Automatic detection and severity analysis of grape black measles disease based on deep learning and fuzzy logic

Tacho-less sparse CNN to detect defects in rotor-bearing systems at varying speed

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared. Code and preprocessed data for generating all the results and figures presented in the battery digital twin case study in part 2 of this review are available on Github . 

A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies

http://manuscript.elsevier.com/S0306454922000378/pdf/S0306454922000378.pdf

A digital twin approach to system-level fault detection and diagnosis for improved equipment health monitoring

/pdf/temperature-field-inversion-of-heat-source-systems-via-1hcu6tqz.pdf

Jeff Sidon

Papers

A physics-informed deep learning approach for bearing fault detection