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.
Highlights
Rotating machinery often works in a high speed and heavy load environment, which is prone to failure and will cause very serious consequences upon failure. erefore, real-time monitoring and fault diagnosis of the important parts of rotating machinery have great significance [1]
(6) After dimensionality reduction, the low-dimensional feature vectors are put into the random forest (RF) classifier to obtain the final fault classification and recognition results e flow chart of this method is shown in Figure 3. e original vibration signals of the failure machinery are processed by Uniform Phase Empirical Mode Decomposition (UPEMD), obtaining a set of Intrinsic Mode Functions (IMFs) components, and the correlation coefficients between them and the original vibration data are calculated
Calculate the ImvMAAPE values of the signal reconstructed by the selected IMF components with more fault information. en, the t-distributed Stochastic Neighbor Embedding (t-stochastic neighbor embedding (SNE)) method is used to reduce the dimension of high dimension feature vectors, remove the interference and redundancy features, and obtain the sensitive low-dimension features
Summary
Rotating machinery often works in a high speed and heavy load environment, which is prone to failure and will cause very serious consequences upon failure. erefore, real-time monitoring and fault diagnosis of the important parts of rotating machinery have great significance [1]. Erefore, real-time monitoring and fault diagnosis of the important parts of rotating machinery have great significance [1]. Ese vibration signals contain a lot of fault information. If they can be extracted effectively, rotating machinery’s faults will be diagnosed in time and effectively. Time-frequency analysis methods such as fast Fourier transform (FFT), empirical mode decomposition (EMD) [2], and wavelet packet transform (WPT) [3] are widely used in feature extraction of vibration signals. Fast Fourier transform (FFT) method is only suitable for the analysis of stationary signal; WPT is more flexible than WT and can choose the frequency resolution, but it is still not self-adaptive and still needs to set the basic wavelet functions and parameters in advance.
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