An Advanced PLS Approach for Key Performance Indicator-Related Prediction and Diagnosis in Case of Outliers

In the process industry, the key performance indicator (KPI)-related prediction and fault diagnosis are important steps to guarantee the product quality and improve economic benefits. A popular monitoring method as it has been, the partial least squares (PLS) algorithm is sensitive to outliers in training datasets, and cannot efficiently distinguish faults related to KPI from those unrelated to KPI due to its oblique projection to the input space. In this paper, a novel robust data-driven approach, named advanced partial least squares (APLS), is presented to handle process outliers under an improved framework of PLS. By means of a weighting strategy, APLS can remove the impact of outliers on process measurements and establish a more accurate model than PLS for fault diagnosis in the monitoring scheme, whose effectiveness has been verified through the Tennessee Eastman (TE) benchmark process. Simulation results demonstrate that the proposed approach is suitable not only for the KPI-related process prediction but also for the diagnosis of KPI-related faults.

[1]  Huijun Gao,et al.  Data-Driven Process Monitoring Based on Modified Orthogonal Projections to Latent Structures , 2016, IEEE Transactions on Control Systems Technology.

[2]  Peter Filzmoser,et al.  Partial robust M-regression , 2005 .

[3]  Daniel Peña,et al.  A robust partial least squares regression method with applications , 2009 .

[4]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[5]  Salah Laghrouche,et al.  Adaptive Second-Order Sliding Mode Observer-Based Fault Reconstruction for PEM Fuel Cell Air-Feed System , 2015, IEEE Transactions on Control Systems Technology.

[6]  Steven X. Ding,et al.  A New Soft-Sensor-Based Process Monitoring Scheme Incorporating Infrequent KPI Measurements , 2015, IEEE Transactions on Industrial Electronics.

[7]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .

[8]  Fuwen Yang,et al.  Data-driven subspace-based adaptive fault detection for solar power generation systems , 2013 .

[9]  Guang Wang,et al.  Quality-Related Fault Detection Approach Based on Orthogonal Signal Correction and Modified PLS , 2015, IEEE Transactions on Industrial Informatics.

[10]  Xu Yang,et al.  Robust PLS approach for KPI-related prediction and diagnosis against outliers and missing data , 2014, Int. J. Syst. Sci..

[11]  Zhiqiang Ge,et al.  Robust modeling of mixture probabilistic principal component analysis and process monitoring application , 2014 .

[12]  Okyay Kaynak,et al.  Big Data for Modern Industry: Challenges and Trends [Point of View] , 2015, Proc. IEEE.

[13]  Hao Ye,et al.  Fault diagnosis based on parameter estimation in closed-loop systems , 2015 .

[14]  Frank Nielsen,et al.  Total Bregman divergence and its applications to shape retrieval , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Shen Yin,et al.  A modified partial robust M-regression to improve prediction performance for data with outliers , 2013, 2013 IEEE International Symposium on Industrial Electronics.

[16]  Okyay Kaynak,et al.  An LWPR-Based Data-Driven Fault Detection Approach for Nonlinear Process Monitoring , 2014, IEEE Transactions on Industrial Informatics.

[17]  Shen Yin,et al.  Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System , 2015, IEEE Transactions on Industrial Electronics.

[18]  Bhupinder S. Dayal,et al.  Improved PLS algorithms , 1997 .

[19]  Shen Yin,et al.  Performance Monitoring for Vehicle Suspension System via Fuzzy Positivistic C-Means Clustering Based on Accelerometer Measurements , 2015, IEEE/ASME Transactions on Mechatronics.

[20]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[21]  Kaixiang Peng,et al.  A Novel Scheme for Key Performance Indicator Prediction and Diagnosis With Application to an Industrial Hot Strip Mill , 2013, IEEE Transactions on Industrial Informatics.

[22]  Okyay Kaynak,et al.  Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[23]  Bin Zhang,et al.  Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering , 2011, IEEE Transactions on Industrial Electronics.

[24]  P. Rousseeuw,et al.  Alternatives to the Median Absolute Deviation , 1993 .