of rules is formulated based on the sensor data to infer the state of the operator during a maintenance task. Based on the operator state, the adaptive HMI can augment the operator’s senses using a scheme that combines visual, audio, and haptic guidance cues during maintenance to enhance the operator’s ability to perceive information and perform maintenance tasks. Various schemes of visual, audio, and haptic cues are developed based on a comparison of the best practices obtained from experienced operators.