Distributed sensor system for fault detection and isolation in multistage manufacturing systems

2006 ◽  
Vol 25 (4) ◽  
pp. 182 ◽  
Author(s):  
Du Shi Chang ◽  
Xi Li Feng ◽  
Shi Jian Jun
Keyword(s):  
Fault Detection ◽  
Manufacturing Systems ◽  
Fault Detection And Isolation ◽  
Sensor System ◽  
Distributed Sensor ◽  
Multistage Manufacturing

Chemical static equipment commonly used sensor fault detection and isolation methods

2021 ◽  
pp. 1-11
Author(s):  
Fang Peng ◽  
Wei Yang
Keyword(s):  
Fault Detection ◽  
Stable State ◽  
Fault Detection And Isolation ◽  
System Model ◽  
Sensor System ◽  
Support Vector ◽  
Sensor Fault ◽  
Isolation Method ◽  
Isolation Methods ◽  
Sensor Fault Detection

This paper conducts a study on the faults of common sensors involved in chemical static equipment. Firstly, the types and characteristics of commonly used sensors of chemical static equipment are analyzed, and the characteristics of sensor output signal changes are summarized with the working characteristics of chemical equipment. Then the faults of static equipment sensors are classified and a fault model is established. Through the study of sensor fault detection and isolation methods at home and abroad, the overall scheme of sensor system fault detection and isolation combining single sensor fault detection and isolation method and multi-sensor fault detection and isolation method is proposed. According to the characteristics that chemical processes are generally in a dynamic and stable state and there is a certain correlation between the signals of each detection point in the equipment, a sensor system model is established by using the correlation of multiple sensors on the equipment, and when a sensor in the sensor system fails, the system model changes beyond the threshold value, and a different form of residual generation is used to determine which sensor is faulty and achieve the detection and isolation of faulty sensors. The fault detection method is simulated and studied by using relevant software, combined with a support vector machine and neural network toolbox. The results show that the method proposed in this paper can effectively complete the fault detection and isolation of sensors commonly used in chemical static equipment. The accuracy and reliability of the prediction model are high.

Distributed sensor fault detection and isolation for multimachine power systems

2014 ◽  
Vol 24 (8-9) ◽  
pp. 1403-1430 ◽  
Author(s):  
Qi Zhang ◽  
Xiaodong Zhang ◽  
Marios M. Polycarpou ◽  
Thomas Parisini
Keyword(s):  
Fault Detection ◽  
Power Systems ◽  
Fault Detection And Isolation ◽  
Sensor Fault ◽  
Distributed Sensor ◽  
Sensor Fault Detection

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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