Main > Acta Optica Sinica >  Volume 40 >  Issue 24 >  Page 2423001 > Article
  • Abstract
  • Abstract
  • View Summary
  • Figures (6)
  • Tables (2)
  • Equations (0)
  • References (26)
  • Get PDF(in Chinese)
  • Paper Information
  • Received: Jul. 24, 2020

    Accepted: Sep. 16, 2020

    Posted: Dec. 1, 2020

    Published Online: Dec. 3, 2020

    The Author Email: Hu Changhua (huc66603@163.com)

    DOI: 10.3788/AOS202040.2423001

  • Get Citation
  • Copy Citation Text

    Xudong Gao, Changhua Hu, Jianxun Zhang, Dangbo Du, Hong Pei. Adaptive Prediction of Remaining Useful Life for Optoelectronic Equipment Based on Nonlinear Fractional Brownian Motion[J]. Acta Optica Sinica, 2020, 40(24): 2423001

    Download Citation

  • Category
  • Optical Devices
  • Share
Acta Optica Sinica, Vol. 40, Issue 24, 2423001 (2020)

Adaptive Prediction of Remaining Useful Life for Optoelectronic Equipment Based on Nonlinear Fractional Brownian Motion

Gao Xudong, Hu Changhua*, Zhang Jianxun, Du Dangbo, and Pei Hong

Author Affiliations

  • College of Missile Engineering, Rocket Force University of Engineering, Xi'an, Shaanxi 710025, China

Abstract

In the existing studies, the Markov process model without memory effects is usually used to describe the random degradation of optoelectronic equipment, ignoring the long-term correlation of states in the degradation process. In view of this, we firstly proposed a random degradation model with memory effects based on nonlinear fractional Brownian motion to describe the degradation process of optoelectronic equipment under the influence of measurement errors and random effects. On this basis, we employed the weak convergence theory to derive the approximate analytical formula of the remaining useful life of equipment in the sense of the first hitting time. Secondly, we adopted the maximum likelihood estimation algorithm and Bayesian inference to complete the offline estimation and real-time update of the model parameters, thus realizing the adaptive prediction of the remaining useful life. Finally, the proposed method was applied to the performance monitoring data of GaAs lasers. The experimental results show that the proposed method can effectively improve the prediction accuracy of the remaining useful life of optoelectronic equipment.

keywords

Please Enter Your Email: