Filtering of dynamic measurements in intelligent sensors for fault detection based on data-driven models

Increasing complexity of test benches and the widespread use of automatic calibration and optimization tools leads to tighter requirements on the data quality. For many applications, like engine test benches, there are too few physical information a priori to allow the use of classical fault detection methods. In this paper, we propose a structure which has been developed and tested for engine test benches, in which data-driven models are built dynamically from measurements and fault detection is carried out by using data-driven models as reference situation. To improve the performance of fault detection statements, signal analysis algorithms are applied in intelligent sensors to detect disturbances such as peaks or drifts in the dynamic signals.