Kernel principal component analysis (KPCA) is a well-recognized nonlinear dimensionality reduction method that has been widely used in nonlinear fault detection tasks. As a kernel trick-based method, KPCA inherits two major problems. First, the form and the parameters of the kernel function are usually selected blindly, depending seriously on trial-and-error. As a result, there may be serious performance degradation in case of inappropriate selections. Second, at the online monitoring stage, KPCA has much computational burden and poor real-time performance, because the kernel method requires to leverage all the offline training data. In this work, to deal with the two drawbacks, a learnable faster realization of the conventional KPCA is proposed. The core idea is to parameterize all feasible kernel functions using the novel nonlinear DAE-FE (deep autoencoder based feature extraction) framework and propose DAE-PCA (deep autoencoder based principal component analysis) approach in detail. The proposed DAE-PCA method is proved to be equivalent to KPCA but has more advantage in terms of automatic searching of the most suitable nonlinear high-dimensional space according to the inputs. Furthermore, the online computational efficiency improves by approximately 100 times compared with the conventional KPCA. With the Tennessee Eastman (TE) process benchmark, the effectiveness and superiority of the proposed method is illustrated.
翻译:内核元件分析(KPCA)是一种公认的非线性尺寸减少方法,在非线性故障探测任务中广泛使用。作为一种内核的把戏方法,金伯利公司继承了两个主要问题。首先,内核函数的形式和参数通常都是盲目选择的,严重依赖试探和试探。结果,在选择不当的情况下,业绩可能严重退化。第二,在在线监测阶段,金伯利公司有许多计算负担和实时性能差,因为内核方法需要利用所有离线培训数据。在这项工作中,处理两个缺点,提议更快地实现传统的金伯利公司功能。核心想法是使用新的非线性DAE-FE(基于深度自动电解剖地物提取)框架,对所有可行的内核功能进行参数参数参数参数参数参数参数参数参数化,并详细提出DAE-PCA(基于深度自动电解元件的主要部件分析)方法。拟议的DAE-PCA方法证明相当于所有离线培训数据。在这项工作中,处理两种缺点,可以更快地更快地理解传统的国际金伯利标准,通过最高级的计算方法,从而比较地改进了常规方法。