Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding

Kang, Shouqiang; Ma, Danyang; Wang, Yujing; Lan, Chaofeng; Qing-guo, Chen; Микулович, В. И.

doi:10.1016/j.ymssp.2016.10.006

Cited by 39 publications

(16 citation statements)

References 17 publications

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“…Kang et al [19] proposed a novel state assessment method based on the multiple‐domain features. Osman and Wang [20] proposed a new morphological Hilbert–Huang (MH) technique for incipient bearing fault detection.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS

Helmi¹,

Forouzantabar²

2019

IET Electric Power Applications

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Demands for various products, higher qualities, reduction of costs and competitiveness, have resulted in the use of intelligent fault detection systems. Bearing fault diagnosis as a major component of the electric motors has had an essential role in the operation of production units' reliability. In addition, vibration analysis is one of the most powerful tools in diagnostics. Advances in signal processing technology and electrical equipment have developed a machinery condition monitoring for defect detection. This study has used the extracted features of vibration signals and the adaptive neuro-fuzzy interface system (ANFIS) network proposing a structure for fault detection and diagnosis of rolling bearings. Time-domain and frequency-domain statistical characteristics have been extracted fault information from vibration signals. Besides, the test data sets are presented to the ANFIS network. Simulation results indicated that the performance of the ANFIS network is acceptable. The results reveal that this method has more accuracy and better classification performance in comparison with other methods proposed in the literature.

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Section: Introductionmentioning

confidence: 99%

Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS

Helmi¹,

Forouzantabar²

2019

IET Electric Power Applications

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“…In [26], kernel principal component analysis was used to select important features by the feature contribution rate. In [27], a locally linear embedding algorithm was used to reduce the dimensions of each row vector of a feature matrix. In [28], locality preserving projections were used to select the more sensitive low-dimensional information hidden in high-dimensional fusion feature structures.…”

Section: Introductionmentioning

confidence: 99%

Machinery Fault Diagnosis Scheme Using Redefined Dimensionless Indicators and mRMR Feature Selection

Qin

et al. 2020

IEEE Access

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Machinery fault diagnosis methods based on dimensionless indicators have long been studied. However, traditional dimensionless indicators usually suffer a low diagnostic accuracy for mechanical components. Toward this end, an effective fault diagnosis method based on redefined dimensionless indicators (RDIs) and minimum redundancy maximum relevance (mRMR) is proposed to identify the health conditions of mechanical components. In the proposed method, the vibration signals are first processed by the variational mode decomposition, and multiple RDIs are constructed based on the decomposed signals. Subsequently, the mRMR approach is introduced to select the RDIs and several important RDIs can be obtained. Finally, the obtained RDIs are fed into a grid search support vector machine to perform fault pattern identification. To verify the superiority of the proposed method, two experimental examples for different fault types of mechanical components including rolling bearing and gearbox are conducted. The experimental results demonstrated that the RDIs as new fault features can effectively solve the deficiency of the traditional dimensionless indicator, and has stronger distinguishing ability for machinery faults. Additionally, our proposed method successfully differentiated 12 fault conditions of rolling bearings and nine fault conditions of gears with average accuracies of 97.47% and 97.12% with 11 and 5 RDIs, respectively. INDEX TERMS Redefined dimensionless indicators, variational mode decomposition, minimum redundancy maximum relevance, grid search support vector machine, fault diagnosis.

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“…Deng et al [11] proposed a new motor bearing fault diagnosis method based on integrating the empirical wavelet transform, fuzzy entropy, and SVM, which can accurately and effectively perform fault diagnosis. Kang et al [12] extracted features of the time, frequency, and time-frequency domains of the rolling bearings vibration signals to construct high-dimensional hybrid-features. The reduced features were inputted into the SVM for multiple-state assessment and achieved high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Method of state identification of rolling bearings based on deep domain adaptation under varying loads

Kang

Chen

Wang

et al. 2020

IET Science, Measurement & Technology

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Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding

Cited by 39 publications

References 17 publications

Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS

Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS

Machinery Fault Diagnosis Scheme Using Redefined Dimensionless Indicators and mRMR Feature Selection

Method of state identification of rolling bearings based on deep domain adaptation under varying loads

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