Research on Performance Degradation Assessment Method of Train Rolling Bearings under incomplete data

  title={Research on Performance Degradation Assessment Method of Train Rolling Bearings under incomplete data},
  author={Yong Qin and Dandan Wang and Zhipeng Wang and Limin Jia and Xuejun Zhao},
This paper mainly discusses the performance degradation assessment of train rolling bearings under incomplete data, by using the support vector data description (SVDD) and dynamic particle swarm optimization (DPSO).The proposed method is based on the similarity weight for the assessment of the train rolling bearings under incomplete data. Firstly, to obtain effective features of bearing performance degradation from collected vibration data, the local mean decomposition (LMD) is employed to… 

Figures from this paper

Quantitative Method of Health State Assessment Inside the Safe Region
  • Yuhua Yin, Zhiliang Liu, Zhe Cheng
  • Computer Science
    2020 Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modeling (APARM)
  • 2020
A novel method, which consists of center optimization based on genetic algorithm, boundary adjustment based on support vector data description and residual healthy life prediction based on regressive analysis, to realize the health state assessment is presented.


Performance degradation assessment by Kolmogorov-Smirnov test and prognosis based on AR model
Equipment performance degradation assessment can give effective reference to intelligent proactive maintenance to realize near-zero downtime.By carrying out the research on performance degradation
Application of the Wavelet-SOFM Network in Roll Bearing Defect Diagnosis
  • Wei He, Xiang Zhou
  • Engineering
    2009 WRI Global Congress on Intelligent Systems
  • 2009
An approach for roll bearing fault diagnosis using neural networks and time/frequency-domain bearing vibration analysis is presented and results indicate that neural networks can be effective agents in the diagnosis of various bearing faults through the measurement and interpretation of bearing vibration signal.
Fault Diagnosis Method of Rolling Bearing Based on AFD Algo rithm
Adaptive Fourier decomposition(AFD) algorithm decomposes the vibratio n signal of rolling bearing into a series of mono-components,and the kurtosis o f each mono-component is calculated.The kurtosis
Fault diagnosis method for rolling bearing of metro vehicle based on EMD and SVM
Aiming at the problem of fault diagnosis for rolling bearing of metro vehicle, a method combined empirical mode decomposition(EMD), with support vector machine(SVM) was proposed. Firstly, the
Intelligent Monitoring System for Screw Life Evaluation
The effects of rotating speed and load on screw life were investigated to study the performance degradation of screws of NC(numerical control) machine tools under different machining conditions.The
Application of improved Hilbert-Huang transform method in gear fault diagnosis
The first intrinsic mode function(IMF) is generally a multi-component as the result of the abundant frequency component in gear fault signal.Pseudocomponents in low frequency may be produced in
Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of FFT, CWT, and DWT features
This paper presents a novel method to detect, recognize, and classify a variety of railroad wheel-bearing defects using audible acoustic signals at several different train speeds.
Local mean decomposition method based on B-spline interpolation
The local mean decomposition (LMD) is a relatively new approach of adaptive signal analysis.The core of LMD is to decompose an original signal into several production functions (PF),and each of which
[Integration of soft and hard classifications using linear spectral mixture model and support vector machines].
Experimental results prove that the new soft and hard classification method can effectively solve the problem of mixed pixels, and can obviously improve image classification accuracy.