On-line hazard aversion and fault diagnosis in chemical processes: the digraph+fault-tree method

1988 ◽  
Vol 37 (2) ◽  
pp. 171-177 ◽  
Author(s):  
N.H. Ulerich ◽  
G.J. Powers
Keyword(s):  
Fault Diagnosis ◽  
Fault Tree ◽  
Chemical Processes ◽  
On Line ◽  
Tree Method

On-Line Fault Diagnosis Using Dynamic Fault Tree

2001 ◽  
Vol 34 (27) ◽  
pp. 167-172
Author(s):  
Sheng Yung Chang ◽  
Cheng Ren Lin ◽  
Chuei-Tin Chang ◽  
Shuh Woei Yu
Keyword(s):  
Fault Diagnosis ◽  
Fault Tree ◽  
Dynamic Fault Tree ◽  
On Line

scholarly journals Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zhuqing Bi ◽  
Chenming Li ◽  
Xujie Li ◽  
Hongmin Gao
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Historical Data ◽  
Complex Structure ◽  
Fault Tree ◽  
Uncertain Information ◽  
New Approach ◽  
Pumping Station ◽  
The One ◽  
Tree Method

According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN) is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient.

Non Linear State and Parameters Estimation in Chemical Processes: Analysis and Improvement of Three Estimation Structures Applied to a CSTR

2011 ◽  
Vol 9 (1) ◽  
Author(s):  
Héctor Botero ◽  
Hernán Álvarez
Keyword(s):  
Parameter Estimation ◽  
Chemical Process ◽  
Chemical Processes ◽  
The State ◽  
Convergence Speed ◽  
Parameters Estimation ◽  
State Variables ◽  
Input Output ◽  
On Line ◽  
Linear State

This paper proposes a new composite observer capable of estimating the states and unknown (or changing) parameters of a chemical process, using some input-output measurements, the phenomenological based model and other available knowledge about the process. The proposed composite observer contains a classic observer (CO) to estimate the state variables, an observer-based estimator (OBE) to obtain the actual values of the unknown or changing parameters needed to tune the CO, and an asymptotic observer (AO) to estimate the states needed as input to the OBE. The proposed structure was applied to a CSTR model with three state variables. With the proposed structure, the concentration of reactants and other CSTR parameters can be estimated on-line if the reactor and jacket temperatures are known. The procedure for the design of the proposed structure is simple and guarantees observer convergence. In addition, the convergence speed of state and parameter estimation can be adjusted independently.

Sign in / Sign up

Export Citation Format

Share Document