针对高维噪声数据的故障诊断难题，提出无监督鲁棒诊断新方法。
Robust Unsupervised Fault Diagnosis For High-Dimensional Nonlinear Noisy Data
- 设计高维故障数据专用降维方法，保留关键特征。
- 通过图结构学习增强非线性特征，提升识别能力。
- 引入l2,1范数与典型性约束，有效抑制噪声和异常值影响。
传统故障诊断方法难以应对高维、强噪声等复杂数据特性。深度学习虽有潜力，但通常依赖标注数据。本文提出一种鲁棒的无监督故障诊断方法：首先设计针对高维故障数据的特殊降维方法；其次通过图结构学习融入非线性信息以增强特征表达；再次从模型优化角度引入$l_{2,1}$-范数与典型性感知约束，缓解噪声和离群点导致的诊断精度下降问题。在基准Tennessee-Eastman过程和真实热轧钢铣削过程上的实验表明，该方法在存在异常值或噪声时仍保持高诊断准确率，优于现有方法，具备更强鲁棒性。
原文摘要 · Abstract (English)
Traditional fault diagnosis methods struggle to handle fault data, with complex data characteristics such as high dimensions and large noise. Deep learning is a promising solution, which typically works well only when labeled fault data are available. To address these problems, a robust unsupervised fault diagnosis using machine learning is proposed in this paper. First, a special dimension reduction method for the high-dimensional fault data is designed. Second, the extracted features are enhanced by incorporating nonlinear information through the learning of a graph structure. Third, to alleviate the problem of reduced fault-diagnosis accuracy attributed to noise and outliers, $l_{2,1}$-norm and typicality-aware constraints are introduced from the perspective of model optimization, respectively. Finally, this paper provides comprehensive theoretical and experimental evidence supporting the effectiveness and robustness of the proposed method. The experiments on both the benchmark Tennessee-Eastman process and a real hot-steel milling process show that the proposed method exhibits better robustness compared to other methods, maintaining high diagnostic accuracy even in the presence of outliers or noise.
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