On-line quality control of DC permanent magnet motor using neural networks

This paper addresses the use of neural network methods to perform real-time quality control in industrial assembly lines of DC Permanent Magnet Motors (PM). This task can be viewed as a difficult non-linear inverse identification problem. Due to long parameter setting time, noisy environment and in some cases human supervision requirements, these methods are not adequate for real-time applications. Moreover, PM quality control requires the satisfaction of particular specifications rather than simple model parameter identification. So neural networks seem to be a promising paradigm. In this study we apply fast adaptive spline networks to perform complex model inversion and reduce the effect of noise on the data. Experimental results demonstrate the effectiveness of the proposed method.