Compound faults diagnosis based on customized balanced multiwavelets and adaptive maximum correlated kurtosis deconvolution

Liu, Hong; Liu, Xiaobo; Zuo, Hongchao

doi:10.1016/j.measurement.2019.06.022

Cited by 48 publications

(20 citation statements)

References 42 publications

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“…In this section, the algorithm performance of IA-optimal CNN will be compared with five existing A-optimal-based classification method. Algorithms used for comparison are A-optimal subspace learning method proposed by He [36], the nonnegative matrix factorization method proposed by Liu [37], the method based on neighborhood regularization proposed by Li [38], the method based on Hessian energy Regularization proposed by Yang [50], and the A-optimal CNN method proposed by Yin [51]. The five metrics to evaluate networks performance are accuracy, precision, recall, G-score, and F1-score.…”

Section: Performance Comparison Of Different A-optimalbased Classification Algorithmsmentioning

confidence: 99%

“…However, an inconvenience of the traditional method is that there are many features that can be extracted from the signals, such as peak, peak to peak, RMS, kurtosis, skewness, impulse factor, et al [33][34][35][36][37]. In different application scenarios, the types and quantities of extracted signals features need to be selected based on historical experience.…”

Section: Introductionmentioning

confidence: 99%

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Signals classification based on IA-optimal CNN

Zhang

et al. 2021

Neural Comput & Applic

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The versatility of the existing A-optimal-based CNN for solving multiple types of signals classification problems has not been verified by different signals datasets. Moreover, the existing A-optimal-based CNN uses a simplified approximate function as the optimization objective function instead of precise analytical function, which affects the signals classification accuracy to a certain extent. In this paper, a classification method called IA-optimal CNN is proposed. To improve the stability of the classifier, the trace of the covariance matrix of the weights of the fully connected layer is used as the optimization objective function, and the parameter optimization model is established without any simplification of the optimization objective function. In addition, to avoid the difficulty of not being able to obtain the analytical expression formula of the partial derivative of the inverse matrix with regard to the networks parameters, a novel dual function is introduced to transform the optimization problem into an equivalent binary function optimization problem. Furthermore, based on the above analytical solution results, the parameters are updated using the alternate iterative optimization method and the accurate weight update formula is deduced in detail. Five signals datasets are used to test the universality of the IAoptimal CNN in signals classification fields. The performance of IA-optimal CNN is showed, and the experimental results are compared with the existing A-optimal-based classification algorithm. Lastly, the following conclusion is proved theoretically: For the A-optimal-based CNN, the trace of the covariance matrix will continue to decrease and approach a convergence value in the iterative process, but it is impossible for the networks to strictly reach the A-optimal state.

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Section: Performance Comparison Of Different A-optimalbased Classification Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Signals classification based on IA-optimal CNN

Zhang

et al. 2021

Neural Comput & Applic

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“…Multiwavelets are more flexible, and can combine properties such as compact support, orthogonality and symmetry. This added flexibility is for example advantageous in multiwavelet denoising, which has been applied in rolling bearing fault detection [13][14][15][16], and in the load spectrum of computer numerical control lathe [17]. Recently, the correspondence between multiwavelet shrinkage and nonlinear diffusion was studied [18].…”

Section: Introductionmentioning

confidence: 99%

The Design of Matched Balanced Orthogonal Multiwavelets

Karel

Steenkiste

Peeters

2022

Front. Appl. Math. Stat.

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The theory of orthogonal multiwavelets offers enhanced flexibility for signal processing applications and analysis by employing multiple waveforms simultaneously, rather than a single one. When implementing them with polyphase filter banks, it has been recognized that balanced vanishing moments are needed to prevent undesirable artifacts to occur, which otherwise compromise the interpretation and usefulness of the multiwavelet analysis. In the literature, several such balanced orthogonal multiwavelets have been constructed and published; but however useful, their choice is still limited. In this work we present a full parameterization of the space of all orthogonal multiwavelets with two balanced vanishing moments (of orders 0 and 1), for arbitrary given multiplicity and degree of the polyphase filter. This allows one to search for matching multiwavelets for a given application, by optimizing a suitable design criterion. We present such a criterion, which is sparsity-based and useful for detection purposes, which we illustrate with an example from electrocardiographic signal analysis. We also present explicit conditions to build in a third balanced vanishing moment (of order 2), which can be used as a constraint together with the earlier parameterization. This is demonstrated by constructing a balanced orthogonal multiwavelet of multiplicity three, having three balanced vanishing moments, but this approach can easily be employed for arbitrary multiplicity.

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“…McDonald et al [38] proposed a new deconvolution method for the detection of gear and bearing faults from vibration data. Hong et al [39] proposed a method combining customized balanced multiwavelets and adaptive MCKD for rotating mechanical compound faults diagnosis. Jia et al [40] proposed an improved spectral kurtosis (SK) method based on maximum correlated kurtosis deconvolution (MCKD) to extract the weak fault characteristics of bearings from the signals.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis

Guo²,

Chen

2021

IEEE Access

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Aiming at the problem of feature extraction of non-stationary, non-linear and weak fault signals, a new feature extraction method based on empirical wavelet transform (EWT) with scale space threshold (STEWT) and improved maximum correlation kurtosis deconvolution (MCKD) with power spectral entropy and grid search (PGMCKD), namely STEWT-PGMCKD is proposed for rolling bearing faults in this paper. In the proposed STEWT-PGMCKD method, the scale space threshold method is designed to solve the problems of falling into local extremum and mode over decomposition caused by the local-max-min band decomposition method of EWT, which is used to decompose the frequency band of signal, and the correlation analysis is carried out between the decomposed modal components and the original signal to retain the modal components with high correlation. Then an adaptive MCKD based on power spectral entropy is proposed to solve the problem that the signal processing effect of MCKD is affected by filter size L and deconvolution period T . Nextly, the parameters of the MCKD are optimized by grid search method. Finally, the power spectrum analysis of the enhanced signal is carried out to realize the feature extraction and fault diagnosis. The experiment results show that the proposed STEWT-PGMCKD method can effectively extract the weak fault information and accurately realize the fault diagnosis for rolling bearings.

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Compound faults diagnosis based on customized balanced multiwavelets and adaptive maximum correlated kurtosis deconvolution

Cited by 48 publications

References 42 publications

Signals classification based on IA-optimal CNN

Signals classification based on IA-optimal CNN

The Design of Matched Balanced Orthogonal Multiwavelets

Feature Extraction Based on EWT With Scale Space Threshold and Improved MCKD for Fault Diagnosis

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