Multilevel nested reliability-based design optimization with hybrid intelligent regression for operating assembly relationship

Fei, Chengwei; Li, Huan; Liu, Hao-Tian; Lu, Cheng; Keshtegar, Begrooz; An, Liqiang

doi:10.1016/j.ast.2020.105906

Cited by 67 publications

(16 citation statements)

References 33 publications

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“…To build up a model-based fault diagnosis approach which is widely applied on rolling bearings [35], [36], we apply the Laplacian score (LS) method to sort and select the features. And at last, we use the particle swarm optimization based support vector machine (PSO-SVM) classification method [37] - [41] to realize an intelligent fault diagnosis of rolling bearings. Compared with some learningbased methods [42], PSO-SVM has better robustness.…”

Section: Introductionmentioning

confidence: 99%

Generalized Composite Multiscale Diversity Entropy and Its Application for Fault Diagnosis of Rolling Bearing in Automotive Production Line

Liang

Chen

2021

IEEE Access

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This paper considers the entropy based feature extraction method for the fault diagnosis of rolling bearings in automobile production line, where the fault information is difficult to identify due to the strong nonlinear and non-stationary characteristics of the fault vibration signals. In our work, a novel entropy based method called generalized composite multiscale diversity entropy (GCMDiEn) is developed. This method can effectively track the inside pattern changes of the time series by the description of cosine similarity between adjacent orbits. Unlike most of the existing entropy based results which concentrate on the static orderliness, we analyze the dynamic complexity characteristics of the arbitrary time series. Moreover, compared with the multiscale diversity entropy, GCMDiEn calculates all coarse-grained time series entropy value with the same scale and extends the first-order moment to second-order moment to mitigate the shortcomings of the short time series instability and losing part of the frequency region information. This paper proposed a new rolling bearing fault diagnosis framework which combined the GCMDiEn with the empirical wavelet transform (EWT), Laplacian score (LS), and particle swarm optimization-based support vector machine (PSO-SVM). Finally, the simulation results show the superiority of the GCMDiEn method over the multiscale diversity entropy method. The proposed framework has a higher fault recognition rate (99.38%) than the existing methods.

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Section: Introductionmentioning

confidence: 99%

Generalized Composite Multiscale Diversity Entropy and Its Application for Fault Diagnosis of Rolling Bearing in Automotive Production Line

Liang

Chen

2021

IEEE Access

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“…Rolling bearings play a pivotal role in modern industry and their applications are very extensive, such as aircraft engines [1,2], gas turbines [3], landing gear [4], flexible mechanism [5], and wind turbines [6][7][8][9][10][11]. However, the rolling bearing is very easy to cause damage in a strong vibration environment, and the rolling bearing is very prone to failure in most rotating machinery systems.…”

Section: Introductionmentioning

confidence: 99%

A Fault Feature Extraction Method for Rolling Bearing Based on Intrinsic Time‐Scale Decomposition and AR Minimum Entropy Deconvolution

Ding

Huang

Xiao

et al. 2021

Shock and Vibration

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It is very difficult to extract the feature frequency of the vibration signal of the rolling bearing early weak fault and in order to extract its feature frequency quickly and accurately. A method of extracting early weak fault vibration signal feature frequency of the rolling bearing by intrinsic time-scale decomposition (ITD) and autoregression (AR) minimum entropy deconvolution (MED) is proposed in this paper. Firstly, the original early weak fault vibration signal of the rolling bearing is decomposed by the ITD algorithm to proper rotations (PRs) with fault feature frequency. Then, the sample entropy value of each PR is calculated to find the largest PRs of the sample entropy. Finally, the AR-MED filtering algorithm is utilized to filter and reduce the noise of the largest PRs of the sample entropy value, and the early weak fault vibration signal feature frequency of the rolling bearing is accurately extracted. The results show that the ITD-AR-MED method can extract the early weak fault vibration signal feature frequency of the rolling bearing accurately.

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“…For example, the intentional mistuning was introduced to reduce the forced response of blade [32][33][34][35]. In addition, some other scholars conducted the reliability and optimization of blades [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Nonproportional Intentionally Mistuned Turbine Blisk Design with Improved Component Modal Synthesis

Bai

Yang

Zhu

et al. 2021

Shock and Vibration

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An improved component modal synthesis-based nonproportional mistuning method (ICMS-NPMM) is proposed to investigate mistuned turbine blisks (MTBs) since the high-fidelity finite element models (HFEMs) involve large number of computations, which leads to low calculation efficiency. To reduce degrees of freedom and suppress the flutter of MTB, it is divided into mistuned blade structure and tuned disk structure, and the intentional mistuning is considered. Furthermore, the mistuned parameters, nonproportional mistuning, and complex loads are also considered. Firstly, the basic theory of ICMS-NPMM is investigated; secondly, the model of MTB is established via ICMS-NPMM; finally, the intentionally mistuned design of modal shape amplitudes (MSAs) is investigated via ICMS-NPMM. The results indicate that the calculation efficiency is enhanced via ICMS-NPMM relative to that of via HFEM. In addition, the sensitivity and the flutter are decreased; meanwhile, the amplitude fluctuations of MSAs are distinctly decreased and become comparatively smooth. This investigation provides an important guidance for the vibration characteristic study of complex mechanical structures in engineering practice.

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Multilevel nested reliability-based design optimization with hybrid intelligent regression for operating assembly relationship

Cited by 67 publications

References 33 publications

Generalized Composite Multiscale Diversity Entropy and Its Application for Fault Diagnosis of Rolling Bearing in Automotive Production Line

Generalized Composite Multiscale Diversity Entropy and Its Application for Fault Diagnosis of Rolling Bearing in Automotive Production Line

A Fault Feature Extraction Method for Rolling Bearing Based on Intrinsic Time‐Scale Decomposition and AR Minimum Entropy Deconvolution

Nonproportional Intentionally Mistuned Turbine Blisk Design with Improved Component Modal Synthesis

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