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Automatic slub detection is vital in the classification and identification of fabric images. This paper seeks to present a rapid and accurate approach for automatic detection of slub in fabric images using Gabor filters.

Slub can be regarded as defects along weft or warp. Gabor filters as bandpass filters consider the directional characteristics of slub and its frequency spectrum after Fourier transform. Choosing appropriate parameters for Gabor filters, slub can be detected accurately.

The proposed method achieves automatic detection of slub. The experimental results suggest that the authors approach is effective.

This paper considers appropriate parameters to design a Gabor filter for automatic detection of slub. And it is helpful to classify and identify fabric images.

Nowadays, most methods of illusion garment evaluation are based on the subjective evaluation of experienced practitioners, which consumes time and the results are too subjective to be accurate enough. It is necessary to explore a method that can quantify professional experience into objective indicators to evaluate the sensory comfort of the optical illusion skirt quickly and accurately. The purpose of this paper is to propose a method to objectively evaluate the sensory comfort of optical illusion skirt patterns by combining texture feature extraction and prediction model construction.

Firstly, 10 optical illusion sample skirts are produced, and 10 experimental images are collected for each sample skirt. Then a Likert five-level evaluation scale is designed to obtain the sensory comfort level of each skirt through the questionnaire survey. Synchronously, the coarseness, contrast, directionality, line-likeness, regularity and roughness of the sample image are calculated based on Tamura texture feature algorithm, and the mean, contrast and entropy are extracted of the image transformed by Gabor wavelet. Both are set as objective parameters. Two final indicators T1 and T2 are refined from the objective parameters previously obtained to construct the predictive model of the subjective comfort of the visual illusion skirt. The linear regression model and the MLP neural network model are constructed.

Results show that the accuracy of the linear regression model is 92%, and prediction accuracy of the MLP neural network model is 97.9%. It is feasible to use Tamura texture features, Gabor wavelet transform and MLP neural network methods to objectively predict the sensory comfort of visual illusion skirt images.

Compared with the existing uncertain and non-reproducible subjective evaluation of optical illusion clothing based on experienced experts. The main advantage of the authors' method is that this method can objectively obtain evaluation parameters, quickly and accurately obtain evaluation grades without repeated evaluation by experienced experts. It is a method of objectively quantifying the experience of experts.

This paper describes fabric inspection system aided by computer vision to detect and classify defects in circular knitted fabrics using different common texture-recognition methods, including co-occurrence matrices, the discrete Fourier transform, wavelets, Gabor, and clustering. The images of the fabrics were broadly classified into six classes: cracks, holes, vertical stripes, horizontal stripes, soil freckles, and defect-free. One hundred and twenty images (256 gray level and 100 dpi) containing 20 images of defect-free fabrics (rib 1x1) as well as 100 images corresponding to five different categories were used. In general, one-half of the images in each category were employed for training and the remaining images were used for testing.

The application of the clustering method applied in this work was found to be highly promising at identifying defects in knitted fabrics. With an overall success rate of 91.6%, the clustering method has a higher efficiency value than all of the other methods. In the case of the wavelet and Gabor methods, the results are acceptable. However, the overall success rates of the co-occurrence matrix and Fourier transform methods in recognizing defects in knitted fabrics are not acceptable.

About 13 to 44 per cent of motor faults are caused by bearing failures in induction motors (IMs), where lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication. An excessive amount of grease causes the rollers or balls to slide along the race instead of turning, and the grease will actually churn. This churning action will eventually wear down the base oil of the grease and all that will be left to lubricate the bearing is a thickener system with little or no lubricating properties. The heat generated from the churning, insufficient lubricating oil will begin to harden the grease, and this will prevent any new grease added to the bearing from reaching the rolling elements, with the consequence of bearing failure and equipment downtime. Regarding the case of grease excess in bearings, this case has not been sufficiently studied. This work aims to present an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the Margenau-Hill distribution (MHD) and artificial neural networks (ANNs), where the obtained results demonstrate the correct classification of the studied cases.

This work proposed an effective methodology applied to the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs.

In this paper, three cases of study for a bearing in an IM are studied, detected and classified correctly by combining some methods. The marginal frequency is obtained from the MHD, which in turn is achieved from the stator current signal, and a total of six features are estimated from the power spectrum, and these features are forwarded to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing.

The proposed methodology can be applied to other applications; it could be useful to use a time–frequency representation through the MHD for obtaining the energy density distribution of the signal frequency components through time for analysis, evaluation and identification of faults or conditions in the IM for example; therefore, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration.

The lubrication plays a significant role in maintaining rotating equipment because it minimizes friction and prevents wear by separating parts that move next to each other, and more than 35 per cent of bearing failures can be attributed to improper lubrication and it negatively affects the efficiency of the motor, resulting in higher operating costs. Therefore, in this work, a new methodology is proposed for the detection and automatic classification of mechanical bearing faults and bearing excessively lubricated conditions in an IM through the MHD and ANNs. The proposed methodology uses a total of six features estimated from the power spectrum, and these features are sent to the designed ANN with three output neurons, where each one represents a condition in the IM: healthy bearing, mechanical bearing fault and excessively lubricated bearing. From the obtained results, it was demonstrated that the proposed approach achieves higher classification performance, compared to short-time Fourier transform, Gabor transform and Wigner-Ville distribution methods, allowing to identify mechanical bearing faults and bearing excessively lubricated conditions in an IM, with a remarkable 100 per cent effectiveness during classification for treated cases. Also, the proposed methodology has a generalized nature that allows its application for detecting other conditions or even multiple conditions under different working conditions by a proper calibration.

The quality of produced textile fibers plays a very important role in the textile industry, and detection and assessment schemes are the key problems. Therefore, the purpose of this paper is to propose a relatively simple and effective technique to detect and assess the quality of produced textile fibers.

