Multiple Fault Diagnosis of Aeroengine Control System Based on Autoassociative Neural Network

2020 ◽  
Author(s):  
Huihui Li ◽  
Linfeng Gou ◽  
Huacong Li ◽  
Xiaojian Xing ◽  
Jiang Yang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Multiple Fault ◽  
Multiple Fault Diagnosis ◽  
Autoassociative Neural Network

Performance-based fault diagnosis of a gas turbine engine using an integrated support vector machine and artificial neural network method

2018 ◽  
Vol 233 (6) ◽  
pp. 786-802 ◽  
Author(s):  
Amare Desalegn Fentaye ◽  
Syed Ihtsham Ul-Haq Gilani ◽  
Aklilu Tesfamichael Baheta ◽  
Yi-Guang Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Gas Turbine Engine ◽  
Support Vector ◽  
Multiple Fault ◽  
Turbine Engine ◽  
Multiple Fault Diagnosis

An effective and reliable gas path diagnostic method that could be used to detect, isolate, and identify gas turbine degradations is crucial in a gas turbine condition-based maintenance. In this paper, we proposed a new combined technique of artificial neural network and support vector machine for a two-shaft industrial gas turbine engine gas path diagnostics. To this end, an autoassociative neural network is used as a preprocessor to minimize noise and generate necessary features, a nested support vector machine to classify gas path faults, and a multilayer perceptron to assess the magnitude of the faults. The necessary data to train and test the method are obtained from a performance model of the case engine under steady-state operating conditions. The test results indicate that the proposed method can diagnose both single- and multiple-component faults successfully and shows a clear advantage over some other methods in terms of multiple fault diagnosis. Moreover, 5-8 sets of measurements have been used to assess the prediction accuracy, and only a 2.3% difference was observed. This result indicates that the proposed method can be used for multiple fault diagnosis of gas turbines with limited measurements.

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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