Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

Rodríguez, Nibaldo; Barba, Lida; Alvarez, Pablo Gonzalez; Cabrera‐Guerrero, Guillermo

doi:10.3390/e21060540

Cited by 10 publications

(3 citation statements)

References 50 publications

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“…The results from ELMs were refined by Softmax to generate final result. Rodriguez and Barba [149] combined wavelet packet Fourier entropy and shallow kernel ELM for bearing fault detection. Qiu and Wu [141] proposed an insulator pollution detection method based on ELM for hyperspectral image.…”

Section: Recognitionmentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

279

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Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

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Section: Recognitionmentioning

confidence: 99%

A review on extreme learning machine

Wang

et al. 2021

Multimed Tools Appl

279

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“…Analyzing the signs of vibrations has been successfully used in various industrial processes [ 25 , 26 ]. Various methods were used by researchers to analyze vibration signals [ 27 , 28 , 29 ]. Due to the complexity and dynamics of the signals, studies have been conducted to establish the criteria for processing the vibration signals of the CDFW process.…”

Section: Vibration Signal Analysismentioning

confidence: 99%

Vibrations in CDFW

Alcântara

Balestrassi

Gomes

et al. 2020

Entropy

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Continuous drive friction welding is a solid-state welding process that has been experimentally proven to be a fast and reliable method. This is a complex process; deformations in the viscosity of a material alter the friction between the surfaces of the pieces. All these dynamics cause changes in the vibration signals; the interpretation of these signals can reveal important information. The vibration signals generated during the friction and forging stages are measured on the stationary part of the structure to determine the influence of the manipulated variables on the time domain statistical characteristics (root mean square, peak value, crest factor, and kurtosis). In the frequency domain, empirical mode decomposition is used to characterize frequencies. It was observed that it is possible to identify the effects of the manipulated variables on the calculated statistical characteristics. The results also indicate that the effect of manipulated variables is stronger on low-frequency signals.

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“…Among them, Shannon entropy features have been widely used in machine health monitoring recently. For example, the instantaneous energy distribution-permutation entropy (IED-PE) [ 16 ], the improved multiscale dispersion entropy (IMDE) [ 17 ], the composite multi-scale weighted permutation entropy (CMWPE) [ 18 ], the stationary wavelet packet Fourier entropy (SWPFE) [ 19 ], and similarity-fuzzy entropy [ 20 ] have been proposed to construct the sensitive feature for rolling balling heath monitoring. However, the construction of good sensitive features requires manual experience, which is called feature engineering problem.…”

Section: Introductionmentioning

confidence: 99%

A Novel End-To-End Feature Selection and Diagnosis Method for Rotating Machinery

Wang

Zhao

Zhang

et al. 2021

Sensors

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Feature selection is to obtain effective features from data, also known as feature engineering. Traditional feature selection and predictive model learning are separated, and there is a problem of inconsistency of criteria. This paper presents an end-to-end feature selection and diagnosis method that organically unifies feature expression learning and machine prediction learning into one model. The algorithm first combines the prediction model to calculate the mean impact value (MIVs) of the feature and realizes primary feature selection for the prediction model by selecting the feature with a larger MIV. In order to take into account the performance of the feature itself, the within-class and between-class discriminant analysis (WBDA) method is proposed, and combined with the feature diversity strategy, the feature-oriented secondary selection is realized. Eventually, feature vectors obtained by two selections are classified using a multi-class support vector machine (SVM). Compared with the modified network variable selection algorithm (MIVs), the principal component analysis dimensionality reduction algorithm (PCA), variable selection based on compensative distance evaluation technology (CDET), and other algorithms, the proposed method MIVs-WBDA exhibits excellent classification accuracy owing to the fusion of feature selection and predictive model learning. According to the results of classification accuracy testing after dimensionality reduction on rotating machinery status, the MIVs-WBDA method has a 3% classification accuracy improvement under the low-dimensional feature set. The typical running time of this classification learning algorithm is less than 10 s, while using deep learning, its running time will be more than a few hours.

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Stationary Wavelet-Fourier Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis

Cited by 10 publications

References 50 publications

A review on extreme learning machine

A review on extreme learning machine

Vibrations in CDFW

A Novel End-To-End Feature Selection and Diagnosis Method for Rotating Machinery

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