Doo-chul Choi

Demagnetization fault diagnosis method for PMSM of electric vehicle

Permanent magnet synchronous motor (PMSM) is attractive candidate for traction motor of electric vehicle (EV). But several kinds of faults can occur in PMSM system: bearing faults, electrically short/open faults and demagnetization fault, etc. Demagnetization fault means that strengths of permanent magnets (PM) in PMSM significantly decrease. The magnet flux linkage, which is proportional to the strengths of the PMs, can be estimated by means of conventional parameter identification methods and the estimation results can be used for detection of the fault. However, variations of q-d axis inductances and stator resistance can cause inaccurate estimation and failure of demagnetization fault detection . In this paper, demagnetization fault diagnosis method for PMSM subject to the parameter variations has been proposed. This method consists of the following serial processes: estimation of parameters variations, data extraction, the least square method, calculation of upper bounds of estimation error rate and magnet flux linkage, fault decision. This method is robust against estimation errors becuase of the upper bounds. It will be demonstrated in chapter IV.

Defect detection for corner cracks in steel billets using a wavelet reconstruction method

Presently, automatic inspection algorithms are widely used to ensure high-quality products and achieve high productivity in the steelmaking industry. In this paper, we propose a vision-based method for detecting corner cracks on the surface of steel billets. Because of the presence of scales composed of oxidized substances, the billet surfaces are not uniform and vary considerably with the lighting conditions. To minimize the influence of scales and improve the accuracy of detection, a detection method based on a visual inspection algorithm is proposed. Wavelet reconstruction is used to reduce the effect of scales. Texture and morphological features are used to identify the corner cracks among the defective candidates. Finally, the experimental results show that the proposed algorithm is effective in detecting corner cracks on the surfaces of the steel billets.

Pinhole detection in steel slab images using Gabor filter and morphological features

Presently, product inspection for quality control is becoming an important part in the steel manufacturing industry. In this paper, we propose a vision-based method for detection of pinholes in the surface of scarfed slabs. The pinhole is a very tiny defect that is 1-5 mm in diameter. Because the brightness in the surface of a scarfed slab is not uniform and the size of a pinhole is small, it is difficult to detect pinholes. To overcome the above-mentioned difficulties, we propose a new defect detection algorithm using a Gabor filter and morphological features. The Gabor filter was used to extract defective candidates. The morphological features are used to identify the pinholes among the defective candidates. Finally, the experimental results show that the proposed algorithm is effective to detect pinholes in the surface of the scarfed slab.

Detection of line defects in steel billets using undecimated wavelet transform

In this paper, we present a new detection algorithm for line defects of scale-covered steel billets. Because of the presence of scales on the billet surface, features of surface images such as brightness and textures are non-uniform. To minimize the influence of scales and to improve the accuracy of detection, a new detection method based on undecimated wavelet transform is proposed. The vertical projection profile of subimage with high-frequency information produced by undecimated wavelet transform is used to detect the line defects. Experimental results conducted on billets surface image from actual steel production line show that the proposed algorithm is capable of detecting line defects on billet surface.

Real-time defect detection of steel wire rods using wavelet filters optimized by univariate dynamic encoding algorithm for searches

We propose a new defect detection algorithm for scale-covered steel wire rods. The algorithm incorporates an adaptive wavelet filter that is designed on the basis of lattice parameterization of orthogonal wavelet bases. This approach offers the opportunity to design orthogonal wavelet filters via optimization methods. To improve the performance and the flexibility of wavelet design, we propose the use of the undecimated discrete wavelet transform, and separate design of column and row wavelet filters but with a common cost function. The coefficients of the wavelet filters are optimized by the so-called univariate dynamic encoding algorithm for searches (uDEAS), which searches the minimum value of a cost function designed to maximize the energy difference between defects and background noise. Moreover, for improved detection accuracy, we propose an enhanced double-threshold method. Experimental results for steel wire rod surface images obtained from actual steel production lines show that the proposed algorithm is effective.

Algorithm for detecting seam cracks in steel plates using a Gabor filter combination method

Presently, product inspection based on vision systems is an important part of the steel-manufacturing industry. In this work, we focus on the detection of seam cracks in the edge region of steel plates. Seam cracks are generated in the vertical direction, and their width range is 0.2-0.6 mm. Moreover, the gray values of seam cracks are only 20-30 gray levels lower than those of the neighboring surface. Owing to these characteristics, we propose a new algorithm for detecting seam cracks using a Gabor filter combination method. To enhance the performance, we extracted features of seam cracks and employed a support vector machine classifier. The experimental results show that the proposed algorithm is suitable for detecting seam cracks.

Steel-surface defect detection using a switching-lighting scheme

In this paper a novel filtering scheme combined with a lighting method is proposed for defect detection in steel surfaces. A steel surface has non-uniform brightness and various shaped defects, which cause difficulties in defect detection. To solve this problem we propose a sub-optimal filtering that is combined with a switching-lighting method. First, dual-light switching lighting (DLSL) is explained, which decreases the effect of non-uniformity of surface brightness and improves the detection accuracy. By using the DLSL method, defects are represented as alternated black and white patterns regardless of the size, shape, or orientation of defects. Therefore, defects can be detected by finding alternated black and white patterns. Second, we propose a scheme for detecting defects in steel-surface images acquired using the DLSL method. The presence of scales strongly affects the optical properties of the surface. Moreover, the textures of steel-plate images vary greatly because of the temperature and grade of steel. Therefore, conventional filter-design methods are not effective for different image textures. A sub-optimal scheme based on an optimized general-finite impulse-response filter is also proposed. Finally, experimental results conducted on steel-surface images from an actual steel-production line show the effectiveness of the proposed algorithm.

Affine projection algorithm with coefficient vector reusing

The recently proposed normalized least mean squares algorithm reuses estimated past coefficient vectors for the finite impulse response model and effectively reduces the misadjustment. The affine projection algorithm, which is an extension of the normalized least mean squares algorithm, generally suffers from relatively high misadjustment in the steady state. To decrease the misadjustment, we adopt the method of reusing the past coefficient vectors for the normalized least mean squares algorithm to the affine projection algorithm. Through computer simulations, we show the effect and performance of the affine projection algorithm which uses the coefficient vector reusing.

ENSEMBLE EVALUATION FOR IMAGE SEGMENTATION USING A SEMI-SUPERVISED SVM

A new unsupervised ensemble evaluation algorithm is proposed for image segmentation. A semi-supervised support vector machine is used to combine the existing unsupervised evaluators. We also proposed feature extraction and data selection procedures to enhance the overall performance. We experimentally demonstrated that our proposed algorithm is superior to existing segmentation evaluation measures.

Blowhole detection algorithm using texture analysis

In this paper, we developed a blowhole detection algorithm using texture analysis. We applied Gabor filter to extract defect candidates and used subsequently texture information to classify defect and pseudo-defect. To increase performance, size filtering and adaptive thresholding method were used. The proposed algorithm was tested on 343 images. The experimental result described in this paper shows that this algorithm was effective and suitable for blowhole detection in steel slabs.