Automatic Optical Apparatus for Inspecting Bearing Assembly Defects

Ye, Jhan-Hong; Hsu, Quang‐Cherng

doi:10.18494/sam.2018.2113

Cited by 4 publications

(1 citation statement)

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“…With the continuous development and progress of modern science and technology, when we need to detect defects, machine vision begins to be more and more used. Ye and Hsu designed a new lighting system to collect images in a darkroom, avoiding the influence of external factors and light sources, and developed a rule-based local mask sensor algorithm to achieve high-precision detection of metal defects [3]. Shen et al designed a new type of lighting and image acquisition system.…”

Section: Introductionmentioning

confidence: 99%

Bearing Defect Detection with Unsupervised Neural Networks

Zuo

Danchao³

et al. 2021

Shock and Vibration

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Bearings always suffer from surface defects, such as scratches, black spots, and pits. Those surface defects have great effects on the quality and service life of bearings. Therefore, the defect detection of the bearing has always been the focus of the bearing quality control. Deep learning has been successfully applied to the objection detection due to its excellent performance. However, it is difficult to realize automatic detection of bearing surface defects based on data-driven-based deep learning due to few samples data of bearing defects on the actual production line. Sample preprocessing algorithm based on normalized sample symmetry of bearing is adopted to greatly increase the number of samples. Two different convolutional neural networks, supervised networks and unsupervised networks, are tested separately for the bearing defect detection. The first experiment adopts the supervised networks, and ResNet neural networks are selected as the supervised networks in this experiment. The experiment result shows that the AUC of the model is 0.8567, which is low for the actual use. Also, the positive and negative samples should be labelled manually. To improve the AUC of the model and the flexibility of the samples labelling, a new unsupervised neural network based on autoencoder networks is proposed. Gradients of the unlabeled data are used as labels, and autoencoder networks are created with U-net to predict the output. In the second experiment, positive samples of the supervised experiment are used as the training set. The experiment of the unsupervised neural networks shows that the AUC of the model is 0.9721. In this experiment, the AUC is higher than the first experiment, but the positive samples must be selected. To overcome this shortage, the dataset of the third experiment is the same as the supervised experiment, where all the positive and negative samples are mixed together, which means that there is no need to label the samples. This experiment shows that the AUC of the model is 0.9623. Although the AUC is slightly lower than that of the second experiment, the AUC is high enough for actual use. The experiment results demonstrate the feasibility and superiority of the proposed unsupervised networks.

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Section: Introductionmentioning

confidence: 99%