Reliability Prediction for Mechatronic Drive Systems

Presently, for many systems and components, especially for mechatronic, electromechanical and also mechanical components of machines and devices, few failure data are available. Manufacturers gain information about the reliability and lifetime of their products, for a specific application, from endurance tests at customer-specific operating conditions. These experiments provide specific failure time data. However, they are time-consuming and expensive. Statistically ensured statements require performance tests with adequate test lot sizes, though it is impossible to cover all of the imaginable combinations of applied load profiles and impact parameters. Additionally, findings gained through experiments are valid only for the specific applied test conditions and loads. On the other hand, developers require as early as possible meaningful key data characterizing the applied components to determine the overall reliability of the device or machine. Often, modified components which are based on the same technology are applied using other load profiles. But the available test data cannot be applied as it is and first needs to be prepared. Using a variety of existing data sets from endurance tests of similar components and other load cases, we can derive prognoses for newly developed components under new application environments. For this purpose, we develop further, adopt and test stochastic models based on well-known regression models of Survival Analysis for engineering applications. The final objective of this research project is to develop prognosis tools to statistically predict the failure behaviour of mechatronic systems for values of the impact parameters that were not tested. The tools should be able to involve existing failure data for the prognosis. We demonstrate examples of data sets of DC motors and planetary gear drives, which were recorded at the IKFF facilities.