Noise Source Identification of the Carpet Tufting Machine Based on the Single Channel Blind Source Separation and Time-Frequency Signal Analysis

Abstract

Noise source identification is the first key step to reduce the noise pressure level of the carpet tufting machine. For identifying the main noise sources of the carpet tufting machine, the single channel blind source separation (SCBSS) method is proposed to separate the acquired single channel noise, and the time-frequency signal analysis is applied to identify separated noise components. The SCBSS includes ensemble empirical mode decomposition (EEMD), improved Akaike information criterion (AIC) source number estimation and fast independent component analysis (FastICA). The separation method based on EEMD-AIC-FastICA not only overcomes traditional blind source separation problems that require enough test channel numbers, but also solves the problem that the number of virtual multichannel signals is unknown. Four independent components (ICs) are obtained after using the SCBSS. Combining the time-frequency analysis of the four ICs and the acquired vibration signals of six main components, the specific four noise sources can be identified. The four ICs correspond to the noise of needles, noise of hooks, noise of hook driven shaft, and noise of motor spindle, respectively.

Optimization Method for Denoising Yarn Tension Signals Based on Empirical Wavelet Transform and Adaptive Wavelet Threshold Denoising

“…Chiu et al 8 used Fast Independent Component Analysis (FastICA) to separate unknown noise signals and abnormal tension signals on a yarn twisting machine, successfully preserving the main tension signal information. Sheng et al 9 proposed a Single-Channel Blind Source Separation (SCBSS) method to separate single-channel noise, but its effectiveness may be limited when noise originates from multiple channels or exhibits overlap in frequency or time. Mao et al 10 designed a PSO-Variational Mode Decomposition (VMD) method for signal denoising, achieving adaptive denoising through PSO optimization of VMD.…”