Multiple Fault Isolation Through Parameter Estimation

2008 ◽  
pp. 373-422
Keyword(s):  
Parameter Estimation ◽  
Fault Isolation ◽  
Multiple Fault

Magnetic Current Imaging Power Short Localization

2015 ◽  
Author(s):  
J. Gaudestad ◽  
F. Rusli ◽  
A. Orozco ◽  
M.C. Pun
Keyword(s):  
Thermal Imaging ◽  
Flip Chip ◽  
Optical Microscope ◽  
Fault Isolation ◽  
Magnetic Current ◽  
Multiple Fault ◽  
Defect Location ◽  
Isolation Techniques ◽  
Chip Sample

Abstract A Flip Chip sample failed short between power and ground. The reference unit had 418Ω and the failed unit with the short had 16.4Ω. Multiple fault isolation techniques were used in an attempt to find the failure with thermal imaging and Magnetic Current Imaging being the only techniques capable of localizing the defect. To physically verify the defect location, the die was detached from the substrate and a die cracked was seen using a visible optical microscope.

Method of Multiple Fault Isolation in Large Scale Systems

2012 ◽  
Vol 20 (5) ◽  
pp. 1302-1310 ◽  
Author(s):  
J. M. Koscielny ◽  
Michał Bartys ◽  
M. Syfert
Keyword(s):  
Large Scale ◽  
Fault Isolation ◽  
Multiple Fault ◽  
Large Scale Systems

Fault Localization Analysis for Multiple Fault Diagnosis in Multi-Station Assembly Systems

2007 ◽  
Author(s):  
Posheng Tsai ◽  
Kamal Mannar ◽  
Darek Ceglarek
Keyword(s):  
Manufacturing Systems ◽  
Large Scale ◽  
Fault Localization ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Isolation ◽  
Data Driven ◽  
Multiple Fault ◽  
Critical Measure ◽  
The Impact

Modern large scale multi-station manufacturing systems require effective variation reduction to improve the final assembly dimensional quality. One critical measure is to diagnose the fault in the process using knowledge-based root cause identification, which can be very challenging due to the complexity of the system. The paper investigates the need of data-driven fault localization to enhance the diagnosability within the context of multiple-fault scenario(s) in multi-station assembly processes where multivariate measurements are used. The paper proposes three types of fault-signal transmission in assembly system and illustrates the nature of structured noise. Moreover, the impact of structured noise on the diagnosability is illustrated on two major fault isolation methods, namely, Principal Component Analysis and Independent Component Analysis. We then propose to use data-driven fault localization to reduce the structured noise effect and enhance the diagnosability. A simulation case study based on automotive panel assembly model is provided to illustrate the impact of structured noise and the need for data-driven localization.

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