Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data

Abstract

In practical industrial applications, the key performance indicator (KPI)-related prediction and diagnosis are quite important for the product quality and economic benefits. To meet these requirements, many advanced prediction and monitoring approaches have been developed which can be classified into model-based or data-driven techniques. Among these approaches, partial least squares (PLS) is one of the most popular data-driven methods due to its simplicity and easy implementation in large-scale industrial process. As PLS is totally based on the measured process data, the characteristics of the process data are critical for the success of PLS. Outliers and missing values are two common characteristics of the measured data which can severely affect the effectiveness of PLS. To ensure the applicability of PLS in practical industrial applications, this paper introduces a robust version of PLS to deal with outliers and missing values, simultaneously. The effectiveness of the proposed method is finally demonstrated by the application results of the KPI-related prediction and diagnosis on an industrial benchmark of Tennessee Eastman process.

Keywords:

• PLS
• partial least squares
• data-driven
• KPI
• key performance indicator
• prediction
• diagnosis
• outlier
• missing value
• robust

Additional information

Funding

The authors acknowledge the support of China postdoctoral science foundation [grant number 2012M520738]; Heilongjiang postdoctoral fund [grant number LBH-Z12092].

Notes on contributors

Shen Yin

Shen Yin received his BE degree in automation from Harbin Institute of Technology, China, in 2004, MSc degree in control and information system and the PhD degree in electrical engineering and information technology from University of Duisburg-Essen, Germany, in 2007 and 2012, respectively. His research interests are model-based and data-driven fault diagnosis, fault-tolerant control and their application to large-scale industrial processes.

Guang Wang

Guang Wang received his BE degree in automation from Harbin Engineering University, Harbin, China, and the ME degree in control science and engineering from Harbin Institute of Technology, Harbin, China, in 2010 and 2012, respectively. He is currently working toward the PhD degree in control science and engineering with the Research Institute of Intelligent Control and Systems. His research interests include data-driven fault detection and diagnosis, performance monitoring, fault-tolerant control and their applications in the industrial process.

Xu Yang

Xu Yang received his BS degree in automation from Harbin Institute of Technology, Harbin, China, in 2012. He is currently a master in control theory and engineering at Harbin Institute of Technology. His current research interests include modelling and analysis of mechatronics, reliability analysis of process control system, application of subspace identification method and application of data-driven method.