Making Factories Smarter Through Machine Learning
because the data memory size required would need to be exceedingly large - impractical and unnecessary .
The other capability provided by the software is the ability to read complex sensors and perform pre-processing in terms of data reduction : For example , vibration is sampled at least two times the vibration frequency . In this case , a fast Fourier transform is performed and only the frequency of interest is stored . This is an area where there is high opportunity for more efficient processing – effectively using machine learning for pre-processing and feature selection .
Next , data acquired from different sources needs to be joined to assure completeness of database and avoid empty data spaces ( Not a Number -NaN ) 12 . This Sensor fusion is performed using different multivariate techniques in real time .
From this point , as shown in Figure 1 , data is processed and then served to a superior process , which can be a machine learning algorithm , visualization and / or storage . The data is sent using OPC Unified Architecture ( OPC-UA ) or other protocols depending on the needs . For example , if data is needed for real time visualization below 45ms , OPC-UA protocol is used .
Figure 1 : The elements and the connectivity being utilized to develop and provide updates to the production system . Based on historical data acquired during typical operation , machine learning algorithms , both unsupervised and supervised , utilize this and other real-time operational data to identify and effectively learn system behavior patterns during the machining process . The data is analyzed in
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R . C . LUO , C . -C . YIH y K . L . SU , « Multisensor fusion and integration : approaches , applications , and future research directions .,» IEEE Sensors journal , vol . 2 , n º 2 , 2002
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