Incipient multiple fault diagnosis in real time with application to large-scale systems

1994 ◽  
Vol 41 (4) ◽  
pp. 1692-1703 ◽  
Author(s):  
Hak-yeong Chung ◽  
Z. Bien ◽  
Joo-hyun Park ◽  
Poong-hyun Seong
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Large Scale ◽  
Multiple Fault ◽  
Large Scale Systems ◽  
Multiple Fault Diagnosis

SDG multiple fault diagnosis by real-time inverse inference

2005 ◽  
Vol 87 (2) ◽  
pp. 173-189 ◽  
Author(s):  
Zhao-qian Zhang ◽  
Chong-guang Wu ◽  
Bei-ke Zhang ◽  
Tao Xia ◽  
An-feng Li
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Multiple Fault ◽  
Multiple Fault Diagnosis

Dynamic Set-Covering for Real-Time Multiple Fault Diagnosis

2008 ◽  
Author(s):  
Anuradha Kodali ◽  
Satnam Singh ◽  
Kihoon Choi ◽  
Krishna Pattipati ◽  
Setu Madhavi Namburu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Real Time ◽  
Set Covering ◽  
Multiple Fault ◽  
Multiple Fault Diagnosis

Fault detection algorithms for real-time diagnosis in large-scale systems

2001 ◽  
Author(s):  
Thiagalingam Kirubarajan ◽  
Venkatesh N. Malepati ◽  
Somnath Deb ◽  
Jie Ying
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Large Scale ◽  
Large Scale Systems ◽  
Detection Algorithms ◽  
Time Diagnosis

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

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.

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