A Novel Framework for Fault Diagnosis Using Kernel Partial Least Squares Based on an Optimal Preference Matrix
A Novel Framework for Fault Diagnosis Using Kernel Partial Least Squares Based on an Optimal Preference Matrix
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基于最优偏好矩阵的核偏最小二乘故障诊断新框架
DOI：
10.1109/tie.2017.2668986
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发表时间：
2017
影响因子：
7.7
通讯作者：
Li Taifu
中科院分区：
文献类型：
--
作者：
Yi Jun;Huang Di;He Haibo;Zhou Wei;Han Qi;Li Taifu
在标准的核偏最小二乘（KPLS）中，在提取新的得分向量之前，需要将特征空间中的映射数据集中。然而，集中变量的各个向量往往是均匀分布的，可能会丢失一些能够反映各个变量对故障诊断贡献的原始特征。结果，它可能导致对主成分的误解，并增加故障检测的虚警率。为了解决这些困难，本文提出了一种新的数据驱动框架，使用基于最优偏好矩阵（OPM）的KPLS。在故障监测中，提出了一种改变变量分布和重新调整协方差矩阵特征值的OPM。为了获得OPM，目标函数可以根据预测误差的平方和霍特林的T平方（${T^2}$）统计来确定。两种优化算法，遗传算法和粒子群优化算法，扩展到最大限度地提高效率的OPM。与传统方法相比，该方法克服了特征子空间集中映射数据丢失原始特征的缺点，提高了故障诊断的准确率。此外，在故障检测中需要很少的额外计算成本。对田纳西伊士曼基准过程和铝电解生产过程的案例研究进行了大量的实验研究，给出了可靠的故障诊断结果。
In the standard kernel partial least squares (KPLS), the mapped data in the feature space need to be centralized before extraction of new score vectors. However, each vector of the centralized variables is often uniformly distributed, and some original features that can reflect the contribution of each variable to fault diagnosis might be lost. As a result, it might lead to misleading interpretations of the principal components and to increasing the false alarm rate for fault detection. To cope with these difficulties, a novel data-driven framework using KPLS based on an optimal preference matrix (OPM) is presented in this paper. In fault monitoring, an OPM is proposed to change the distribution of the variable and to readjust the eigenvalues of the covariance matrix. To obtain the OPM, the objective function can be determined in terms of the squared prediction error and Hotelling's T-squared ( ${T^2}$) statistics. Two optimization algorithms, genetic algorithm and particle swarm optimization algorithm, are extended to maximize effectiveness of the OPM. Compared with traditional methods, the proposed method can overcome the drawback of original features loss of the centralized mapped data in the feature subspace and improve the accuracy of fault diagnosis. Also, few extra computation costs are needed in fault detection. Extensive experimental results on both the Tennessee Eastman benchmark process and the case study of the aluminum electrolytic production process give credible fault diagnosis.