Computationally efficient algorithms for multiple fault diagnosis in large graph-based systems

2003 ◽  
Vol 33 (1) ◽  
pp. 73-85 ◽  
Author(s):  
Fang Tu ◽  
K.R. Pattipati ◽  
S. Deb ◽  
V.N. Malepati
Keyword(s):  
Fault Diagnosis ◽  
Efficient Algorithms ◽  
Computationally Efficient ◽  
Multiple Fault ◽  
Large Graph ◽  
Multiple Fault Diagnosis ◽  
Computationally Efficient Algorithms

Multiple fault diagnosis in digital microfluidic biochips

2006 ◽  
Vol 2 (4) ◽  
pp. 262-276 ◽  
Author(s):  
Daniel Davids ◽  
Siddhartha Datta ◽  
Arindam Mukherjee ◽  
Bharat Joshi ◽  
Arun Ravindran
Keyword(s):  
Fault Diagnosis ◽  
Multiple Fault ◽  
Multiple Fault Diagnosis ◽  
Microfluidic Biochips ◽  
Digital Microfluidic

Computationally efficient algorithms for location area planning in future cellular systems

2000 ◽  
Vol 23 (13) ◽  
pp. 1263-1280 ◽  
Author(s):  
P. Demestichas ◽  
N. Georgantas ◽  
E. Tzifa ◽  
V. Demesticha ◽  
M. Striki ◽  
...  
Keyword(s):  
Efficient Algorithms ◽  
Cellular Systems ◽  
Computationally Efficient ◽  
Location Area ◽  
Computationally Efficient Algorithms ◽  
Area Planning

scholarly journals Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4424
Author(s):  
Udeme Inyang ◽  
Ivan Petrunin ◽  
Ian Jennions
Keyword(s):  
Fault Diagnosis ◽  
Health Management ◽  
Network Models ◽  
Health Condition ◽  
Limited Attention ◽  
Neural Network Models ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Multiple Fault Diagnosis ◽  
Non Gaussian

Bearings are critical components found in most rotating machinery; their health condition is of immense importance to many industries. The varied conditions and environments in which bearings operate make them prone to single and multiple faults. Widespread interest in the improvements of single fault diagnosis meant limited attention was spent on multiple fault diagnosis. However, multiple fault diagnosis poses extra challenges due to the submergence of the weak fault by the strong fault, presence of non-Gaussian noise, coupling of the frequency components, etc. A number of existing convolutional neural network models operate on a distinct feature that is not enough to assure reliable results in the presence of these challenges. In this paper, extended feature sets in three homogenous deep learning models are used for multiple fault diagnosis. This ensures a measure of diversity is introduced to the health management dataset to obtain complementary solutions from the models. The outputs of the models are fused through blending ensemble learning. Experiments using vibration datasets based on bearing multiple faults show an accuracy of 98.54%, with an improvement of 2.74% in the overall effectiveness over the single models. Compared with other technologies, the results show that this approach provides an improved generalized diagnostic capability.

scholarly journals A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer Models

2008 ◽  
Vol 18 (4) ◽  
pp. 465-476 ◽  
Author(s):  
Antoni Ligęza ◽  
Jan Kościelny
Keyword(s):  
Fault Diagnosis ◽  
Simultaneous Occurrence ◽  
Diagnostic Model ◽  
Rule Based ◽  
New Approach ◽  
Multiple Fault ◽  
Multiple Faults ◽  
Model Based ◽  
Multiple Fault Diagnosis ◽  
System Variables

A New Approach to Multiple Fault Diagnosis: A Combination of Diagnostic Matrices, Graphs, Algebraic and Rule-Based Models. The Case of Two-Layer ModelsThe diagnosis of multiple faults is significantly more difficult than singular fault diagnosis. However, in realistic industrial systems the possibility of simultaneous occurrence of multiple faults must be taken into account. This paper investigates some of the limitations of the diagnostic model based on the simple binary diagnostic matrix in the case of multiple faults. Several possible interpretations of the diagnostic matrix with rule-based systems are provided and analyzed. A proposal of an extension of the basic, single-level model based on diagnostic matrices to a two-level one, founded on causal analysis and incorporating an OR and an AND matrix is put forward. An approach to the diagnosis of multiple faults based on inconsistency analysis is outlined, and a refinement procedure using a qualitative model of dependencies among system variables is sketched out.

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