SemKoRe: Improving Machine Maintenance in Industrial IoT with Semantic Knowledge Graphs

Abstract

The recent focus on sustainability and improved efficiency requires innovative approaches in industrial automation. We present SemKoRe, a knowledge graph developed to improve machine maintenance in the industrial domain. SemKoRe is vendor-agnostic, it helps Original Equipment Manufacturers (OEMs) to capture, share and exploit the failure knowledge generated by their customers machines located around the world. Based on our interactions with actual customers, it usually takes several hours to days to fix a machine-related issue. During this time, production stops and incurs cost in terms of lost production. SemKoRe significantly enhances the maintenance process by reducing the failure diagnostic time, and by centralizing machine maintenance knowledge fed by the experts and technicians around the world. We developed flexible architecture to cover our customers’ varying needs, along with failure and machine domain ontologies. To demonstrate the feasibility of SemKoRe, a proof-of-concept is developed. SemKoRe gathers all failure related data in the knowledge graph, and shares it among all connected customers in order to easily solve future failures of the same type. SemKoRe received the approval of several substantial clients located in USA, UK, France, Germany, Italy and China, associated with various segments such as pharmaceutical, automotive, HVAC and food and beverage.

Keywords

• failure diagnostics
• industry 4.0
• industrial internet of things (IIoT)
• knowledge graph
• machine maintenance
• semantic web