Noice reduction in control signals of industrial sewing machines using adaptive filtering

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.