Actuator fault detection identification and isolation via high-gain observer for an olive oil waste esterification in a Semi Continuous Stirred Tank Reactor

2015 ◽  
Author(s):  
Amira Abdelkader ◽  
Moez Boussada ◽  
Koffi Fiaty ◽  
Hassan Hammouri ◽  
Ahmed Said Nouri
Keyword(s):  
Fault Detection ◽  
Olive Oil ◽  
High Gain ◽  
Stirred Tank ◽  
Actuator Fault ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
High Gain Observer ◽  
Continuous Stirred Tank

scholarly journals Unknown input observer design for fault detection and diagnosis in a continuous stirred-tank reactor

2019 ◽  
Vol 90 ◽  
pp. 02004
Author(s):  
Ali H. Al-Shatri ◽  
Ahmad Arshad ◽  
Oladokun Olagoke ◽  
Bemgba B. Nyakuma
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Observer Design ◽  
Stirred Tank ◽  
Unknown Input ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Continuous Stirred Tank ◽  
Detection And Diagnosis

Early and accurate fault detection and diagnosis (FDD) minimises downtime, increases the safety and reliability of plant operation, and reduces manufacturing costs. This paper presents a robust FDD strategy for a nonlinear system using a bank of unknown input observers (UIO). The approach is based on structure residual generation that provides not only decoupling of faults from model uncertainties and unknown input disturbance but also decoupling the effect of a fault from the effects of other faults. The generated residual was evaluated through the statistical threshold to avoid fault missing or false alarm. The performance of the robust FDD scheme was assessed by some sensor fault scenarios created in a continuous stirred-tank reactor (CSTR). The simulation result showed the effectiveness of the proposed approach.

scholarly journals Fault Detection Approach Based on Weighted Principal Component Analysis Applied to Continuous Stirred Tank Reactor

2015 ◽  
Vol 9 (1) ◽  
pp. 966-972 ◽  
Author(s):  
Shanmao Gu ◽  
Yunlong Liu ◽  
Ni Zhang ◽  
De Du
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Principal Component ◽  
Stirred Tank ◽  
Time Varying ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Detection Approach ◽  
Continuous Stirred Tank

Fault detection approach based on principal component analysis (PCA) may perform not well when the process is time-varying, because it can cause unfavorable influence on feature extraction. To solve this problem, a modified PCA which considering variance maximization is proposed, referred to as weighted PCA (WPCA). WPCA can obtain the slow features information of observed data in time-varying system. The monitoring statistical indices are based on WPCA model and their confidence limits are computed by kernel density estimation (KDE). A simulation example on continuous stirred tank reactor (CSTR) show that the proposed method achieves better performance from the perspective of both fault detection rate and fault detection time than conventional PCA model.

scholarly journals Continuous Stirred Tank Reactor Fault Detection Using Higher Degree Cubature Kalman Filter

2019 ◽  
Vol 18 (3-2) ◽  
pp. 47-50
Author(s):  
Muhammad Naguib Ahmad Nazri ◽  
Zool Hilmi Ismail ◽  
Rubiyah Yusof
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Computational Cost ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Current State ◽  
Cubature Kalman Filter ◽  
Continuous Stirred Tank

Continuous Stirred Tank Reactor (CSTR) plays a major role in chemical industries, it ensures the process of mixing reactants according to the attended specification to produce a specific output. It is a complex process that usually represent with nonlinear model for benchmarking. Any abnormality, disturbance and unusual condition can easily interrupt the operations, especially fault. And this problem need to detect and rectify as soon as possible.  A good knowledge based fault detection using available model require a good error residual between the measurement and the estimated state. Kalman filter is an example of a good estimator, and has been exploited in many researches to detect fault. In this paper, Higher degree Cubature Kalman Filter (HDCKF) is proposed as a method for fault detection by estimation the current state. Cubature Kalman filter (CKF) is an extension of the Kalman filter with the main purpose is to estimate process and measurement state with high nonlinearities. It is based on spherical radial integration to estimate current state by generating cubature points with specific value. Conventional CKF use 3rd degree spherical and 3rd degree radial, here we implement Higher Degree CKF (HDCKF) to have better accuracy as compared to conventional CKF. High accuracy is required to ensure no false alarm is detected and furthermore good computational cost will improve its detection. Finally, a numerical example of CSTR fault detection using HDCKF is presented. Implementation of HDCKF for fault detection is compared with other filter to show effective results.

scholarly journals Fault detection and diagnosis for continuous stirred tank reactor using neural network

1970 ◽  
Vol 6 (2) ◽  
pp. 66-74 ◽  
Author(s):  
Ribhan Zafira Abdul Rahman ◽  
Azura Che Soh ◽  
Noor Fadzlina Binti Muhammad
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Stirred Tank ◽  
Continuous Stirred Tank Reactor ◽  
Stirred Tank Reactor ◽  
Tank Reactor ◽  
Engineering And Technology ◽  
Continuous Stirred Tank ◽  
Detection And Diagnosis

The paper focuses on the application of neural network techniques in fault detection and diagnosis. The objective of this paper is to detect and diagnose the faults to a continuous stirred tank reactor (CSTR). Fault detection is performed by using the error signals, where when error signal is zero or nearly zero, the system is in normal condition, and when the fault occurs, error signals should distinctively diverge from zero. The fault diagnosis is performed by identifying the amplitude error of the CSTR output error. Keywords: Fault Detection and Diagnosis; Neural Network; CSTR  DOI: 10.3126/kuset.v6i2.4014Kathmandu University Journal of Science, Engineering and Technology Vol.6. No II, November, 2010, pp.66-74

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