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Early diagnosis in DC-link capacitors: electrolytic and films

Early diagnosis in DC-link capacitors: electrolytic and films

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This chapter presents an RUL prediction algorithm based on accelerated life test data and derived physics models for electrolytic capacitors. The main elements are (1) development of the first principles based degradation models; (2) the implementation of a Bayesian-based health state tracking and RUL prediction algorithm based on the Kalman filtering framework. One major advancement reported here is the prediction of RUL for capacitors as new measurements become available. The key contribution of this work is the prediction of RUL for capacitors as new measurements become available. The derived degradation models can be updated and developed at a finer granularity to be implemented for detailed prognostic implementation. This capability increases the technology readiness level ofprognostics applied to electrolytic capacitors.The results presented here are based on accelerated life test data and on the accelerated life timescale. Further research will focus on development of functional mappings that will translate the accelerated life timescale into real usage conditions timescale, where the degradation process dynamics will be slower and subject to several types of stresses. The performance of the proposed exponential-based degradation model is satisfactorily based on the quality of the model fit to the experimental data and the RUL prediction performance as compared to ground truth.

Chapter Contents:

  • 3.1 Introduction
  • 3.1.1 Research challenges
  • 3.1.2 Organization
  • 3.2 Modeling for prognostics
  • 3.3 Research methodology
  • 3.4 Degradation in electrolytic capacitors
  • 3.4.1 Degradation mechanisms
  • 3.4.2 Capacitor degradation models
  • 3.4.3 Physics-based models for C and ESR
  • 3.4.3.1 Capacitance model
  • 3.4.3.2 ESR model
  • 3.4.3.3 Computing electrolyte volume from capacitor geometry
  • 3.4.3.4 Electrolyte evaporation model
  • 3.4.4 Time-dependent degradation models
  • 3.5 Model-based prognostics framework
  • 3.5.1 Kalman filter for state estimation
  • 3.5.2 Future state forecasting
  • 3.5.3 Noise models
  • 3.5.4 Prognostics problem formulation
  • 3.5.5 Physics-based modeling framework using unscented Kalman filter
  • 3.5.5.1 Capacitance degradation dynamic model
  • 3.5.5.2 UKF for capacitance state estimation
  • 3.5.5.3 ESR degradation dynamic model
  • 3.5.5.4 UKF for ESR state estimation
  • 3.6 Accelerated aging experiments
  • 3.6.1 Experimental setup
  • 3.6.2 Electrical overstress
  • 3.6.3 EOS experiment
  • 3.7 Prediction of remaining useful life results and validation tests
  • 3.7.1 Results for capacitor degradation model (D4)
  • 3.7.1.1 Remaining useful life
  • 3.7.1.2 Validation tests
  • 3.7.1.3 Discussion
  • 3.7.2 Results fordegradation model (D5)
  • 3.7.2.1 Remaining useful life
  • 3.7.2.2 Validation tests
  • 3.8 Conclusion
  • References

Inspec keywords: electrolytic capacitors; electron device testing; condition monitoring; reliability; Kalman filters; remaining life assessment

Other keywords: accelerated life timescale; prognostic implementation; film capacitors; accelerated life test data; Bayesian-based health state tracking; DC-link capacitors diagnosis; electrolytic capacitors; stresses; RUL prediction algorithm; remaining useful life; functional mappings; degradation models; physics models; Kalman filtering framework

Subjects: Inspection and quality control; Reliability; Maintenance and reliability; Capacitors; Production facilities and engineering; Testing; Filtering methods in signal processing

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