Probability Density Estimation and Bayesian Causal Analysis Based Fault Detection and Root Identification

2018 ◽  
Vol 57 (43) ◽  
pp. 14656-14664 ◽  
Author(s):  
Xiaolu Chen ◽  
Jing Wang ◽  
JingLin Zhou
Keyword(s):  
Fault Detection ◽  
Probability Density ◽  
Density Estimation ◽  
Causal Analysis ◽  
Probability Density Estimation

A Probability Density Estimation for Fault Detection

2012 ◽  
Vol 562-564 ◽  
pp. 1113-1116 ◽  
Author(s):  
Zhe Min Zhuang ◽  
Fen Lan Li ◽  
Chu Liang Wei
Keyword(s):  
Fault Detection ◽  
Probability Density ◽  
Density Estimation ◽  
Estimation Method ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Probability Density Estimation ◽  
Time Frequency ◽  
Parzen Window ◽  
Rotating Machine

In this paper, a time-domain analysis method based on probability density estimation is presented for rotating machine fault detection. Generally, the vibration signals obtained from a rotating machine are time-variant since they are strongly related to the rotational speed that is not constant even in the macro steady state. Since the mostly used signal processing method, the Fourier analysis is only suitable for stationary signals, the development of the joint time-frequency analysis is demanded. Here, the probability density estimation method based on Parzen window is introduced. The probability density function of the vibration signal of the rotating machine is estimated by Parzen window, and a threshold value is predefined to decide the state of the rotating machine. By inspecting the change of the probability density of the vibration signal, the condition of the machine is monitored. Air gap eccentricity and ball cage broken bearing are considered in the experiment section, they are difficult to be detected in frequency domain while the rotating speed is not constant. The validity of the method is proved by the experiment.

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