In order to achieve automatic visual inspection of fabric defects, first, images of the textile fabric are pre-processed by using Block-Matching and 3-D (BM3D) filtering. And then, features of textile fibers image are respectively extracted, including color, texture and frequency spectrum features. The color features are extracted by using hue–saturation–intensity model, which is more consistent with the human vision perception model; texture features are extracted by using scale-invariant feature transform scheme, which is a quite good method to detect and describe the local image features, and the obtained features are robust to local geometric distortion; frequency spectrum features of textiles are less sensitive to noise and intensity variations than spatial features. Finally, for evaluating the quality of the fabric in real time, two quantitatively metric parameters, peak signal-to-noise ratio and structural similarity, are used to objectively assess the quality of textile fabric image.

Compared to the quality between production and pre-processing of textile fiber images, the BM3D filtering method is a very efficient technology to improve the quality of textile fiber images. Compared to the different features of textile fibers, like color, texture and frequency spectrum, the proposed detection and assessment method based on textile fabric image feature can easily detect and assess the quality of textiles. Moreover, the objective metrics can further improve the intelligence and performance of detection and assessment schemes, and it is very simple to detect and assess the quality of textiles in the textile industry.

An intelligent detection and assessment method based on textile fabric image feature is proposed, which can efficiently detect and assess the quality of textiles, thereby improving the efficiency of textile production lines.

Many incidents of rotor failures are reported due to the development and propagation of the crack. Condition monitoring is adopted for the identification of symptoms of the crack at very early stage in the rotating machinery. Identification requires a reliable and accurate vibration analysis technique for achieving the objective of the study. The purpose of this paper is to detect the crack in the rotating machinery by measuring vibration parameters at different measurement locations.

Two different types of cracks were simulated in these experiments. Experiments were conducted using healthy shaft, crack simulated shaft and glued shaft with and without added unbalance to observe the changes in vibration pattern, magnitude and phase. Deviation in vibration response allows the identification of crack and its location. Initial data were acquired in the form of time waveform. Run-up and coast-down measurements were taken to find the critical speed. The wavelet packet energy analysis technique was used to get better localization in time and frequency zone.

The presence of crack changes the dynamic behavior of the rotor. 1× and 2× harmonic components for steady-state test and critical speed for transient test are important parameters in condition monitoring to detect the crack. To separate the 1× and 2× harmonic component in the different wavelet packets, original signal is decomposed in nine levels. Wavelet packet energy analysis is carried out to find the intensity of the signal due to simulated crack.

Original signals obtained from the experiment test set up may contain noise component and dominant frequency components other than the crack. Wavelet packets contain the crack-related information that are identified and separated in this study. This technique develops the condition monitoring procedure more specific about the type of the fault and accurate due to the separation of specific fault features in different wavelet packets. From the experiment end results, it is found that there is significant rise in a 2× energy component due to crack in the shaft. The intensity of a 1× energy component depends upon the shaft crack and unbalance orientation angle.

During the production process of steel strip, some defects may appear on the surface, that is, traditional manual inspection could not meet the requirements of low-cost and high-efficiency production. The purpose of this paper is to propose a method of feature selection based on filter methods combined with hidden Bayesian classifier for improving the efficiency of defect recognition and reduce the complexity of calculation. The method can select the optimal hybrid model for realizing the accurate classification of steel strip surface defects.

A large image feature set was initially obtained based on the discrete wavelet transform feature extraction method. Three feature selection methods (including correlation-based feature selection, consistency subset evaluator [CSE] and information gain) were then used to optimize the feature space. Parameters for the feature selection methods were based on the classification accuracy results of hidden Naive Bayes (HNB) algorithm. The selected feature subset was then applied to the traditional NB classifier and leading extended NB classifiers.

The experimental results demonstrated that the HNB model combined with feature selection approaches has better classification performance than other models of defect recognition. Among the results of this study, the proposed hybrid model of CSE + HNB is the most robust and effective and of highest classification accuracy in identifying the optimal subset of the surface defect database.

The main contribution of this paper is the development of a hybrid model combining feature selection and multi-class classification algorithms for steel strip surface inspection. The proposed hybrid model is primarily robust and effective for steel strip surface inspection.

The traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated.

The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area.

The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface.

The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.

A new method of spectral analysis has been proposed for non‐stationary harmonic analysis of corrosion processes. The current of a model circuit has been considered which would simulate a first‐order electrode reaction proceeding in conditions of a linearly changing electrode potential with a superimposed sinusoid signal. It has been shown that the Fourier transformation approach does not reflect the amplitude changes of harmonic components as a function of constant potential. In addition, it has been shown mathematically that application of Gabor transformation in spectral analysis is a means of obtaining the correct frequency components. The Gabor transform correctly reflects amplitude changes of harmonic components as a function of potential. Digital analysis of current changes by Gabor transformation unequivocally confirmed the usability of this method for harmonic analysis of corrosion processes.

This study aims to reveal the essential characteristics of nonstationary signals and explore the high-concentration representation in the joint time–frequency (TF) plane.

In this paper, the authors consider the effective TF analysis for nonstationary signals consisting of multiple components.

To make it, the authors propose the combined multi-window Gabor transform (CMGT) under the scheme of multi-window Gabor transform by introducing the combination operator. The authors establish the completeness utilizing the discrete piecewise Zak transform and provide the perfect-reconstruction conditions with respect to combined TF coefficients. The high-concentration is achieved by optimization. The authors establish the optimization function with considerations of TF concentration and computational complexity. Based on Bergman formulation, the iteration process is further analyzed to obtain the optimal solution.

With numerical experiments, it is verified that the proposed CMGT performs better in TF analysis for multi-component nonstationary signals.