Combination of KPCA and causality analysis for root cause diagnosis of industrial process fault

2017 ◽  
Vol 95 (8) ◽  
pp. 1497-1509 ◽  
Author(s):  
Hasssan Gharahbagheri ◽  
Syed Imtiaz ◽  
Faisal Khan
Keyword(s):  
Industrial Process ◽  
Causality Analysis ◽  
Root Cause

scholarly journals Causality analysis for root cause diagnosis in Fluid Catalytic Cracking unit

2015 ◽  
Vol 48 (21) ◽  
pp. 838-843 ◽  
Author(s):  
H. Gharahbagheri ◽  
S. Imtiaz ◽  
F. Khan ◽  
S. Ahmed
Keyword(s):  
Catalytic Cracking ◽  
Fluid Catalytic Cracking ◽  
Causality Analysis ◽  
Root Cause

scholarly journals Signed directed graph based modeling and its validation from process knowledge and process data

2012 ◽  
Vol 22 (1) ◽  
pp. 41-53 ◽  
Author(s):  
Fan Yang ◽  
Sirish Shah ◽  
Deyun Xiao
Keyword(s):  
Directed Graph ◽  
Information Transfer ◽  
Graphical Model ◽  
Industrial Process ◽  
Process Data ◽  
Process Knowledge ◽  
Root Cause ◽  
Signed Directed Graph ◽  
Cross Correlation Analysis ◽  
Propagation Analysis

Signed directed graph based modeling and its validation from process knowledge and process data This paper is concerned with the fusion of information from process data and process connectivity and its subsequent use in fault diagnosis and process hazard assessment. The Signed Directed Graph (SDG), as a graphical model for capturing process topology and connectivity to show the causal relationships between process variables by material and information paths, has been widely used in root cause and hazard propagation analysis. An SDG is usually built based on process knowledge as described by piping and instrumentation diagrams. This is a complex and experience-dependent task, and therefore the resulting SDG should be validated by process data before being used for analysis. This paper introduces two validation methods. One is based on cross-correlation analysis of process data with assumed time delays, while the other is based on transfer entropy, where the correlation coefficient between two variables or the information transfer from one variable to another can be computed to validate the corresponding paths in SDGs. In addition to this, the relationship captured by data-based methods should also be validated by process knowledge to confirm its causality. This knowledge can be realized by checking the reachability or the influence of one variable on another based on the corresponding SDG which is the basis of causality. A case study of an industrial process is presented to illustrate the application of the proposed methods.

scholarly journals Causality analysis in process control based on denoising and periodicity-removing CCM

2020 ◽  
Vol 1 (1) ◽  
pp. 25-41
Author(s):  
Qiming Chen ◽  
Xinyi Fei ◽  
Lie Xie ◽  
Dongliu Li ◽  
Qibing Wang
Keyword(s):  
Control System ◽  
Process Control ◽  
Singular Spectrum Analysis ◽  
Process Control System ◽  
Fluctuation Analysis ◽  
Causality Analysis ◽  
Industrial Case Study ◽  
Content Type ◽  
Root Cause ◽  
Convergent Cross Mapping

Purpose1. To improve the causality analysis performance, a novel causality detector based on time-delayed convergent cross mapping (TD-CCM) is proposed in this work. 2. Identify the root cause of plant-wide oscillations in process control system.Design/methodology/approachA novel causality analysis framework is proposed based on denoising and periodicity-removing TD-CCM (time-delayed convergent cross mapping). We first point out that noise and periodicity have adverse effects on causality detection. Then, the empirical mode decomposition (EMD) and detrended fluctuation analysis (FDA) are combined to achieve denoising. The periodicities are effectively removed through singular spectrum analysis (SSA). Following, the TD-CCM can accurately capture the causalities and locate the root cause by analyzing the filtered signals.Findings1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. Simulation studies show that the proposed method is able to improve the causality analysis performance. 3. Industrial case study shows the proposed method can be used to analyze the root cause of plant-wide oscillations in process control system.Originality/value1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. The influences of noise and periodicity on causality analysis are investigated. 3. Simulations and industrial case shows that the proposed method can improve the causality analysis performance and can be used to identify the root cause of plant-wide oscillations in process control system.

scholarly journals Root Cause Diagnosis of Process Faults Using Conditional Granger Causality Analysis and Maximum Spanning Tree

2018 ◽  
Vol 51 (18) ◽  
pp. 381-386 ◽  
Author(s):  
Han-Sheng Chen ◽  
Zhengbing Yan ◽  
Xuelei Zhang ◽  
Yi Liu ◽  
Yuan Yao
Keyword(s):  
Granger Causality ◽  
Spanning Tree ◽  
Causality Analysis ◽  
Root Cause ◽  
Granger Causality Analysis ◽  
Maximum Spanning Tree

scholarly journals Root Cause Diagnosis of Oscillation-Type Plant Faults Using Nonlinear Causality Analysis

2017 ◽  
Vol 50 (1) ◽  
pp. 13898-13903 ◽  
Author(s):  
Han-Sheng Chen ◽  
Chunhui Zhao ◽  
Zhengbing Yan ◽  
Yuan Yao
Keyword(s):  
Causality Analysis ◽  
Root Cause ◽  
Oscillation Type
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