scholarly journals Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection

2015 ◽  
Vol 30 ◽  
pp. 90-103 ◽  
Author(s):  
Anders Willersrud ◽  
Mogens Blanke ◽  
Lars Imsland ◽  
Alexey Pavlov
Keyword(s):  
Fault Diagnosis ◽  
Change Detection ◽  
Adaptive Observers ◽  
Statistical Change

scholarly journals Active Fault Diagnosis Based on Stochastic Tests

2008 ◽  
Vol 18 (4) ◽  
pp. 487-496 ◽  
Author(s):  
Niels Poulsen ◽  
Henrik Niemann
Keyword(s):  
Fault Diagnosis ◽  
Change Detection ◽  
Input Signal ◽  
Output Signal ◽  
Active Fault ◽  
Fast Change ◽  
Signal Injection ◽  
Cusum Method ◽  
Cusum Tests ◽  
Auxiliary Input

Active Fault Diagnosis Based on Stochastic TestsThe focus of this paper is on stochastic change detection applied in connection with active fault diagnosis (AFD). An auxiliary input signal is applied in AFD. This signal injection in the system will in general allow us to obtain a fast change detection/isolation by considering the output or an error output from the system. The classical cumulative sum (CUSUM) test will be modified with respect to the AFD approach applied. The CUSUM method will be altered such that it will be able to detect a change in the signature from the auxiliary input signal in an (error) output signal. It will be shown how it is possible to apply both the gain and the phase change of the output signal in CUSUM tests. The method is demonstrated using an example.

scholarly journals Diagnosis of CO Pollution in HTPEM Fuel Cell using Statistical Change Detection

2015 ◽  
Vol 48 (21) ◽  
pp. 547-553 ◽  
Author(s):  
Christian Jeppesen ◽  
Mogens Blanke ◽  
Fan Zhou ◽  
Søren Juhl Andreasen
Keyword(s):  
Fuel Cell ◽  
Change Detection ◽  
Statistical Change

Observer-Based Fault Diagnosis of Satellite Systems Subject to Time-Varying Thruster Faults

2006 ◽  
Vol 129 (3) ◽  
pp. 352-356 ◽  
Author(s):  
Wen Chen ◽  
Mehrdad Saif
Keyword(s):  
Fault Diagnosis ◽  
Estimation Error ◽  
Fault Estimation ◽  
Time Varying ◽  
Satellite Systems ◽  
Adaptive Observers ◽  
Error Dynamics ◽  
The Stability ◽  
Estimation Of Time ◽  
Identification Strategy

This paper presents a novel fault diagnosis approach in satellite systems for identifying time-varying thruster faults. To overcome the difficulty in identifying time-varying thruster faults by adaptive observers, an iterative learning observer (ILO) is designed to achieve estimation of time-varying faults. The proposed ILO-based fault-identification strategy uses a learning mechanism to perform fault estimation instead of using integrators that are commonly used in classical adaptive observers. The stability of estimation-error dynamics is established and proved. An illustrative example clearly shows that time-varying thruster faults can be accurately identified.

scholarly journals Ice Condition Assessment Using Onboard Accelerometers and Statistical Change Detection

2020 ◽  
Vol 45 (3) ◽  
pp. 898-914
Author(s):  
Hans-Martin Heyn ◽  
Mogens Blanke ◽  
Roger Skjetne
Keyword(s):  
Change Detection ◽  
Condition Assessment ◽  
Statistical Change

scholarly journals Diagnosis of UAV Pitot Tube Defects Using Statistical Change Detection

2010 ◽  
Vol 43 (16) ◽  
pp. 485-490 ◽  
Author(s):  
Søren Hansen ◽  
Mogens Blanke ◽  
Jens Adrian
Keyword(s):  
Change Detection ◽  
Pitot Tube ◽  
Statistical Change

The long-term hydrology of East Africa’s water tower: statistical change detection in the watersheds of the Abbay Basin

2013 ◽  
Vol 14 (1) ◽  
pp. 321-331 ◽  
Author(s):  
Solomon Gebreyohannis Gebrehiwot ◽  
Annemieke I. Gärdenäs ◽  
Woldeamlak Bewket ◽  
Jan Seibert ◽  
Ulrik Ilstedt ◽  
...  
Keyword(s):  
Change Detection ◽  
Statistical Change
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