Over the past century, the manufacturing industry has undergone a number of paradigm shifts, from the Ford assembly line (1900s) to Toyota production system (1960s), flexible manufacturing (1980s), reconfigurable manufacturing (1990s), agent-based manufacturing (2000s), cloud manufacturing (2010s)[1,2]. Various countries have developed strategic roadmaps to transform manufacturing to take advantage of the emerging infrastructure, as presented by Internet of Things (IoTs) and data science. As an example, Germany introduced the framework of Industry 4.0 in 2010, which has been evolved into a collaborative effort among member countries in the European Union. Similarly, in 2011 the Smart Manufacturing Leadership Coalition (SMLC) in the U.S. created a systematic framework for implementing smart manufacturing. The plan “China Manufacturing 2025”, introduced in China in 2015, aims to promote advanced manufacturing. As manufacturing machines are increasingly equipped with sensors and communicationcapabilities,there is significantpotentialto further improve the condition-awareness of manufacturing machines and processes, reduce operational downtime, improve the level of automation and product quality and response more timely to dynamically changing customer demands [3–۸]. Statistics shows that 82% of the companies using smart manufacturing technologies have experienced increased efficiency and 45% of the companies of the companies experienced increased customer satisfaction [9]. Smart manufacturing refers to a new manufacturing paradigm where manufacturing machines are fully connected through wireless networks, monitored by sensors, and controlled by advanced computational intelligence to improve product quality, system productivity, and sustainability while reducing costs. Recent advancement of Internet of Things (IoTs), Cloud Computing, Cyber Physical System (CPS) provides key supporting technologies to advance modern manufacturing [10–۱۳]. By leveraging these new technologies in manufacturing, data at different stages of a product’s life, ranging from raw materials, machines’ operations, facility logistics, and even human operators, is collected and processed [12]. With the proliferation of manufacturing data, data driven intelligence with advanced analytics transforms unprecedented volumes ofdata into actionable andinsightfulinformationfor smart manufacturing as illustrated in Fig. 1. Data driven intelligence models the complex multivariate nonlinear relationships among data, with no in-depth understanding of system physical behaviours required.