Resource-efficient Edge AI solution for predictive maintenance

Abstract

Data-driven predictive maintenance (PM) is an approach that leverages advanced analytics, artificial intelligence (AI), and sensor data to predict when equipment failure might occur, and to perform maintenance just in time to prevent it, reducing downtime and maintenance costs. In manufacturing, one of the biggest potential applications for intelligent PM systems is tool condition monitoring (TCM). TCM aims to monitor tool wear in real-time, ensuring the quality of the manufactured products and the safety of the surrounding people and equipment. In recent decades, many studies have been carried out on tool condition monitoring for different machining operations such as milling, drilling or turning, and have demonstrated the effectiveness of various AI algorithms. Recent advances in hardware have made it possible for edge devices to run complex AI algorithms locally. This technology is called Edge AI. The Edge AI approach has several key benefits such as reduced latency, scalability, data privacy and security that can accelerate the integration of the PM solution for TCM at the production level. This paper presents the design of an Edge AI system for tool condition monitoring, consisting of state-of-the-art, low-cost components and using open-source software. Based on the proposed design, a prototype was built and tested during milling process. Four machine learning (ML) models and one deep learning (DL) model were run on a low performance edge device. Their predictions were validated. Challenges faced in the implementation are concluded along with directions and suggestions for future research.