Digital‐twin assisted: Fault diagnosis using deep transfer learning for machining tool condition. Issue 12 (27th May 2021)

• Record Type: Journal Article
• Authors: Deebak, B. D.; Al‐Turjman, Fadi
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 12(2022)
• Page Start: 10289
• Page End: 10316
• Publication Date: 2021-05-27
• Subjects: deep transfer learning -- digital twin -- fault diagnosis -- intelligent wireless monitoring -- machinery process; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22493
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: The rapid development forms a new transition of information technologies to offer an intelligent manufacturing. The manufacturer has revolutionized the stages of product lifecycle including process planning and maintenance for the early detection of potential system failures and proactive management. Technological advancements including big data, the cloud, and the Internet of Things have applied digital‐twin for industrial practice. It has low‐power wireless‐enabled devices to play a vital role in various industrial automation systems such as industry logistics, portable equipment, and intelligent wireless monitoring. It is evident that industrial manufacturers are nowadays aiming to transform the machine into fully automated systems that not only control the operation of the equipment but also try to meet the demand of future markets effectively. One of the challenging issues in the automation of the machinery process is the deployment of reliable systems to analyze the machinery condition such as fault diagnosis. Thus, this article proposes a digital‐twin‐assisted fault diagnosis using deep transfer learning to analyze the operational conditions of machining tools. Moreover, this proposed system has developed an intelligent tool‐holder that integrates a k‐type thermocouple and cloud data acquisition system over the WiFi module. The analytical study proves that this intelligent tool‐holder provides better accuracy to demonstrate the optimization of milling and drilling operations of cutting tools.