The knitting needle cylinder is one of the core parts of a hosiery machine. The operation of its needles can directly affect the production quality and efficiency of the hosiery machine. To reduce the production loss of a hosiery machine caused by knitting needle faults, a knitting needle fault detection system for hosiery machines based on a synergistic combination of laser detection and machine vision is proposed in this paper. When the system was operating normally, a photoelectric detector collected the laser signal reflected by the knitting needle and the system monitored the operation of the knitting needle using the ratio of adjacent peak-to-peak distances of the signals. When a fault signal was detected, the hosiery machine was stopped by the system immediately, and a charge-coupled device camera was used to take an image of the faulty knitting needle. After image preprocessing, the faulty knitting needle could be identified quickly and accurately using an image region size classifier based on a decision tree. The experimental results showed that a single image classification by the classifier could be performed in as little as 0.002 s.

Problems with knitting needles are one of the main causes of production loss of fabric. In order to detect problems with knitting needles quickly and accurately, this paper proposes a hosiery knitting needle detection system based on machine vision. Meanwhile, according to the working condition of real hosiery knitting needles, a simulated needle cylinder rotary platform is built. The system can detect knitting needle problems, and the needle causing the issue can be identified as the beginning of the fabric defect appears. The production losses caused by the bending and fracture of knitting needles are reduced at the source. In the image processing, the vertical projection algorithm is introduced in the system, and the defect of image binarization is improved. At the same time, the multi-classification support vector machine based on the decision tree is used to realize the image classification. The experimental results show that the classification accuracy of the two levels classifiers in the system can reach 100% and 98%, and the best time of system detection is 0.348 s.

The number of phase wraps that result from the carrier component can be completely eliminated or reduced by first applying a fast Fourier transform (FFT) to the image and then shifting the spectrum to the origin. However, because the spectrum can only be shifted by an integer number, the phase wraps of the carrier component cannot be completely reduced. In this paper, an improved carrier frequency-shifting algorithm based on 2-FFT for phase wrap reduction is proposed which allows the spectrum to be shifted by a rational number. Firstly, the phase wraps are reduced by the conventional FFT frequency shift method. Secondly, the wrapped phase with residual carrier components is filtered and magnified sequentially; the amplified phase is transformed into the frequency domain using an FFT, and then, the wrapped phase with the residual carrier components can be further reduced by shifting the spectrum by a rational number. Simulations and experiments were conducted to validate the efficiency of the proposed method.

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