Sensor Selection for Fault Detection and Isolation in Structurally Reconfigurable Systems

2018 ◽  
Author(s):  
Eeshan Deosthale ◽  
Daniel Jung ◽  
Qadeer Ahmed
Keyword(s):  
Fault Detection ◽  
Sensor Selection ◽  
Fault Detection And Isolation ◽  
Reconfigurable Systems ◽  
Selection For

Discrete Fault Diagnosis of Structurally Reconfigurable Systems

2021 ◽  
Author(s):  
Eeshan Deosthale ◽  
Daniel Jung ◽  
Qadeer Ahmed
Keyword(s):  
Fault Diagnosis ◽  
Structural Analysis ◽  
Fault Detection ◽  
Hybrid Systems ◽  
Fault Tolerant ◽  
Automatic Transmission ◽  
Sensor Selection ◽  
Reconfigurable Systems ◽  
Mode Identification ◽  
Selection For

Abstract Fault diagnosis of a certain class of hybrid systems referred to as Structurally Reconfigurable (SR) systems is complicated. This is because SR systems tend to switch their configuration which may or may not be faulty. It is important to identify the mode of the SR system along with the corresponding fault if any, in order to facilitate a fault tolerant action. This paper combines discrete fault diagnosis with mode identification for SR systems to achieve two main objectives: Sensor selection for fault detection, isolation and mode identification, and residual selection for mode identification. The framework is built using a structural analysis based approach to meet these objectives. This framework is demonstrated for a 10-Speed Automatic Transmission, which is an illustrative example of SR systems.

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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