A Neural Network Degradation Model for Computing and Updating Residual Life Distributions Academic Article uri icon


  • The ability to accurately estimate the residual life of partially degraded components is arguably the most challenging problem in prognostic condition monitoring. This paper focuses on the development of a neural network-based degradation model that utilizes condition-based sensory signals to compute and continuously update residual life distributions of partially degraded components. Initial predicted failure times are estimated through trained neural networks using real-time sensory signals. These estimates are used to derive a prior failure time distribution for the component that is being monitored. Subsequent failure time estimates are then utilized to update the prior distributions using a Bayesian approach. The proposed methodology is tested using real world vibration-based degradation signals from rolling contact thrust bearings. The proposed methodology performed favorably when compared to other reliability-based and statistical-based benchmarks. 2007 IEEE.

published proceedings

  • IEEE Transactions on Automation Science and Engineering

altmetric score

  • 6

author list (cited authors)

  • Gebraeel, N. Z., & Lawley, M. A.

citation count

  • 137

complete list of authors

  • Gebraeel, NZ||Lawley, MA

publication date

  • January 2008