SLAE-CPS: Smart Lean Automation Engine Enabled by Cyber-Physical Systems Technologies

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 7; p. 1500
• Main Authors: Ma, Jing, Wang, Qiang, Zhao, Zhibiao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 28.06.2017; MDPI
• Subjects: Automation; Configuration management; Cyber-physical systems; Design standards; Economic conditions; Engine blocks; Flexibility; Industrial applications; Mass production; Operators (mathematics); Service oriented architecture; System effectiveness; cyber-physical systems; Internet of things; Industry 4.0; lean production; Jidoka; lean automation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17071500

In the context of Industry 4.0, the demand for the mass production of highly customized products will lead to complex products and an increasing demand for production system flexibility. Simply implementing lean production-based human-centered production or high automation to improve system flexibility is insufficient. Currently, lean automation (Jidoka) that utilizes cyber-physical systems (CPS) is considered a cost-efficient and effective approach for improving system flexibility under shrinking global economic conditions. Therefore, a smart lean automation engine enabled by CPS technologies (SLAE-CPS), which is based on an analysis of Jidoka functions and the smart capacity of CPS technologies, is proposed in this study to provide an integrated and standardized approach to design and implement a CPS-based smart Jidoka system. A set of comprehensive architecture and standardized key technologies should be presented to achieve the above-mentioned goal. Therefore, a distributed architecture that joins service-oriented architecture, agent, function block (FB), cloud, and Internet of things is proposed to support the flexible configuration, deployment, and performance of SLAE-CPS. Then, several standardized key techniques are proposed under this architecture. The first one is for converting heterogeneous physical data into uniform services for subsequent abnormality analysis and detection. The second one is a set of Jidoka scene rules, which is abstracted based on the analysis of the operator, machine, material, quality, and other factors in different time dimensions. These Jidoka rules can support executive FBs in performing different Jidoka functions. Finally, supported by the integrated and standardized approach of our proposed engine, a case study is conducted to verify the current research results. The proposed SLAE-CPS can serve as an important reference value for combining the benefits of innovative technology and proper methodology.