Remaining Useful Life Estimation Algorithms
topic
Remaining useful life estimation for textile machine components uses physics-based degradation models for components with well-characterised wear mechanisms such as ring travellers and spinning rings, and data-driven recurrent neural network or Gaussian process regression models for complex components where failure physics are not analytically tractable, providing probabilistic RUL estimates with confidence intervals that enable risk-based maintenance scheduling decisions.
Role
Provides the actionable time-to-failure estimates that enable optimal maintenance timing decisions by specifying not just that a component is degrading but how much useful life remains before the probability of failure reaches an unacceptable level, with RUL estimation enabling the maintenance window optimisation that is the ultimate objective of predictive maintenance by providing the quantitative life estimate needed to schedule intervention at the most convenient upcoming machine stop opportunity.
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