Filtering of Dynamic Measurements in Intelligent Sensors for Fault Detection based on Data-Driven Models
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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, i.e. increasing the detection rate while decreasing or at least not worsening the overdetection rate, and hence to improve the efficiency of the overall system, signal analysis algorithms in intelligent sensors are applied to detect or even eliminate, i.e. filter disturbances such as peaks or drifts in the dynamic signals. The verification of the impact of filtering on fault detection statements due to real-life engine test bench measurements is presented at the end of the paper.