scholarly journals Sequential Diagnosis of K-Out-Of-N Systems with Imperfect Tests

2013 ◽  
Author(s):  
Wenchao Wei ◽  
Fabrice Nobibon Talla ◽  
R. Leus
Keyword(s):  
Sequential Diagnosis

scholarly journals On sequential diagnosis of multiprocessor systems

2005 ◽  
Vol 146 (3) ◽  
pp. 311-342 ◽  
Author(s):  
T. Yamada ◽  
T. Ohtsuka ◽  
A. Watanabe ◽  
S. Ueno
Keyword(s):  
Multiprocessor Systems ◽  
Sequential Diagnosis

scholarly journals Experience with a model of sequential diagnosis

1968 ◽  
Vol 1 (5) ◽  
pp. 490-507 ◽  
Author(s):  
G.Anthony Gorry ◽  
G.Octo Barnett
Keyword(s):  
Sequential Diagnosis

High diagnosability of a sequential diagnosis algorithm in hypercubes under the PMC model

2011 ◽  
Vol 61 (3) ◽  
pp. 1116-1134 ◽  
Author(s):  
Chin-Lin Kuo ◽  
Ming-Jeng Yang ◽  
Yao-Ming Chang ◽  
Yao-Ming Yeh
Keyword(s):  
Pmc Model ◽  
Diagnosis Algorithm ◽  
Sequential Diagnosis

Sequential Diagnosis with Asymmetrical Tests

1998 ◽  
Vol 41 (3) ◽  
pp. 163-170 ◽  
Author(s):  
A. Biasizzo
Keyword(s):  
Sequential Diagnosis

scholarly journals A Model-Based Active Testing Approach to Sequential Diagnosis

2010 ◽  
Vol 39 ◽  
pp. 301-334 ◽  
Author(s):  
A. Feldman ◽  
G. Provan ◽  
A. Van Gemund
Keyword(s):  
Pattern Generation ◽  
Model Based ◽  
Trade Offs ◽  
Additional Input ◽  
Active Testing ◽  
Testing Algorithms ◽  
Input Variables ◽  
Testing Algorithm ◽  
Sequential Diagnosis ◽  
Test Vectors

Model-based diagnostic reasoning often leads to a large number of diagnostic hypotheses. The set of diagnoses can be reduced by taking into account extra observations (passive monitoring), measuring additional variables (probing) or executing additional tests (sequential diagnosis/test sequencing). In this paper we combine the above approaches with techniques from Automated Test Pattern Generation (ATPG) and Model-Based Diagnosis (MBD) into a framework called FRACTAL (FRamework for ACtive Testing ALgorithms). Apart from the inputs and outputs that connect a system to its environment, in active testing we consider additional input variables to which a sequence of test vectors can be supplied. We address the computationally hard problem of computing optimal control assignments (as defined in FRACTAL) in terms of a greedy approximation algorithm called FRACTAL-G. We compare the decrease in the number of remaining minimal cardinality diagnoses of FRACTAL-G to that of two more FRACTAL algorithms: FRACTAL-ATPG and FRACTAL-P. FRACTAL-ATPG is based on ATPG and sequential diagnosis while FRACTAL-P is based on probing and, although not an active testing algorithm, provides a baseline for comparing the lower bound on the number of reachable diagnoses for the FRACTAL algorithms. We empirically evaluate the trade-offs of the three FRACTAL algorithms by performing extensive experimentation on the ISCAS85/74XXX benchmark of combinational circuits.

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