Fault Detection and Isolation for Descriptor Systems

2000 ◽  
pp. 115-144 ◽  
Author(s):  
Ming Hou
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Descriptor Systems

scholarly journals ON OBSERVER-BASED FAULT DETECTION AND ISOLATION FOR CONTINUOUS LINEAR DESCRIPTOR SYSTEMS 1

2006 ◽  
Vol 39 (9) ◽  
pp. 315-320
Author(s):  
V.G. Silva ◽  
G. Garcia ◽  
S. Tarbouriech ◽  
E.B. Castelan
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Descriptor Systems ◽  
Continuous Linear

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

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