Abstract:
Control signals of a typical industrial sewing machine are distorted when they are con
nected to the controller. Such distortions due to noise appear at the input port of the
control signals and they are, in general, nonstationary signals. Furthermore, access
to the controller of an industrial sewing machine is restricted. Therefore, such distor
tions cannot be attenuated using classical adaptive filters such as Wiener filters. In this
dissertation, an adaptive algorithm is developed in order to solve this challenging prob
lem. Here, an additive inverse of the distortion is generated and added to the control
signals so that the distortion is significantly attenuated. In order to generate the addi
tive inverse of the distortion, the
Normalized LeasMean Square (NLMS) algorithm is
employed as the adaptive algorithm with an external reference signal. In general, the
error signal to the filter is the estimation of the signal, However, based on the nature
of the adaptive filtering problem, the NLMS algorithm is formulated in a way that, the
error signal to the filter is the difference between the noise signal and the estimated
noise signal. The experimental results obtained with the control signals of a typical
industrial sewing machine confirm that the proposed method effectively attenuates the
distortion signal with fast convergence of the
NLMS algorithm.
Citation:
Niranjan, K.H.V.C. (2022). Noice reduction in control signals of industrial sewing machines using adaptive filtering [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21644