Towards Intelligent Maintenance Systems: Rescuing Human Operator and Context Factors

Abstract

Current technologies have used embedded devices for degradation monitoring based on Condition-Based Maintenance (CBM) paradigm for critical parts of equipments. Intelligent Maintenance Systems (IMS) have been proposed in order to autonomously provide failure predictions through data acquisition from sensors. This paper intends to discuss how to capture the operator skill and environment conditions into the IMS. We have adapted the data flow in CBM classical architecture in order to consider the resultant information from maintenance operations associated with the technical teams and their contexts. For this purpose was used a Cyber-Physical Systems (CPS) approach to consider human and context factors. Considering the CPS as system that combines and coordinates physical and computational elements, the CPS incorporates the ability to act in the physical world with the intelligence from cyber or virtual world. In this paper, we propose to use a CPS for taking account human/context factors applied maintenance strategies. The proposed tool, called Toogle-IMS, aims at developing an advanced Human-Computer Interaction (HCI) applied to Intelligent Maintenance System (IMS). We have applied the Toogle-IMS in two case studies associated with: i. mobile robots in maintenance and ii. mobile trucks in shipyards.