Independent Component Analysis and Comparative Analysis of Oil and Acoustic Emission Technique for Condition Monitoring of Diesel Engines

Author(s):  
P Rajesh Kumar ◽  
D. Lakshmanan ◽  
Tom Page
2013 ◽  
Vol 373-375 ◽  
pp. 677-680
Author(s):  
Wei Li ◽  
Yu Li Gong ◽  
Yang Yu

Based on the characteristics of the acoustic emission (AE) signals from low carbon steel pitting corrosion, a new extraction method was proposed with wavelet transformation and independent component analysis. The experiment result shows that the new method can overcome the influence induced by the uncertainty of the independent source of low carbon steel pitting corrosion and good extraction result can be achieved.


2013 ◽  
Vol 860-863 ◽  
pp. 1801-1806
Author(s):  
Yue Zhao ◽  
Feng Qi Si ◽  
Zhi Gao Xu

Traditional condition monitoring methods are not suitable for the nonlinear operation parameters and time-variable operation conditions. We propose an independent component analysis method based on sliding window statistics (SSWICA). This method uses statistics in sliding windows of parameters as input samples, then uses a N-step forward sliding window ICA method to modeling. Then we monitor the operating state of the equipments by observing whether the SPE index of real-time parameters exceeds the control limits. SSWICA is applied to condition monitoring of condenser in 600MW unit, comparing with traditional ICA monitoring methods based on sliding window. The results show SSWICA can accurately reflect current operating state and related changes of condensers state parameters, recognize steady, unsteady and fault conditions effectively. It is valuable for engineering practice and suitable for the application to equipments condition monitoring in power plant.


2013 ◽  
Author(s):  
Katarzyna Bizon ◽  
Gaetano Continillo ◽  
Simone Lombardi ◽  
Ezio Mancaruso ◽  
Paolo Sementa ◽  
...  

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