Fault diagnosis of rolling bearings based on improved direct fast iterative filtering and spectral amplitude modulation

As the transient impulse components in early fault signals are weak and easily buried by strong background noise, the fault features of rolling bearings are difficult to be extracted effectively. Focusing on this issue, a novel method based on improved direct fast iterative filtering and spectral amplitude modulation (IDFIF-SAM) is presented for detecting the early fault of rolling bearings. First, the ratio of the average crest factor of autocorrelation envelope spectrum to the average envelope entropy is taken as the fitness function to search the optimal parameters of direct fast iterative filtering (DFIF) adaptively via particle swarm optimization (PSO). Then, the efficient kurtosis entropy (EKE) index is being employed to choose the suitable components to reconstruct the signal. Finally, the reconstructed signal is subjected to spectral amplitude modulation (SAM) to strengthen the impulse features. The superiority of improved direct fast iterative filtering (IDFIF) over fixed-parameter DFIF, fast iterative filtering (FIF), and hard thresholding fast iterative filtering (HTFIF) is clarified through the simulated signal. Moreover, the comparative experimental analysis shows that the proposed IDFIF-SAM method can identify the early fault feature of rolling bearings more effectively.

Pan-Sharpening for Spectral Details Preservation Via Convolutional Sparse Coding in Non-Subsampled Shearlet Space

The optical satellite sensors encounter certain constraints on producing high-resolution multispectral (HRMS) images. Pan-sharpening (PS) is a remote sensing image fusion technique, which is an effective mechanism to overcome the limitations of available imaging products. The prevalent issue in PS algorithms is the imbalance between spatial quality and spectral details preservation, thereby producing intensity variations in the fused image. In this paper, a PS method is proposed based on convolutional sparse coding (CSC) implemented in the non-subsampled shearlet transform (NSST) domain. The source images, panchromatic (PAN) and multispectral (MS) images, are decomposed using NSST. The resultant high-frequency bands are fused using adaptive weights determined from chaotic grey wolf optimization (CGWO) algorithm. The CSC-based model is employed to fuse the low-frequency bands. Further, an iterative filtering mechanism is developed to enhance the quality of fused image. Four datasets with different geographical content like urban area, vegetation, etc. and eight existing algorithms are used for evaluation of the proposed PS method. The comprehensive visual and quantitative results approve that the proposed method accomplishes considerable improvement in spatial and spectral details equivalence in the pan-sharpened image.

Application of Adaptive Local Iterative Filtering and Permutation Entropy in Gear Fault Recognition

In this paper, a fault identification method combining adaptive local iterative filtering and permutation entropy is proposed. The adaptive local iterative filtering can decompose the nonstationary signal into a finite number of stationary intrinsic mode functions. And the experiment gear fault data are decomposed into several intrinsic mode functions by this method. Then, using the permutation entropy to calculate each intrinsic mode function, it is found that the permutation entropy of the first several intrinsic mode functions can represent the characteristics of different fault types, and the permutation entropy of the intrinsic mode function corresponding to the rotating frequency signal of the gear system could be the boundary. Finally, the fault type of gear is identified by calculating the gray correlation degree of permutation entropy of essential mode function of vibration signal decomposition under different working conditions. The example analysis results show that the proposed method can be effectively applied to the fault diagnosis of the gear system.

A Hybrid Method to Diagnose 3D Rotor Eccentricity Faults in Synchronous Generators Based on ALIF_PE and KFCM

This paper proposed a new hybrid diagnosis method for the generator’s 3D static eccentricity faults which include the axial eccentricity, the radial eccentricity, and the mixed eccentricity composed of the former two. Firstly, adaptive local iterative filtering (ALIF) method was used to decompose the vibration signals of the generator under eccentricity faults. Then, in order to figure out the intrinsic mode function (IMF) components with the upmost feature information, the correlation coefficient was calculated. Finally, the components’ permutation entropy (PE) is extracted to construct the eigenvector matrix which can be used to input the kernel fuzzy C-means (KFCM) algorithm to obtain the result of clustering. The result indicates that the classification coefficient based on ALIF and KFCM behaves closer to 1, while the average fuzzy entropy (FE) is closer to 0, showing that this method is able to detect different eccentricity faults more accurately.