Incipient fault detection and diagnosis of nonlinear industrial process with missing data,Journal of the Taiwan Institute of Chemical Engineers

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Incipient fault detection and diagnosis of nonlinear industrial process with missing data
Journal of the Taiwan Institute of Chemical Engineers ( IF 5.5 ) Pub Date : 2021-11-19 , DOI: 10.1016/j.jtice.2021.10.015
Miao Mou ₁ , Xiaoqiang Zhao _{1,

2,

3}

Affiliation

Background

In real industrial process, timely detection and diagnosis incipient fault is often more meaningful. At the same time, due to sensor failures or data acquisition system failures, process data may be missing or corrupted, resulting in loss of process information.

Methods

In view of the above problems, a Mixed Kernel function Dissimilarity Neighborhood Preserving Embedding (MKDNPE) method is proposed. Firstly, Low Rank Matrix Decomposition (LRMD) is used to recover the missing data, the recovered low rank matrix contains the main information of the process. Then, the MKDNPE model is developed in the recovered low rank matrix, where the mixed kernel function is composed of a Gaussian radial basis kernel function and a polynomial kernel function. It can simultaneously extract the local information of process data and the global characteristics of data structure, and deal with the nonlinear characteristic of process. Finally, the dissimilarity statistic is introduced for incipient fault detection, and the method based on contribution chart is used for fault diagnosis.

Significant findings

A numerical example and two benchmark processes are carried out for simulation verification. The simulation results further verified that the proposed method has good detection and diagnosis capabilities for incipient nonlinear faults in industrial processes with missing data.

中文翻译：

数据缺失的非线性工业过程初期故障检测与诊断

背景

在实际工业过程中，及时发现和诊断初期故障往往更有意义。同时，由于传感器故障或数据采集系统故障，过程数据可能会丢失或损坏，导致过程信息丢失。

方法

针对上述问题，提出了一种混合核函数相异邻域保持嵌入(MKDNPE)方法。首先，低秩矩阵分解（LRMD）用于恢复丢失的数据，恢复的低秩矩阵包含过程的主要信息。然后，在恢复的低秩矩阵中开发MKDNPE模型，其中混合核函数由高斯径向基核函数和多项式核函数组成。它可以同时提取过程数据的局部信息和数据结构的全局特征，处理过程的非线性特征。最后，引入相异统计量用于初期故障检测，并采用基于贡献图的方法进行故障诊断。