Anomaly detection in Smart-manufacturing era: A review

Abstract

Manufacturing downtime due to faults is costly and disruptive. With the increasing availability of real-time data in modern Smart Manufacturing (SM) environments, effective anomaly detection (AD) has become crucial but challenging due to diverse scenarios and methods. This paper aims to present a comprehensive review of state-of-the-art AD methods tailored for SM, to facilitate their implementation in real manufacturing environments while providing a foundation for future research. First, it introduces a structured SM classification framework highlighting recent and successful AD algorithms applied in real-world scenarios with a valuable repository of over 100 manufacturing datasets to support further research. Second, an extensive experimental evaluation of 16 AD algorithms across 29 SM datasets covering a broad and diverse spectrum of techniques, including supervised, unsupervised, and semi-supervised approaches, encompassing classic, deep, and ensemble methods. Finally, insights gained from these experiments are presented providing practical guidance on the most suitable methods for various manufacturing contexts, identifying key challenges and opportunities for future developments.