Manifold learning and Lempel-Ziv complexity-based fault severity recognition method for bearing

Yin, Jun; Zhuang, X.; Sui, Wentao; Sheng, Yunlong

doi:10.1016/j.measurement.2023.112714

Cited by 6 publications

(2 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They start with statistical analysis [1,2], an autoregressive model (AR) [3], empirical mode decomposition (EMD) [4], Fourier transform (FT) [5], wavelet analysis [6,7], singular value decomposition (SVD) [8], and other methods to extract signal features which largely determine the effect of fault diagnosis. Then, feature dimensionality reduction is performed using methods such as principal component analysis (PCA) [9] and manifold learning [10] to remove noise and redundant components in order to obtain low-dimensional sensitive features. Finally, trained machine learning methods, such as artificial neural networks (ANNs) [11][12][13] and support vector machine (SVM) [14][15][16][17], are used to identify and classify unknown fault features.…”

Section: Introductionmentioning

confidence: 99%

A Time–Frequency Residual Convolution Neural Network for the Fault Diagnosis of Rolling Bearings

Wu,

Jiang,

et al. 2023

Processes

View full text Add to dashboard Cite

A time–frequency residual convolution neural network (TFRCNN) was proposed to identify various rolling bearing fault types more efficiently. Three novel points about TFRCNN are presented as follows: First, by constructing a double-branch convolution network in the time domain and the frequency domain, the respective features in the time domain and the frequency domain were extracted to ensure the rich and complete feature representation of raw data sources. Second, specific residual structures were designed to prevent learning degradation of the deep network, and global average pooling was adopted to improve the network’s sparsity. Third, TFRCNN was better than the other models in terms of prediction accuracy, robustness, generalization ability, and convergence. The experimental results demonstrate that the prediction accuracy rate of TFRCNN, trained using mixing load data, reached 98.88 to 99.92% after optimizing the initial learning rate and choosing the optimizer and loss function. It was verified that TFRCNN can adaptively learn to extract deep fault features, accurately identify bearing fault conditions, and overcome the limitations of classical shallow feature extraction and classification methods, as well as common convolution neural networks. Hence, this investigation revealed TFRCNN’s potential for bearing fault diagnosis in practical engineering applications.

show abstract

Section: Introductionmentioning

confidence: 99%

A Time–Frequency Residual Convolution Neural Network for the Fault Diagnosis of Rolling Bearings

Wu,

Jiang,

et al. 2023

Processes

View full text Add to dashboard Cite

show abstract

“…In whole, its applications to the machinery fault diagnosis can be divided into two types. One is that the manifold learning is used to reduce the high dimensional features for enhancing the accuracy of the fault pattern recognition [33][34][35]. Another one is that the manifold learning modes is utilized to extract the intrinsic modes related with the repetitive transients from the multiscale or multicomponent information under noise contaminated circumstance [32,36].…”

Section: Introductionmentioning

confidence: 99%

Manifold learning-assisted sparse filtering method for machinery fault diagnosis

Wang,

Peng,

Jiang

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Sparse filtering (SF) has received considerable attentions in the machinery fault diagnosis thanks to its ability to extract the fault-related features using their sparsity. However, the existing SF methods have dilemmas with the empirical selection of model parameters, the loss of fault-related information caused by a screening way for the target mode, and the singularity of results induced by some large-amplitude random impulses (LARIs). Hence, a manifold learning-assisted sparse filtering (MLASF) method is proposed for machinery fault-related feature enhancement in this study. First, an improved intrinsic component filtering (ICF) is presented for extracting the multiple modes with feature enhancement, where the parameters of ICF are adaptively determined by using the optimization object to avoid the empirical selection of parameters. Second, the manifold learning is introduced to compress the enhanced multiple modes to overcome the loss of fault-related information; thus the intrinsic manifolds are obtained for disclosing the buried fault-related features and suppressing the band-in noise. Third, an adaptively weighting strategy for these intrinsic manifolds is constucted to obtain a final representative mode for conducting the machinery fault diagnosis. Meanwhile, the LARIs coupled with intrinsic manifolds are calibrated according to their statistical information to resolve the singularity of the representative features. Simulation and experiments show that the proposed method is more effective in extracting fault-related features than some existing methods.

show abstract

Multivariate multiscale dispersion Lempel–Ziv complexity for fault diagnosis of machinery with multiple channels

Wang,

Li,

Noman

et al. 2024

Information Fusion

View full text Add to dashboard Cite

Manifold learning and Lempel-Ziv complexity-based fault severity recognition method for bearing

Cited by 6 publications

References 53 publications

A Time–Frequency Residual Convolution Neural Network for the Fault Diagnosis of Rolling Bearings

A Time–Frequency Residual Convolution Neural Network for the Fault Diagnosis of Rolling Bearings

Manifold learning-assisted sparse filtering method for machinery fault diagnosis

Multivariate multiscale dispersion Lempel–Ziv complexity for fault diagnosis of machinery with multiple channels

Contact Info

Product

Resources

About