An Integrated Fault Diagnosis Method for Rotating Machinery Based on Improved Multivariate Multiscale Amplitude-Aware Permutation Entropy and Uniform Phase Empirical Mode Decomposition. (6th August 2021)

• Record Type: Journal Article
• Authors: Gong, Jiancheng; Yang, Xiaoqiang; Pan, Fan; Liu, Wuqiang; Zhou, Fuming
• Other Names: Tang Gang Academic Editor.
• Is Part Of: Shock and vibration. Volume 2021(2021)
• Publication Date: 2021-08-06
• Subjects: Shock (Mechanics) -- Periodicals; Vibration -- Periodicals; 534.5
• DOI: 10.1155/2021/2098892
• Languages: English
• ISSNs: 1070-9622

Abstract

Abstract : 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.