scholarly journals Automatic machinery fault detection via using distributed sensor information

2021 ◽  
Author(s):  
Hongbo Chen ◽  
Ying Huo ◽  
Yudong Xu
Keyword(s):  
Fault Detection ◽  
Distributed Sensor ◽  
Sensor Information ◽  
Automatic Machinery

A distributed sensor-fault detection and diagnosis framework using machine learning

2021 ◽  
Vol 547 ◽  
pp. 777-796
Author(s):  
Sana Ullah Jan ◽  
Young Doo Lee ◽  
In Soo Koo
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Sensor Fault ◽  
Distributed Sensor ◽  
Sensor Fault Detection ◽  
Detection And Diagnosis

Fault detection and isolation for sensor information fusion Kalman filter

2007 ◽  
Author(s):  
Ali Okatan ◽  
Chingiz Hajiyev ◽  
Ulviyya Hajiyeva
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Information Fusion ◽  
Fault Detection And Isolation ◽  
Sensor Information

scholarly journals Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1440
Author(s):  
Jianping Wu ◽  
Bin Jiang ◽  
Hongtian Chen ◽  
Jianwei Liu
Keyword(s):  
Fault Detection ◽  
Information Fusion ◽  
High Speed ◽  
Euclidean Distance ◽  
Manifold Regularization ◽  
Fusion System ◽  
Electrical Drive ◽  
High Speed Trains ◽  
Sensor Information ◽  
Drive Systems

Electrical drive systems play an increasingly important role in high-speed trains. The whole system is equipped with sensors that support complicated information fusion, which means the performance around this system ought to be monitored especially during incipient changes. In such situation, it is crucial to distinguish faulty state from observed normal state because of the dire consequences closed-loop faults might bring. In this research, an optimal neighborhood preserving embedding (NPE) method called multi-manifold regularization NPE (MMRNPE) is proposed to detect various faults in an electrical drive sensor information fusion system. By taking locality preserving embedding into account, the proposed methodology extends the united application of Euclidean distance of both designated points and paired points, which guarantees the access to both local and global sensor information. Meanwhile, this structure fuses several manifolds to extract their own features. In addition, parameters are allocated in diverse manifolds to seek an optimal combination of manifolds while entropy of information with parameters is also selected to avoid the overweight of single manifold. Moreover, an experimental test based on the platform was built to validate the MMRNPE approach and demonstrate the effectiveness of the fault detection. Results and observations show that the proposed MMRNPE offers a better fault detection representation in comparison with NPE.

Fault detection in sensor information fusion Kalman filter

2009 ◽  
Vol 63 (9) ◽  
pp. 762-768 ◽  
Author(s):  
Ali Okatan ◽  
Chingiz Hajiyev ◽  
Ulviyye Hajiyeva
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Information Fusion ◽  
Sensor Information
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