Review on prognostics and health management in smart factory: From conventional to deep learning perspectives

Abstract

At present, the fourth industrial revolution is pushing factories toward an intelligent, interconnected grid of machinery, communication systems, and computational resources. Smart factories (SF) and smart manufacturing (SM) incorporate a cyber-physical system that employs advanced technologies such as artificial intelligence (AI) for data analysis, automated process driving, and continuous data handling. Smart factories operate by combining machines, humans, and massive amounts of data into a single, digitally interconnected ecosystem. Prognostics and health management (PHM) has become a critical requirement of smart factories to meet production needs. PHM of components/machines in the smart factory is crucial for securing uninterrupted operation and ensuring safety standards. The growing availability of computational capacity has increased the use of deep learning in PHM strategies. Deep learning supports comprehensive PHM solutions, thus reducing the need for manual feature development. This review presents an extensive study of the PHM strategies employed in the smart factory ranging from the conventional perspective to the deep learning perspective. This includes consideration of the conventional methodologies used for health management along with latest trends in the PHM domain in the smart factory.