Xue Cuiping, Zhang Tie, Xiao Dong
College of Science, Northeastern University, Shenyang 110819, China.
College of Information Science and Engineering and Liaoning Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Northeastern University, Shenyang 110819, China.
ACS Omega. 2021 Apr 19;6(16):10828-10839. doi: 10.1021/acsomega.1c00506. eCollection 2021 Apr 27.
In the process industry, fault monitoring related to output is an important step to ensure product quality and improve economic benefits. In order to distinguish the influence of input variables on the output more accurately, this paper introduces a subalgorithm of fault-unrelated block partition into the prototype knockoff filter (PKF) algorithm for its improvement. The improved PKF algorithm can divide the input data into three blocks: fault-unrelated block, output-related block, and output-unrelated block. Removing the data of fault-unrelated blocks can greatly reduce the difficulty of fault monitoring. This paper proposes a feature selection based on the Laplacian Eigen maps and sparse regression algorithm for output-unrelated blocks. The algorithm has the ability to detect faults caused by variables with small contribution to variance and proves the descent of the algorithm from a theoretical point of view. The output relation block is monitored by the Broyden-Fletcher-Goldfarb-Shanno method. Finally, the effectiveness of the proposed fault detection method is verified by the recognized Eastman process data in Tennessee.
在流程工业中，与输出相关的故障监测是确保产品质量和提高经济效益的重要步骤。为了更准确地区分输入变量对输出的影响，本文将故障无关块划分的子算法引入到原型仿冒滤波器（PKF）算法中进行改进。改进后的PKF算法可以将输入数据分为三个块：故障无关块、输出相关块和输出无关块。去除故障无关块的数据可以大大降低故障监测的难度。本文针对输出无关块提出了一种基于拉普拉斯特征映射和稀疏回归算法的特征选择方法。该算法能够检测由对方差贡献较小的变量引起的故障，并从理论角度证明了算法的下降性。通过Broyden-Fletcher-Goldfarb-Shanno方法对输出关系块进行监测。最后，利用田纳西州公认的伊士曼过程数据验证了所提出的故障检测方法的有效性。
