The tracking of aerospace engines is reasonably achieved through a trajectography radar system that generally yields a disperse cloud of samples on tridimensional space, which roughly describes the engine trajectory. It is proposed an approach on cleaning radar data to yield a wellbehaved and smooth output curve that could be used as basis for instant and further analysis by radar specialists. This approach consists on outlier detection and smoothing phases based on established techniques such as Hampel filter and local regression (LOESS). To prove the effectiveness of the approach, both filtered and unfiltered data are submitted to an extrapolation method, and the results are compared.
Industries are generally data rich but information poor environments. Massive data generated in industrial operations is traditionally neglected (or simply took aside) mainly due to systems design restrictions, to the lack of adequate processing power of typically installed computing infrastructure and to a sector culture notably focused on collecting, selecting, storing and preserving historical series in on-demand access repositories. This huge amount of unprocessed data resting in these repositories is a latent source of information that could be used to improve industrial processes. This work then proposes an approach in which a proper computing power processing engine is plugged-in to current industrial information infrastructure to provide it with the ability of handling massive industrial data. Testing on real-world industrial data volumes of 5GB, 50GB and 100GB attested the effectiveness and potential of the proposed approach in dealing with Industrial Big Data.
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