Rotating Machinery Condition Monitoring Using Time Series Analysis of Vibration Signal

2021 ◽  
pp. 232-242
Author(s):  
Abdellah Chehri ◽  
Alfred Zimmermann ◽  
Wend-Benedo Zoungrana ◽  
Hassan Ezzaidi
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Condition Monitoring ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Series Analysis ◽  
Machinery Condition Monitoring

The Mechanical Fault Diagnosis Method Based on EMD and Time Series Analysis

2014 ◽  
Vol 602-605 ◽  
pp. 2330-2333 ◽  
Author(s):  
Jun Ma ◽  
Shi Hai Zhang
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Time Series Analysis ◽  
Mechanical Vibration ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Analysis Method ◽  
Series Analysis ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

It is the precondition of vibration fault diagnosis technology that appropriate signal analysis method is applied to separate mechanical fault character message from vibration monitoring signal. Based on the characteristics of multi-exciting, multi-model, non-stationary, nonlinear of the complex mechanical vibration signal, the EMD (Empirical Mode Decomposition) method is firstly applied to decompose the most refined IMF (Intrinsic Mode Function) components of vibration monitoring signal, and then the time series analysis method is applied to estimate power spectrums of IMF components and separate the fault character messages. The feasibility and advantage of the associated method are proved by analyzing the diesel engine crankshaft vibration monitoring signal in the paper.

Nearest Neighbor-Time Series Analysis Classification of Faults in Rotating Machinery

1983 ◽  
Vol 105 (2) ◽  
pp. 178-184 ◽  
Author(s):  
W. Gersch ◽  
T. Brotherton ◽  
S. Braun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Nearest Neighbor ◽  
Mean Value ◽  
Rotating Machinery ◽  
Stationary Time Series ◽  
Sensor Data ◽  
Series Analysis ◽  
Probability Of Misclassification

A unified nearest neighbor-time series analysis approach to the problem of the classification of faults in rotating machinery is developed. The procedure has an optimum minimum probability of misclassification property for normally distributed time series and near optimum misclassification properties otherwise. Examples of the classification of acceleration, pressure, and torque sensor data from stationary, locally stationary, and covariance stationary time series with mean value time functions are considered. Estimates of the probability of misclassification are computed for each situation. The underlying assumptions and properties of the nearest neighbor time series classification procedure and signature analysis procedures are compared.

Applications of fault diagnosis in rotating machinery by using time series analysis with neural network

2010 ◽  
Vol 37 (2) ◽  
pp. 1696-1702 ◽  
Author(s):  
Chun-Chieh Wang ◽  
Yuan Kang ◽  
Ping-Chen Shen ◽  
Yeon-Pun Chang ◽  
Yu-Liang Chung
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Time Series Analysis ◽  
Rotating Machinery ◽  
Series Analysis
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