A Cognitive Approach based on the Actionable Knowledge Graph for supporting Maintenance Operations

Abstract

In the era of Industry 4.0, cognitive computing and its enabling technologies (Artificial Intelligence, Machine Learning, etc.) allow to define systems able to support maintenance by providing relevant information, at the right time, retrieved from structured companies' databases, and unstructured documents, like technical manuals, intervention reports, and so on. Moreover, contextual information plays a crucial role in tailoring the support both during the planning and the execution of interventions. Contextual information can be detected with the help of sensors, wearable devices, indoor and outdoor positioning systems, and object recognition capabilities (using fixed or wearable cameras), all of which can collect historical data for further analysis. In this work, we propose a cognitive system that learns from past interventions to generate contextual recommendations for improving maintenance practices in terms of time, budget, and scope. The system uses formal conceptual models, incremental learning, and ranking algorithms to accomplish these objectives.

Design Objectives for Evolvable Knowledge Graphs

“…Maintenance requires time and resources in the manufacturing industry, and AI is increasingly used to optimize activities. For instance, AI systems offer suggestions to maintenance personnel based on past cases [10] or context-optimized assistance [11], and original equipment manufacturers (OEMs) rely on failure diagnostics to enhance their products and services [12]. KGs are often at the core of such systems.…”