Bearing life prognosis based on monotonic feature selection and similarity modeling

Niu, Gang; Fang, Qian; Choi, Byeong-Keun

doi:10.1177/0954406215608892

Cited by 19 publications

(15 citation statements)

References 23 publications

(30 reference statements)

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“…The result was verified with the published literature. [31][32][33] The crack propagation effect was verified with the published article on the same. 38 The bearing B2-2 HDIE curve is shown in Figure 13(a) and (b).…”

Section: Resultssupporting

confidence: 62%

“…The highly monotonic features are used for the detection of fault in bearing as it is treated as an effective feature for calculation of bearing degradation. Recent publications [31][32][33][34] have suggested the frequency domain feature for the prediction of degradation in gear and bearings. The selected fault feature vectors are being applied as an input vector to the GTM.…”

Section: Feature Extractionmentioning

confidence: 99%

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Combination of envelope spectra and generative topographic mapping to diagnose bearing fault and evaluate degradation of bearing

Nistane

Harsha

2019

Noise & Vibration Worldwide

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Functioning of rotary machines is based on the rolling element of bearings, as the rolling element of bearings is the crucial component of rotary machines. The entire operation of rotating machines may terminate after the failure of rolling element of bearings. Hence, the failure of rotating machine is mainly dependent on rolling element of bearings, as it may break down an entire process. Thus, to avoid the sudden breakdown of rotary machines, it is essential to develop the diagnostics and prognostics methodology of rolling element of bearings. In the rolling element of bearings, the assessment of bearing degradation and fault diagnostics is essential to establish the methodology. Therefore, this article proposes a methodology to diagnose the fault of bearing and to assess bearing degradation. Envelope spectra and generative topographic mapping are used together in the methodology proposed. Selection of frequency band based on encompasses one or more resonance of spectrum of signal. Envelope spectrum is determined to pick up the peak of the characteristic fault frequencies of the bearing. Extraction of features from time and frequency domains is used in the generative topographic mapping. Health degradation index, an index termed as evolution of bearing degradation, is obtained from the classifier of generative topographic mapping. Experiments were conducted on the bearings to verify the proposed methodology. On interpreting the obtained results, it was found that the proficiency of proposed methodology is most appropriate for detecting the fault in bearing and tracking the degradation evolution in bearings.

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Section: Resultssupporting

confidence: 62%

Section: Feature Extractionmentioning

confidence: 99%

Combination of envelope spectra and generative topographic mapping to diagnose bearing fault and evaluate degradation of bearing

Nistane

Harsha

2019

Noise & Vibration Worldwide

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“…Therefore, in this paper the authors have included several frequency-domain features that have been applied successfully for condition monitoring of bearings and gears in the recent few years. 18,[31][32][33] The list of features and their computational formulae are listed in Table 1. As far as the authors' knowledge, this feature set is being used first time with SOM.…”

Section: Theory Of Svrmentioning

confidence: 99%

“…The techniques recommended in papers did not require the establishment of threshold for RUL estimation. Niu et al 18 suggested a new approach for monotonic feature selection based on rank mutual information and utilised the concept of similarity modeling for RUL prediction of bearings. The research papers indicate that a monotonic feature is desirable for accurately determining the RUL, which is another big challenge in bearing prognostics.…”

Section: Introductionmentioning

confidence: 99%

Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression

Rai

2017

Proceedings of the Institution of Mechanical Engineers, Part C:

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Rolling element bearings are critical components of rotating machines since the failure of rolling element bearings may cease the functioning of the entire equipment. The damages observed due to bearing failures are expeditious in nature and hence the need to develop an effective prognostic methodology becomes a requisite to prevent the sudden machinery breakdown. The performance degradation assessment and accurate determination of remaining useful life are the two key issues in prognostics of rolling element bearings. This paper proposes a degradation indicator based on self-organising map for the performance degradation assessment of bearings and later support vector regression is utilised to estimate the remaining useful life of bearings. The time-domain and frequency domain features extracted from the raw bearing vibration signals are supplied to the self-organising map classifier to achieve the degradation index termed as self-organising map-minimum quantisation error evolution in the paper. For estimating the remaining useful life of bearings, first the central trend of minimum quantisation error is extracted to achieve the feature vector defined as bearing health index in this work. The bearing health index is then used as input and the life percentage of the bearing is set to output in order to build the support vector regression prediction model for remaining useful life estimation of bearings. The proposed method is validated on the vibration signatures collected in a bearing test rig. The results show that the advocated method can efficiently track the evolution of deterioration and predict the remaining useful life of bearings.

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“…The operating conditions of the test bench are as follows: The motor speed is 1800 rpm. The sampling frequency is 25.6 kHz sampling, and a radial force of 4000N is applied to the rotating shaft and the bearing to be tested [ 77 ].…”

Section: Experimental Studymentioning

confidence: 99%

Information Dynamic Correlation of Vibration in Nonlinear Systems

Yang

Zhang

et al. 2019

Entropy

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In previous studies, information dynamics methods such as Von Neumann entropy and Rényi entropy played an important role in many fields, covering both macroscopic and microscopic studies. They have a solid theoretical foundation, but there are few reports in the field of mechanical nonlinear systems. So, can we apply Von Neumann entropy and Rényi entropy to study and analyze the dynamic behavior of macroscopic nonlinear systems? In view of the current lack of suitable methods to characterize the dynamics behavior of mechanical systems from the perspective of nonlinear system correlation, we propose a new method to describe the nonlinear features and coupling relationship of mechanical systems. This manuscript verifies the above hypothesis by using a typical chaotic system and a real macroscopic physical nonlinear system through theory and practical methods. The nonlinear vibration correlation in multi-body mechanical systems is very complex. We propose a full-vector multi-scale Rényi entropy for exploring the chaos and correlation between the dynamic behaviors of mechanical nonlinear systems. The research results prove the effectiveness of the proposed method in modal identification, system dynamics evolution and fault diagnosis of nonlinear systems. It is of great significance to extend these studies to the field of mechanical nonlinear system dynamics.

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Bearing life prognosis based on monotonic feature selection and similarity modeling

Cited by 19 publications

References 23 publications

Combination of envelope spectra and generative topographic mapping to diagnose bearing fault and evaluate degradation of bearing

Combination of envelope spectra and generative topographic mapping to diagnose bearing fault and evaluate degradation of bearing

Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression

Information Dynamic Correlation of Vibration in Nonlinear Systems

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