Efficient closed-form estimation of multivariate moving-average processes using higher order statistics

1994 ◽  
Vol 42 (8) ◽  
pp. 2197-2200 ◽  
Author(s):  
Hong Chen ◽  
Tao Chen ◽  
Tianping Chen
Keyword(s):  
Closed Form ◽  
Order Statistics ◽  
Moving Average ◽  
Higher Order ◽  
Higher Order Statistics ◽  
Moving Average Processes

Bearing Fault Detection Using Higher-Order Statistics Based ARMA Model

2007 ◽  
Vol 347 ◽  
pp. 271-276 ◽  
Author(s):  
Fu Cai Li ◽  
Lin Ye ◽  
Gui Cai Zhang ◽  
Guang Meng
Keyword(s):  
Fault Detection ◽  
Order Statistics ◽  
Moving Average ◽  
Arma Model ◽  
Vibration Signal ◽  
Higher Order ◽  
Accurate Estimation ◽  
Higher Order Statistics ◽  
Identification Technique ◽  
Non Gaussian

Impulse response provides important information about flaws in mechanical system. Deconvolution is one system identification technique for fault detection when signals captured from bearings with and without flaw are both available. However effects of measurement systems and noise are obstacles to the technique. In the present study, a model, namely autoregressive-moving average (ARMA), is used to estimate vibration pattern of rolling element bearings for fault detection. The frequently used ARMA estimator cannot characterize non-Gaussian noise completely. Aimed at circumventing the inefficiency of the second-order statistics-based ARMA estimator, higher-order statistics (HOS) was introduced to ARMA estimator, which eliminates the effect of noise greatly and, therefore, offers more accurate estimation of the system. Furthermore, bispectrums of the estimated HOS-based ARMA models were subsequently applied to get clearer information. Impulse responses of signals captured from the test bearings without and with flaws and their bispectra were compared for the purpose of fault detection. The results demonstrated the excellent capability of this method in vibration signal processing and fault detection.

Evaluation of a new Power Quality index, based in Higher Order Statistics

2017 ◽  
Vol 1 (15) ◽  
pp. 37-42
Author(s):  
J.M. Sierra-Fernández ◽  
J.J. González De La Rosa ◽  
A. Agüera-Pérez ◽  
J.C. Palomares Salas ◽  
O. Florencias-Oliveros
Keyword(s):  
Order Statistics ◽  
Power Quality ◽  
Quality Index ◽  
Higher Order ◽  
Higher Order Statistics
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