scholarly journals Complete Ensemble Empirical Mode Decomposition on FPGA for Condition Monitoring of Broken Bars in Induction Motors

Mathematics ◽  
2019 ◽  
Vol 7 (9) ◽  
pp. 783 ◽  
Author(s):  
Martin Valtierra-Rodriguez ◽  
Juan Amezquita-Sanchez ◽  
Arturo Garcia-Perez ◽  
David Camarena-Martinez
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Condition Monitoring ◽  
Induction Motors ◽  
A Priori ◽  
Digital Signal ◽  
Decomposition Methods ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Mode Decomposition

Empirical mode decomposition (EMD)-based methods are powerful digital signal processing techniques because they do not need a priori information of the target signal due to their intrinsic adaptive behavior. Moreover, they can deal with non-linear and non-stationary signals. This paper presents the field programmable gate array (FPGA) implementation for the complete ensemble empirical mode decomposition (CEEMD) method, which is applied to the condition monitoring of an induction motor. The CEEMD method is chosen since it overcomes the performance of EMD and EEMD (ensemble empirical mode decomposition) methods. As a first application of the proposed FPGA-based system, the proposal is used as a processing technique for feature extraction in order to detect and classify broken rotor bar faults in induction motors. In order to obtain a complete online monitoring system, the feature extraction and classification modules are also implemented on the FPGA. Results show that an average effectiveness of 96% is obtained during the fault detection.

Degradation Feature Extraction of Rolling Bearings Based on Optimal Ensemble Empirical Mode Decomposition and Improved Composite Spectrum Analysis

2018 ◽  
Author(s):  
Fengli Wang ◽  
Hua Chen
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Single Point ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Significant Feature ◽  
Vibration Signals ◽  
Composite Spectrum ◽  
Optimal Ensemble ◽  
Mode Decomposition

Rolling bearing is a key part of turbomachinery. The performance and reliability of the bearing is vital to the safe operation of turbomachinery. Therefore, degradation feature extraction of rolling bearing is important to prevent it from failure. During rolling bearing degradation, machine vibration can increase, and this may be used to predict the degradation. The vibration signals are however complicated and nonlinear, making it difficult to extract degradation features effectively. Here, a novel degradation feature extraction method based on optimal ensemble empirical mode decomposition (EEMD) and improved composite spectrum (CS) analysis is proposed. Firstly, because only a few IMFs are expected to contain the information related to bearing fault, EEMD is utilized to pre-process the vibration signals. An optimization method is designed for adaptively determining the appropriate EEMD parameters for the signal, so that the significant feature components of the faulty bearing can be extracted from the signal and separated from background noise and other irrelevant components to bearing faults. Then, Bayesian information criterion (BIC) and correlation kurtosis (CK) are employed to select the sensitive intrinsic mode function (IMF) components and obtain fault information effectively. Finally, an improved CS analysis algorithm is used to fuse the selected sensitive IMF components, and the CS entropy (CSE) is extracted as degradation feature. Experimental data on the test bearings with single point faults separately at the inner race and rolling element were studied to demonstrate the capabilities of the proposed method. The results show that it can assess the bearing degradation status and has good sensitivity and good consistency to the process of bearing degradation.

scholarly journals Application of ensemble empirical mode decomposition based on machine learning methodologies in forecasting monthly pan evaporation

2018 ◽  
Vol 50 (2) ◽  
pp. 498-516 ◽  
Author(s):  
Mohammad Rezaie-Balf ◽  
Ozgur Kisi ◽  
Lloyd H. C. Chua
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Support Vector ◽  
Pan Evaporation ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Testing Stage ◽  
Adaptive Time ◽  
Two Hybrid

Abstract Accurate prediction of pan evaporation (PE) is one of the crucial factors in water resources management and planning in agriculture. In this research, two hybrid models, self-adaptive time-frequency methodology, ensemble empirical mode decomposition (EEMD) coupled with support vector machine (EEMD-SVM) and EEMD model tree (EEMD-MT), were employed to forecast monthly PE. The EEMD-SVM and EEMD-MT were compared with single SVM and MT models in forecasting monthly PE, measured between 1975 and 2008, at Siirt and Diyarbakir stations in Turkey. The results were evaluated using four assessment criteria, Nash–Sutcliffe Efficiency (NSE), root mean square error (RMSE), performance index (PI), Willmott's index (WI), and Legates–McCabe's index (LMI). The EEMD-MT model respectively improved the accuracy of MT by 36 and 44.7% with respect to NSE and WI in the testing stage for the Siirt station. For the Diyarbakir station, the improvements in results were less spectacular, with improvements in NSE (1.7%) and WI (2.2%), respectively, in the testing stage. The overall results indicate that the proposed pre-processing technique is very promising for complex time series forecasting and further studies incorporating this technique are recommended.

scholarly journals Blade fault diagnosis using empirical mode decomposition based feature extraction method

2019 ◽  
Vol 255 ◽  
pp. 06009
Author(s):  
C. Y. Tan ◽  
W. K. Ngui ◽  
M. S. Leong ◽  
M. H. Lim
Keyword(s):  
Artificial Intelligence ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Feature Vector ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Statistical Parameters ◽  
Mode Decomposition ◽  
Parameters Extraction

Blade fault diagnosis had become more significant and impactful for rotating machinery operators in the industry. Many works had been carried out using different signal processing techniques and artificial intelligence approaches for blade fault diagnosis. Frequency and wavelet based features are usually used as the input to the artificial neural network for blade fault diagnosis. However, the application of others time-frequency based feature extraction technique and artificial intelligence approach for blade fault diagnosis is still lacking. In this study, a novel blade fault diagnosis method based on ensemble empirical mode decomposition and extreme learning machine was developed. Bandpass filtering was applied to the raw vibration signals and integrated with the high pass filter to obtain the velocity signal. Synchronous time averaging was then applied to the velocity signals. Three ensemble empirical mode decomposition based feature extraction methods were proposed: direct statistical parameters extraction, intrinsic mode functions averaging statistical parameters extraction and features averaging statistical parameters extraction. The effectiveness of different feature vector sets for blade fault diagnosis was examined. Feature vector set of intrinsic mode functions averaging statistical parameters extraction was found to be more effective for blade fault diagnosis. With the novel proposed method, blade fault diagnosis could be more accurate and precise.

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