Combination of Probabilistic and Deterministic Models in Degradation Prognostics with Limited Data

Abstract

Sufficient and high quality data is a requirement for accurate modeling. Modern data acquisition practices and technologies provide good tools to meet these demands. Regardless of carefully implemented experiments, the collected data might not be detailed enough. Sometimes acquired data reveals phenomena that should be studied with a different scale or resolution, but used set-up cannot provide required data. In an ideal case, test set-up is updated to provide required capabilities and new experiments are performed. Often limited resources prevent revision of equipment and new tests, thus other solution has to be found. Lacking data can be supplemented with other existing information, knowledge, and new specific data acquisition, if additional research is possible. This paper concentrates on this problematic field. This study focuses on modeling of CAN-bus connector degradation based on limited data and improving usefulness of the existing data with supplementary information. The supplementary data was gathered with specific measurements and varying of the initial test procedure. In interpretation of the data, the knowledge of the design of CAN-bus connector and physics behind measured quantity were used. Supplementary information was used to rule out otherwise plausible model options. Acquired information is collected into the model that comprises deterministic parts and statistical components. The model can be applied in prognostics of CAN-bus, and used methods give tools to work with processes that are partially unknown.