scholarly journals A time series model-based method for gear tooth crack detection and severity assessment under random speed variation

2021 ◽  
Vol 156 ◽  
pp. 107605
Author(s):  
Yuejian Chen ◽  
Stephan Schmidt ◽  
P. Stephan Heyns ◽  
Ming J. Zuo
Keyword(s):  
Time Series ◽  
Crack Detection ◽  
Gear Tooth ◽  
Time Series Model ◽  
Severity Assessment ◽  
Speed Variation ◽  
Model Based

Gear Damage Detection Using Chaotic Dynamics Techniques: A Preliminary Study

1995 ◽  
Author(s):  
M. Farid Golnaraghi ◽  
DerChyan Lin ◽  
Paul Fromme
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Detection ◽  
Correlation Dimension ◽  
Chaotic Dynamics ◽  
Crack Detection ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Nonlinear Time Series Analysis ◽  
Preliminary Study

Abstract This paper is a preliminary study applying nonlinear time series analysis to crack detection in gearboxes. Our investigations show that the vibration signal emerging from a gearbox is chaotic. Appearance of a crack in a gear tooth alters this response and hence the chaotic signature. We used correlation dimension and Lyapunov exponents to quantify this change. The main goal of this study is to point out the great potential of these methods in detection of cracks and faults in machinery.

Time Series Model Based Earning Forecasting

2013 ◽  
Vol 791-793 ◽  
pp. 2147-2150
Author(s):  
Xiang Rong Jiang ◽  
Ying Ying Cui
Keyword(s):  
Time Series ◽  
Empirical Study ◽  
Relative Stability ◽  
Listed Companies ◽  
Time Series Model ◽  
New Method ◽  
Time Series Models ◽  
Seasonal Patterns ◽  
Model Based ◽  
Over Time

We propose a procedure to forecast earning of listed companies. It is a modification of method developed for forecasting series with stable seasonal patterns. The new method is motivated by the observations that seasonal patterns, which may be evolving over time and remain relative stability, arise in finance market. The method can be applied to forecast individual observations as well as the end-of-season totals. Empirical study will be conducted with data from finance market to evaluate the performance of the proposed method. The new method is proved more effective than traditional time series models.

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