Resource-optimized verification planning for mechatronic systems in the virtual stage of product creation

Most current methods for product verification and reliability assurance are based upon statistical sample sizes and the underlying probability distributions. But that approach typically results in industrially unmanageable sample sizes and high test resource requirements. The proposed approach in this thesis uses a generalized "Inverse Most-Probable-Limit State" concept, which takes a Monte-Carlo based most likely noise factor scenario in all load dimensions into account for each failure mode.