Damage Prediction and Remaining Useful Lifetime Assessment of a Discrete Power Electronic Component Using a Multi-Layer Perceptron based on Mission Profile Data

Bhat, DarshankumarDarshankumarBhatMünch, StefanStefanMünchRöllig, MikeMikeRöllig2022-07-282022-07-282022https://publica.fraunhofer.de/handle/publica/41917310.1109/ISSE54558.2022.9812777In this contribution, an automated routine is presented to assess the reliability of a discrete power electronic component used in electric bikes based on a data driven approach. Real temperature profiles are acquired from the field under different loading conditions of electric bikes and its features are extracted using a custom algorithm. A Multilayer Perceptron trained with synthetic but realistic temperature loads is used to predict creep strains induced in the solder joints of a chip resistor. In addition, the remaining useful lifetime has been evaluated at various stages of the mission profile. The methodology provided a solution for real-time capable remaining useful lifetime estimation method.enTemperature measurementtemperature sensorstrainingtemperature distributioncreeppredictive modelsfeature extractionDamage Prediction and Remaining Useful Lifetime Assessment of a Discrete Power Electronic Component Using a Multi-Layer Perceptron based on Mission Profile Dataconference paper