Adaptive LII-RMPLS based data-driven process monitoring scheme for quality-relevant fault detection

2022 ◽  
pp. 1-12
Author(s):  
Xiaowei Feng ◽  
Xiangyu Kong ◽  
Boyang Du ◽  
Jiayu Luo
Keyword(s):  
Fault Detection ◽  
Process Monitoring ◽  
Data Driven ◽  
Monitoring Scheme

scholarly journals NARX NETWORK BASED DATA-DRIVEN ALGORITHM FOR DETECTION OF TRAY FAULTS IN NONLINEAR DYNAMIC DISTILLATION COLUMN

2020 ◽  
Vol 82 (5) ◽  
Author(s):  
Syed Ali Ammar Taqvi ◽  
Haslinda Zabiri ◽  
Lemma Dendena Tufa ◽  
Fahim Uddin ◽  
Syeda Anmol Fatima ◽  
...  
Keyword(s):  
Fault Detection ◽  
Process Monitoring ◽  
Nonlinear Dynamic ◽  
Distillation Column ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Detection Methods ◽  
Maintenance Cost ◽  
Monitoring And Control ◽  
Process Industries

Efficient monitoring of highly complex process industries is essential for better management, safer operations and high-quality production. Timely detection of various faults helps to improve the performance of the complex industries, prevent various unfavorable consequences and reduce the maintenance cost. Fault Detection and Diagnosis (FDD) for process monitoring and control has been an active field of research for the past two decades. Distillation columns are inherently nonlinear, and thus to have an accurate and robust performance, the fault detection methods should be based on nonlinear dynamic methods. The paper presents a robust data-driven fault detection approach for realistic tray upsets in the distillation column. The detection of tray faults in the distillation column is conducted by Nonlinear AutoRegressive with eXogenous Input (NARX) network with Tapped Delay Lines (TDL). Aspen Plus® Dynamic simulation has been used to generate normal and faulty datasets. The study shows that the proposed method can be used for the detection of tray faults in distillation column for dynamic process monitoring. The performance of the proposed method has been evaluated by the Missed Detection Rate (MDR) and the Detection Delay (DD).

scholarly journals Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework

2020 ◽  
Author(s):  
Muhammad Nawaz ◽  
Abdulhalim Shah Maulud ◽  
Haslinda Zabiri ◽  
Syed Ali Ammar Taqvi ◽  
Alamin Idris
Keyword(s):  
Fault Detection ◽  
Process Monitoring
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