Fabric defect detection using the wavelet transform in an ARM processor

Small devices used in our day life are constructed with powerful architectures that can be used for industrial applications when requiring portability and communication facilities. We present in this paper an example of the use of an embedded system, the Zeus epic 520 single board computer, for defect detection in textiles using image processing. We implement the Haar wavelet transform using the embedded visual C++ 4.0 compiler for Windows CE 5. The algorithm was tested for defect detection using images of fabrics with five types of defects. An average of 95% in terms of correct defect detection was obtained, achieving a similar performance than using processors with float point arithmetic calculations

Microwave flaw detector Patent

Surface defect detection by reflected microwave radiation patter

Phased electromagnetic acoustic transducer array for Rayleigh wave surface defect detection

A phased electromagnetic acoustic transducer (EMAT) array system has been developed for detection and characterisation of surface breaking defects. An array of four linear coils which are individually controlled are used to generate a Rayleigh wave. The high current electronics combined with the coil designs enables the array to generate either narrowband or broadband signals, and controlling the phase delay between the channels makes it possible to change the ultrasound wavelength without requiring the physical separation of the coils to be changed. Experimental results show that the four-coil phased array is able to generate a wavelength range from 3.0 mm to 11.7 mm. Surface breaking defects were characterised using a transmit-receive set-up with a broadband EMAT detector being used to detect the Rayleigh wave. Machined surface slots with different depths were used for technique validation. The results show that the array is sensitive to surface defects and that a wide depth sensitivity range for defect sizing can be easily achieved by applying phasing to tune the wavelength of operation. A large increase in detection flexibility is immediately shown

2-D iteratively reweighted least squares lattice algorithm and its application to defect detection in textured images

In this paper, a 2-D iteratively reweighted least squares lattice algorithm, which is robust to the outliers, is introduced and is applied to defect detection problem in textured images. First, the philosophy of using different optimization functions that results in weighted least squares solution in the theory of 1-D robust regression is extended to 2-D. Then a new algorithm is derived which combines 2-D robust regression concepts with the 2-D recursive least squares lattice algorithm. With this approach, whatever the probability distribution of the prediction error may be, small weights are assigned to the outliers so that the least squares algorithm will be less sensitive to the outliers. Implementation of the proposed iteratively reweighted least squares lattice algorithm to the problem of defect detection in textured images is then considered. The performance evaluation, in terms of defect detection rate, demonstrates the importance of the proposed algorithm in reducing the effect of the outliers that generally correspond to false alarms in classification of textures as defective or nondefective

Damages in rolling element bearings may be detected early

Early detection method locates damage or small defects in rolling element bearings of critical machine components. This detection method operates on the principle that an impact is generated each time a defect in an otherwise smooth surface is in intimate moving contact with another smooth surface

Scanning laser techniques for characterisation of different surface breaking defect geometries

Measurements using a laser scanning system consisting of a pulsed Nd:YAG laser to generate surface ultrasonic waves and an interferometer to detect the surface displacement, are presented for different samples and defect geometries. We show, firstly, details of the interaction of Rayleigh waves in thick samples with machined slots inclined at an angle to the surface normal, or with simple branched geometries, scanning the generation source over the defect (SLLS) or scanning the detection point over the defect (SLD). Secondly, we discuss effects of Lamb waves interacting with V-shaped defects in thin samples. The results from these measurements have shown that the signal enhancement found in the near-field in both cases can be used to position the defect and gain an idea of its geometry, and have shown this to be a suitable fingerprint of the presence of the defect