Extending the operational lifespan of key components in a cyberphysical system. A method and case study

Nowadays extending the lifespan of key electronic components in cyber-physical systems is a must from the viewpoint of efficiency and sustainability. Up-to-date, there is a fresh push to use Artificial intelligence (AI) for estimating the remaining useful life (RUL) of electronic components. This paper proposes an automated framework for predicting RUL and classifying device failures. The framework includes a library of AI-based models to perform RUL estimation and to assess the actual condition of critical electronic components, without disconnecting them in a cyber-physical system. The best model is selected using the Q-learning method, which runs in parallel with the set of models. To evaluate the framework, a case study involving electrolytic capacitors of DC/DC power converters is presented. The results corroborate the suitability of the suggested approach in real-data sets and industrial test-bed.