A Practical Sensors Software to Manage Fault Signals’ Impact

The closely detailed search of sensed feature quality to ensure fault detection from weak sensors, which can skew the application model results signals, is required. As principal component analysis (PCA) is limited to correct isolate faulty signals’ impact, unlike the relative performance of neural Kohonen self-organizing map’s model to monitor air quality on any real complex condition. Indeed, this unsupervised method is enhanced by itself (2-SOM) and SOM hierarchical clustering (SOMHC) with both learning types; sequential and batch. These former models are improved also with Bubble, Gaussian, Gaussian Cut and Epanichnikov Kernel neighborhood functions, in graphical user interface form. Therefore, the study demonstrates more eective and complete results showing in quantization and topography errors including responses classification accuracy as well as in the KSOM-HCs dendograms view. Furthermore, this tool is relevant to provide the credible informations of pollutant detection, dedicated to Human Health safety, despite the conditions complexity.