Percorrer por autor "Long, Jianyu"
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- A comparison of dimension reduction techniques for support vector machine modeling of multi-parameter manufacturing quality predictionPublication . Bai, Yun; Sun, Zhenzhong; Zeng, Bo; Long, Jianyu; Li, Lin; Valente de Oliveira, JOSÉ; Li, ChuanManufacturing quality prediction model, as an effective measure to monitor the quality in advance, has been developed using various data-driven techniques. However, multi-parameter in multi-stage of the modern manufacturing industry brings about the curse of dimensionality, leading to the difficulties for feature extraction, learning and quality modeling. To address this issue, three dimension reduction techniques are investigated in this paper, i.e., principal component analysis (PCA), locally linear embedding (LLE), and isometric mapping (Isomap). Specifically, the PCA is a linear dimension reduction technique, the LLE is a nonlinear reduction technique with local perspective, and the Isomap is a nonlinear reduction technique from global perspective. After getting the low-dimensional information from the PCA, the LLE, and the Isomap methods respectively, a support vector machine (SVM) is utilized for modeling. To reveal the effectiveness of the dimension reduction techniques and compare the difference of the three dimension reduction techniques, two experimental manufacturing data are collected from a competition about manufacturing quality control in Tianchi Data Lab of China. The comparison experiments indicate that the dimension reduction techniques have capacity for improving the SVM modeling performance indeed, and the Isomap-SVM model with the nonlinear global dimension reduction outperforms all the candidate models in terms of qualitative and quantitative analysis.
- Fusing convolutional generative adversarial encoders for 3D printer fault detection with only normal condition signalsPublication . Li, Chuan; Cabrera, Diego; Sancho, Fernando; Sanchez, Rene-Vinicio; Cerrada, Mariela; Long, Jianyu; Oliveira, José Valente deCollecting data from mechanical systems in abnormal conditions is expensive and time consuming. Consequently, fault detection approaches based on classical supervised learning working with both normal and abnormal data are not applicable in some condition-based maintenance tasks. To address this problem, this paper proposes Fusing Convolutional Generative Adversarial Encoders (fCGAE) method to create fault detection models from only normal data. Firstly, to obtain an adequate deep feature space, encoder models based on 1D convolutional neural networks are created. Then, these encoders are optimized in an unsupervised way through Bidirectional Generative Adversarial Networks. Finally, the multi-channel features collected from the system are merged with One-Class Support Vector Machine. fCGAE is applied to fault detection in 3D printers, where experimental results in two fault detection cases show excellent generalization capabilities and better performance compared to peer methods. (C) 2020 Elsevier Ltd. All rights reserved.