Reliability modeling: combining self-healing characteristics and dynamic failure thresholds

ABSTRACT

The failure threshold of a product is not always constant when affected by random shocks. Thus, this paper establishes a reliability model that combines dynamic failure threshold and self-healing characteristics, which are subjected to the competing failure process. Soft failure is caused by degradation exceeding the soft failure threshold, and hard failure occurs because of random shocks. Degradation consists of natural degradation and a degradation increment caused by shocks. The failure thresholds and degradation rate will also be affected by random shocks. Moreover, the self-healing characteristics in the degradation process, together with the degradation increment, will affect the whole degradation process. When the random shock arrives, the soft and hard failure thresholds will change simultaneously. Based on the above factors, the corresponding analytical expressions of reliability models under different shock models are derived. To confirm the effectiveness of the proposed model, an example of micro-engine is used to verify the constructed reliability model. The influences of self-healing and dynamic thresholds in micro-engines on reliability are studied. The effects of self-healing and thresholds on the models are analyzed, which can provide study basis for the maintenance and replacement of product.

KEYWORDS:

• Competing failure processes
• reliability modeling
• failure threshold
• self-healing

Acknowledgements

This work was supported by the National Science Foundation of China (No. U2034209, No. 62120106011, and No. U1934222), the Natural Science Foundation of Shaanxi Province of China (2021JC-42), and the Doctoral Dissertation Innovation Fund of Xi’an University of Technology (252072218).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [No. U2034209, No. 62120106011, and No. U1934222]; the Natural Science Foundation of Shaanxi Province of China [2021JC-42]; the Doctoral Dissertation Innovation Fund of Xi’an University of Technology [252072218].

Notes on contributors

Anqi Shangguan

Anqi Shangguan received the B.S degree in Measurement-Control Technology and Instrumentation from Baoji University of Arts and Science in 2017 and her M.S degree in Pattern recognition and intelligent system from Xi’an University of Technology in 2020. She is presently working on her Ph.D degree in pattern recognition and intelligent system from Xi’an University of Technology. Her research investigations have focused on reliability and safety assessment of the complex system.

Guo Xie

Guo Xie received his B.S degree in Automation and M.S degree in control theory and control engineering from Xi’an University of Technology, Xi’an, China, in 2005 and 2008, respectively. and received the D.E degree in Information Science from Nihon University, Tokoyo, Japan, in2013. He was a Japanese Government Scholarship holder from Japanese Ministry of Education, Culture, Sports, Science and Technology (Monbukagakusho). He is currently an associate professor at Xi’an University of Technology. His research interests include safety and reliability of railway system, optimal control and stochastic control. He is a member of the IEEE, CAA and CCF.

Lingxia Mu

Lingxia Mu received her B.S., M.S., and Ph.D. degrees from Northwestern Polytechnical University, Xi’an, China, in 2010, 2013 and 2018, respectively. During 2015 to 2017, she was a visiting Ph.D. student in the Department of Mechanical, Industrial & Aerospace Engineering, Concordia University, Montreal, Canada. She currently works at the Department of Automation and Information Engineering, Xi’an University of Technology, China. Her research interests include trajectory planning, guidance and control of unmanned systems.

Rong Fei

Rong Fei received the B.S. degree and M.S. degree in Computer Science and Technology from the Xi’an University of Technology, Xi’an, China in 2002 and 2005, and the Ph.D. degree in Power Electronic and Electrical Drive from the Xi’an University of Technology, Xi’an, China, in 2009, from 2009 to 2011. She has been an Associate Professor with the Faculty of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China. Her main research interests are Community detection, stochastic opposition algorithm and location-based service.

Xinhong Hei

Xinhong Hei received the B.S. degree and M.S. degree in computer science and technology from Xi’an University and Technology, Xi’an, China, in 1998 and 2003, respectively, and his Ph.D degree in Computer Science from Nihon University, Tokyo, Japan, in 2008. He is currently a professor with the Faculty of Computer Science and Engineering, Xi’an University of Technology, Xi’an, China. His research interests include intelligent systems and safety-critical system.