A novel local temperature change detection approach in a 3D thermal field

ABSTRACT

Local thermal field monitoring is of great importance in industrial engineering practice. In view of the complex thermodynamics of a 3D thermal field, the local temperature change beneath the global thermal field is relatively challenging to detect using existing approaches. To fill this gap, we propose a thermal field monitoring method that decomposes the dynamic thermal field into global trend and local variability parts. Specifically, a universal kriging model that characterises the spatial correlation at each time epoch is developed to model the local variability using sensing data. To efficiently monitor the thermal field changes, a multivariate cumulative sum chart that uses the estimated parameters is designed. An actual case study of the thermal field of a granary is used to validate the performance of the proposed method.

KEYWORDS:

• Field monitoring
• Gaussian-kriging model
• MCUSUM

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by NSFC Grant [number 71571003, 71771004 and 71690232]; MOF National Nonprofit Special Research Grant [number 201413003].

Notes on contributors

Yu An

Yu An received the B.S. degrees in theoretical and applied mechanics and international politics (international political economy) from Peking University, Beijing, China, in 2019. Currently she is a Ph.D. candidate at the Department of Industrial Engineering and Management, Peking University, Beijing, China. Her research interests focus on statistically modeling of spatiotemporal data and complex systems.

Di Wang

Di Wang received the B.S. degree in industrial engineering from Nankai University, Tianjin, China, in 2015. Currently she is a Ph.D. candidate at the Department of Industrial Engineering and Management, Peking University, Beijing, China. Her research interests focus on statistically modeling of spatiotemporal data and complex systems.

Xi Zhang

Xi Zhang received the B.S. degrees in mechanical engineering and business administration from Shanghai Jiaotong University, Shanghai, China, in 2006, and the Ph.D. degree in industrial and management systems engineering from the University of South Florida, Tampa, FL, USA, in 2010. Currently, he is an Associate Professor with the Department of Industrial Engineering and Management, Peking University, Beijing, China. His research interests focus on engineering data governance and analytics for process monitoring, diagnosis, control and optimization in complex engineering and service systems. Dr. Zhang is a member of ASQ, INFORMS, IEEE, and IISE.