Cooling fan bearing diagnosis based on AR&amp;amp; MED

Liu, Chaoqin; Zhou, Xue; Yang, Shuai; Liang, Wei; Miao, Qiang

doi:10.1109/icqr2mse.2012.6246310

Cited by 5 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is critical to determine the order number of the AR model, because the accuracy of the order not only affects the accuracy of identification of the system, but also influences the stability of the system. In order to estimate the order of the AR model correctly, FPE criterion, BIC criterion, and AIC criterion are usually used [15], and they are expressed as FPE criterion…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on CEEMD and Time Series Modeling

Zhao

Wei

Yan

2014

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Accurately identifying faults in rolling bearing systems by analyzing vibration signals, which are often nonstationary, is challenging. To address this issue, a new approach based on complementary ensemble empirical mode decomposition (CEEMD) and time series modeling is proposed in this paper. This approach seeks to identify faults appearing in a rolling bearing system using proper autoregressive (AR) model established from the nonstationary vibration signal. First, vibration signals measured from a rolling bearing test system with different defect conditions are decomposed into a set of intrinsic mode functions (IMFs) by means of the CEEMD method. Second, vibration signals are filtered with calculated filtering parameters. Third, the IMF which is closely correlated to the filtered signal is selected according to the correlation coefficient between the filtered signal and each IMF, and then the AR model of the selected IMF is established. Subsequently, the AR model parameters are considered as the input feature vectors, and the hidden Markov model (HMM) is used to identify the fault pattern of a rolling bearing. Experimental study performed on a bearing test system has shown that the presented approach can accurately identify faults in rolling bearings.

show abstract

Section: Theoretical Frameworkmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on CEEMD and Time Series Modeling

Zhao

Wei

Yan

2014

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The use of AR model coefficients has been reported in some works; for instance, in [21,22] bearing faults are detected, and in [23,24] faults in gearbox coupling systems are detected. In these works, coefficients are used to estimate the power spectral density (PSD) from the transfer function of the AR model.…”

Section: Introductionmentioning

confidence: 99%

Stator Fault Detection in Induction Motors by Autoregressive Modeling

Garcia-Guevara

Villalobos-Piña

Álvarez-Salas

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This study introduces a novel methodology for early detection of stator short circuit faults in induction motors by using autoregressive (AR) model. The proposed algorithm is based on instantaneous space phasor (ISP) module of stator currents, which are mapped toα-βstator-fixed reference frame; then, the module is obtained, and the coefficients of the AR model for such module are estimated and evaluated by order selection criterion, which is used as fault signature. For comparative purposes, a spectral analysis of the ISP module by Discrete Fourier Transform (DFT) is performed; a comparison of both methodologies is obtained. To demonstrate the suitability of the proposed methodology for detecting and quantifying incipient short circuit stator faults, an induction motor was altered to induce different-degree fault scenarios during experimentation.

show abstract