Learning with supervised data for anomaly detection in smart manufacturing

Abstract

ABSTRACT The emergence of the Internet of Things (IoT), cloud computing, cyber-physical systems, system integration, big data, and data analytics for Industry 4.0 have transformed the world of traditional manufacturing into an era of smart manufacturing (SM). Smart manufacturing’s central focus is to process real-time IoT data and leverage advanced analytical approaches to detect abnormal behaviors. Social smart manufacturing applies analytics tools to empower decision makers and minimize duplication by executing the repetitive data processing work more consistently and precisely than can be done by a human operator. In smart manufacturing, the majority of industrial data is imbalanced. However, most traditional machine learning algorithms tend to be biased toward the majority class and under-represent the minority class. This research proposes a model selection architecture to automate the procedure of preprocessing input data and selecting the best combination of algorithms for anomaly detection. This design will play an essential role in producing high-quality products and improving quality control and business processes in diverse applications including predictive maintenance and fault detection. The framework is transferrable to any smart manufacturing task in the supervised learning domain.