Sha Xin, Diao Naizhe
The College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
The College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
ACS Omega. 2022 Nov 25;7(48):43440-43449. doi: 10.1021/acsomega.2c03295. eCollection 2022 Dec 6.
Industrial data are in general corrupted by noises and outliers, which do not meet the application assumptions in feature extraction. Many existing feature extraction algorithms are not robust, overly consider the less important features of the data, and cannot capture the key features of the data. To this end, the two-level feature extraction method (TFEM) based on -norm is proposed in this study. Compared with single-projection feature extraction algorithms, TFEM consists of two projections: the nonreduced and reduced dimensionality projections. The nonreduced dimensionality projection can remove the parts of less important features that are unrelated to the key features of the data. The reduced dimensionality projection can reduce the dimensionality of the data and further extract the features of the data. In addition, -norm is used to make the algorithm more robust. Finally, the convergence of the proposed algorithm is analyzed. Extensive experiments have been conducted on the Tennessee Eastman and Penicillin Fermentation processes to demonstrate that the proposed method is more effective than other state-of-the-art fault detection methods.
工业数据通常会受到噪声和离群值的干扰，这些噪声和离群值在特征提取中不符合应用假设。许多现有的特征提取算法不够稳健，过度考虑了数据中不太重要的特征，无法捕捉数据的关键特征。为此，本研究提出了基于 -范数的两级特征提取方法（TFEM）。与单投影特征提取算法相比，TFEM 由两个投影组成：非降维和降维投影。非降维投影可以去除与数据关键特征无关的不太重要特征的部分。降维投影可以降低数据的维度并进一步提取数据的特征。此外，使用 -范数使算法更稳健。最后，分析了所提算法的收敛性。在田纳西伊士曼和青霉素发酵过程上进行了大量实验，以证明所提方法比其他现有最先进的故障检测方法更有效。
