Computer vision as a fast, low-cost, noncontact, and online monitoring technology has been an important tool to inspect product quality, particularly on a large-scale assembly production line. However, the current industrial vision system is far from satisfactory in the intelligent perception of complex grain images, comprising a large number of local homogeneous fragmentations or patches without distinct foreground and background. We attempt to solve this problem based on the statistical modeling of spatial structures of grain images. We present a physical explanation in advance to indicate that the spatial structures of the complex grain images are subject to a representative Weibull distribution according to the theory of sequential fragmentation, which is well known in the continued comminution of ore grinding. To delineate the spatial structure of the grain image, we present a method of multiscale and omnidirectional Gaussian derivative filtering. Then, a product quality classifier based on sparse multikernel–least squares support vector machine is proposed to solve the low-confidence classification problem of imbalanced data distribution. The proposed method is applied on the assembly line of a food-processing enterprise to classify (or identify) automatically the production quality of rice. The experiments on the real application case, compared with the commonly used methods, illustrate the validity of our method.

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In recent years, deep learning has become a popular topic in the intelligent fault diagnosis of industrial equipment. In practical working conditions, how to realize intelligent fault diagnosis in the case of the different mechanical components with a tiny labeled sample is a challenging problem. That means training with one component sample but testing with another component sample has not been resolved. In this paper, we propose a deep convolutional nearest neighbor matching network (DC-NNMN) based on few-shot learning. The 1D convolution embedding network is constructed to extract the high-dimensional fault feature. The cosine distance is merged into the K-Nearest Neighbor method to model the distance distribution between the unlabeled sample from the query set and labeled sample from the support set in high-dimensional fault features. The multiple few-shot learning fault diagnosis tasks as the testing dataset are constructed, and then the network parameters are optimized through training in multiple tasks. Thus, a robust network model is obtained to classify the unknown fault categories in different components with tiny labeled fault samples. We use the CWRU bearing vibration dataset, the bearing vibration data selected from the Lab-built experimental platform, and another gearing vibration dataset for across components experiment to prove the proposed method. Experimental results show that the proposed method can achieve fault diagnosis accuracy of 82.19% for gearing and 82.63% for bearings with only one sample of each fault category. The proposed DC-NNMN model provides a new approach to solve the across components fault diagnosis in few-shot learning.

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Computer vision-based rice quality inspection has recently attracted increasing interest in both academic and industrial communities because it is a low-cost tool for fast, non-contact, nondestructive, accurate and objective process monitoring. However, current computer-vision system is far from effective in intelligent perception of complex grainy images, comprised of a large number of local homogeneous particles or fragmentations without obvious foreground and background. We introduce a well known statistical modeling theory of size distribution in comminution processes, sequential fragmentation theory, for the visual analysis of the spatial structure of the complex grainy images. A kind of omnidirectional multi-scale Gaussian derivative filter-based image statistical modeling method is presented to attain omnidirectional structural features of grain images under different observation scales. A modified LS-SVM classifier is subsequently established to automatically identify the processing rice quality. Extensive confirmative and comparative tests indicate the effectiveness and outperformance of the proposed method.

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