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.

本研究解决了传统故障诊断方法在处理高维和噪声数据时的不足。提出了一种稳健的无监督故障诊断方法，通过特殊的降维技术和图结构的非线性信息学习来增强特征，并引入$l_{2,1}$范数和典型性约束以优化模型，显著提高了故障诊断的准确性和鲁棒性。实验结果表明该方法在面对异常值和噪声时仍能保持高诊断准确率，具有重要的应用价值。

高维非线性噪声数据的稳健无监督故障诊断