No abstract

This paper proposes a vision-based fabric inspection system for the circular knitting machine. Firstly, a comprehensive fabric database called Fabric Defect Detection Database (FDDD) are constructed. To extract significant features of fabric images, shearlet transform is used. Means and variances are calculated from all subbands and combined into a high-dimensional feature vector. The proposed system is evaluated on a circular knitting machine in a textile factory. The real-time performance analysis is only carried out by inspecting single jersey knitted fabric. Our proposed system achieves the highest accuracy of 94.0% in the detection of single jersey knitting fabric defects.

No abstract

This paper proposes a vision-based fabric inspection system for the circular knitting machine. Firstly, a comprehensive fabric database called Fabric Defect Detection Database (FDDD) are constructed. To extract significant features of fabric images, shearlet transform is used. Means and variances are calculated from all subbands and combined into a high-dimensional feature vector. The proposed system is evaluated on a circular knitting machine in a textile factory. The real-time performance analysis is only carried out by inspecting single jersey knitted fabric. Our proposed system achieves the highest accuracy of 94.0% in the detection of single jersey knitting fabric defects.

This paper proposes a method for the separation of broken rotor bar failure and low-frequency load fluctuation in line-fed three-phase induction motor. In practice, the presence of load fluctuation at 2s f s has the same effect on a stator current of induction motor as a broken rotor bar fault. In such cases, the detection of broken rotor bar failure becomes difficult. To discern rotor fault and load oscillations, the analytical signal angular fluctuation (ASAF) method, which is a combination of Hilbert transform and the space vector angular fluctuation method, is used. The presented experimental results prove that low-frequency load oscillation and rotor fault can reliably be discriminated using the ASAF signal spectrum.