Fault Feature Extraction of Rotating Machinery by Using EMD and ICA

2020 ◽  
pp. 185-192
Author(s):  
Fengli Wang ◽  
Yuchao Song
Keyword(s):  
Feature Extraction ◽  
Rotating Machinery ◽  
Fault Feature Extraction

An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

2019 ◽  
Vol 26 (3-4) ◽  
pp. 146-160
Author(s):  
Xianzhi Wang ◽  
Shubin Si ◽  
Yongbo Li ◽  
Xiaoqiang Du
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Random Forest ◽  
Extraction Method ◽  
Rotating Machinery ◽  
Permutation Entropy ◽  
Feature Extraction Method ◽  
Multichannel Signals ◽  
Fault Feature Extraction ◽  
Fault Information

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

scholarly journals An Integrated Fault Diagnosis Method for Rotating Machinery Based on Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy and Uniform Phase Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Jiancheng Gong ◽  
Xiaoqiang Yang ◽  
Fan Pan ◽  
Wuqiang Liu ◽  
Fuming Zhou
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Coarse Grained ◽  
Normal Operation ◽  
Permutation Entropy ◽  
High Dimensional ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Fault Feature Extraction

Rotating machinery refers to machinery that executes specific functions mainly relying on their rotation. They are widely used in engineering applications. Bearings and gearboxes play a key role in rotating machinery, and their states can directly affect the operation status of the whole rotating machinery. Accurate fault detection and judgment of bearing, gearbox, and other key parts are of great significance to the rotating machinery’s normal operation. A new fault feature extraction algorithm for rotating machinery called Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy (ImvMAAPE) is proposed in this paper, and the application of an improved coarse-grained method in fault feature extraction of multichannel signals is realized in this method. This algorithm is combined with the Uniform Phase Empirical Mode Decomposition (UPEMD) method and the t-distributed Stochastic Neighbor Embedding (t-SNE) method, forming a new time-frequency multiscale feature extraction method. Firstly, the multichannel vibration signals are decomposed adaptively into sets of Intrinsic Mode Functions (IMFs) using UPEMD; then, the IMF components containing the main fault information are screened by correlation analysis to get the reconstructed signals. The ImvMAAPE values of the reconstructed signals are calculated to generate the initial high-dimensional fault features, and the t-SNE method with excellent nonlinear dimensionality reduction performance is then used to reduce the dimensionality of the initial high-dimensional fault feature vectors. Finally, the low dimensional feature vectors with high quality are input to the random forest (RF) classifier to identify and judge the fault types. Experiments were conducted to verify whether this method has higher accuracy and robustness than other methods.

Wavelet Algorithm in Rotating Machinery Fault Feature Extraction

2013 ◽  
Vol 823 ◽  
pp. 451-455
Author(s):  
Dong Song Luo ◽  
Zheng Fan
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Spectral Characteristics ◽  
Rotating Machinery ◽  
Frequency Resolution ◽  
Fault Mechanism ◽  
Fault Feature Extraction ◽  
Fault Information ◽  
Acceleration Signals ◽  
Wavelet Algorithm

A method of Rotating Machinery fault feature extraction based on wavelet transform and Hilbert demodulation is been studied. On the basis of rotating machinery fault mechanism and spectral characteristics, wavelet transform is used to be decompose the vibration acceleration signals of bearing faults into different frequency bands, Which is then used to achieve accurate fault information by Hilbert demodulation. The result shows the method can effectively improve the frequency resolution and realize accurate extraction of fault feature, and it has certain practical value for industrial production of rotating machinery faults diagnosis when applied to the production industry. Key words: Rotating Machinery; bearings; Wavelet algorithm; Hilbert demodulation

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