A method for degradation features extraction of diesel engine valve clearance based on modified complete ensemble empirical mode decomposition with adaptive noise and discriminant correlation analysis feature fusion

2021 ◽  
pp. 107754632110161
Author(s):  
Yun Ke ◽  
Yihuai Hu ◽  
Enzhe Song ◽  
Chong Yao ◽  
Quan Dong
Keyword(s):  
Diesel Engine ◽  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Feature Fusion ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Valve Mechanism ◽  
Engine Valve ◽  
Mode Decomposition ◽  
Adaptive Noise

The health assessment of the valve clearance is a key link to realize the failure prediction and health management of the valve mechanism. To accurately evaluate the state of valve clearance, this article proposes a diesel engine valve clearance degradation feature extraction method based on modified complete ensemble empirical mode decomposition with adaptive noise and discriminant correlation analysis feature fusion algorithm. First, we use modified complete ensemble empirical mode decomposition with adaptive noise to adaptively filter the cylinder head vibration signal. Then, power spectrum entropy and improved hierarchical dispersion entropy are proposed as degenerate feature entropy. To improve the sensitivity of the degraded feature entropy to the degraded state, the discriminant correlation analysis algorithm is used to fuse the two types of feature entropy to obtain fused degraded feature entropy. Finally, the degenerate fusion features are input into the least squares support vector machine to realize the health status assessment of the valve mechanism. Through the verification of test data, the results show that the proposed method can effectively evaluate the health state of the valve clearance of diesel engines.

scholarly journals Rolling Bearings Fault Diagnosis Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Nonlinear Entropy, and Ensemble SVM

2020 ◽  
Vol 10 (16) ◽  
pp. 5542 ◽  
Author(s):  
Rui Li ◽  
Chao Ran ◽  
Bin Zhang ◽  
Leng Han ◽  
Song Feng
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Economic Loss ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Operational Conditions ◽  
Mode Decomposition ◽  
Adaptive Noise

Rolling bearings are fundamental elements that play a crucial role in the functioning of rotating machines; thus, fault diagnosis of rolling bearings is of great significance to reduce catastrophic failures and heavy economic loss. However, the vibration signals of rolling bearings are often nonlinear and nonstationary, resulting in difficulty for feature extraction and fault recognition. In this paper, a hybrid method for multiple fault diagnosis of rolling bearings is presented. The bearing vibration signals are decomposed with the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) to denoise and extract nonlinear entropy features. The nonlinear entropy features are further processed to select the more discriminative fault features and to reduce feature dimension. Then a multi-class intelligent recognition model based on ensemble support vector machine (ESVM) is constructed to diagnose different bearing fault modes as well as fault severities. The effectiveness of the proposed method is assessed via experimental case studies of rolling bearings under multiple operational conditions (i.e., speeds and loads). The results show that our method gives better diagnosis results as compared to some existing approaches.

scholarly journals Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liye Zhao ◽  
Wei Yu ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

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