Intelligent fault diagnosis of planetary gearbox based on refined composite hierarchical fuzzy entropy and random forest

Wei, Yu; Yang, Yuantao; Xu, Min; Huang, Wenhu

doi:10.1016/j.isatra.2020.10.028

Cited by 83 publications

(22 citation statements)

References 28 publications

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“…43 The RF classifier can deal with the nonlinear data, and it has found various applications, such as sample identification, feature selection, and classification of various machineries. [44][45][46] The above-prepared datasets (dataset I, II, and III) are supplied as input to the RF algorithm to detect the defects and discriminate among the gearbox's health states. The classification accuracies are displayed in Figure 13.…”

Section: Random Forest Algorithmmentioning

confidence: 99%

An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

Inturi

Balaji

Gyanam

et al. 2022

Structural Health Monitoring

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The vibration and acoustic signals collected from rotating machinery are often non-stationary and aperiodic, and it is a challenge to post-process and extract the defect sensitive health indicators. In this paper, we demonstrate how the estimated Hurst exponent of such measured data can be advantageous for analyzing non-stationary and aperiodic data due to its self-similarity and scale-invariance properties. To illustrate this, the paper demonstrates detection of fault diagnostics of a multi-stage gearbox operating under fluctuating speeds through estimated Hurst exponent of the raw vibration and acoustic signals as a health indicator. Thirteen health states of the gearbox are considered, and the raw vibration and acoustic signals are collected. The Hurst exponents are calculated using three different approaches: generalized Hurst exponent (q = 1, 2, 3, and 4), rescale range statistical (R/S) analysis, and dispersion analysis from the vibration and acoustic signals. Three different health indicator datasets are formulated and subjected to feature learning through conventional machine-learning (decision tree and support vector machine) and advanced machine-learning (deep-learning) classifiers. The effectiveness of these datasets while discriminating between the health states of the gearbox is investigated, yielding classification accuracies of 96.4% when compared with the individual health indicator datasets. The ability of the fault diagnosis and defect severity analysis with reduced reliance on the signal post-processing algorithms is demonstrated.

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Section: Random Forest Algorithmmentioning

confidence: 99%

An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

Inturi

Balaji

Gyanam

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…Pan and Yang et al proposed maximum mean difference embedding (MMDE) algorithm to find a kernel function that minimizes the maximum mean difference (MMD) between the training and the test data in a higher dimensional Hilbert space [22] [23]. Then transfer component analysis (TCA) method is evolved on the basis of MMDE, but the difference is that TCA consider the correlation of labels and learn a dimensional reduction matrix to minimize the MMD of the projected data [24].…”

Section: Index Termsmentioning

confidence: 99%

Cross-Domain Intelligent Fault Diagnosis Method of Rotating Machinery Using Multi-Scale Transfer Fuzzy Entropy

et al. 2021

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“…As a classification and regression tool, random forest (RF) can usually obtain excellent diagnostic accuracy in mechanical fault detection [40]. In [41], the refined composite hierarchical fuzzy entropy (RCHFE) and RF are utilized for planetary gearbox fault diagnosis. In [42], a fault diagnosis method based on core principal component analysis and RF is proposed, which is applied to a wind energy conversion system and achieves good results.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest

Liu

Yang²,

et al. 2022

Sensors

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Rolling bearings are the vital components of large electromechanical equipment, thus it is of great significance to develop intelligent fault diagnoses for them to improve equipment operation reliability. In this paper, a fault diagnosis method based on refined composite multiscale reverse dispersion entropy (RCMRDE) and random forest is developed. Firstly, rolling bearing vibration signals are adaptively decomposed by variational mode decomposition (VMD), and then the RCMRDE values of 25 scales are calculated for original signal and each decomposed component as the initial feature set. Secondly, based on the joint mutual information maximization (JMIM) algorithm, the top 15 sensitive features are selected as a new feature set and feed into random forest model to identify bearing health status. Finally, to verify the effectiveness and superiority of the presented method, actual data acquisition and analysis are performed on the bearing fault diagnosis experimental platform. These results indicate that the presented method can precisely diagnose bearing fault types and damage degree, and the average identification accuracy rate is 97.33%. Compared with the refine composite multiscale dispersion entropy (RCMDE) and multiscale dispersion entropy (MDE), the fault diagnosis accuracy is improved by 2.67% and 8.67%, respectively. Furthermore, compared with the RCMRDE method without VMD decomposition, the fault diagnosis accuracy is improved by 3.67%. Research results prove that a better feature extraction technique is proposed, which can effectively overcome the deficiency of existing entropy and significantly enhance the ability of fault identification.

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Intelligent fault diagnosis of planetary gearbox based on refined composite hierarchical fuzzy entropy and random forest

Cited by 83 publications

References 28 publications

An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

An integrated condition monitoring scheme for health state identification of a multi-stage gearbox through Hurst exponent estimates

Cross-Domain Intelligent Fault Diagnosis Method of Rotating Machinery Using Multi-Scale Transfer Fuzzy Entropy

Intelligent Diagnosis of Rolling Element Bearing Based on Refined Composite Multiscale Reverse Dispersion Entropy and Random Forest

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