scholarly journals Nearest neighbor and learning vector quantization classification for damage detection using time series analysis

2009 ◽  
pp. n/a-n/a ◽  
Author(s):  
Oliver R. de Lautour ◽  
Piotr Omenzetter
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Detection ◽  
Vector Quantization ◽  
Nearest Neighbor ◽  
Learning Vector Quantization ◽  
Series Analysis

scholarly journals VIBRATION-BASED DAMAGE DETECTION IN FLEXIBLE RISERS USING TIME SERIES ANALYSIS

2007 ◽  
Vol 24 (2) ◽  
pp. 62s-72s
Author(s):  
Carlos RIVEROS ◽  
Tomoaki UTSUNOMIYA ◽  
Katsuya MAEDA ◽  
Kazuaki ITOH
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Detection ◽  
Series Analysis ◽  
Flexible Risers

scholarly journals A Novel Damage Detection Algorithm using Time-Series Analysis-Based Blind Source Separation

2013 ◽  
Vol 20 (3) ◽  
pp. 423-438 ◽  
Author(s):  
A. Sadhu ◽  
B. Hazra
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Detection ◽  
Blind Source Separation ◽  
Source Separation ◽  
Damage Index ◽  
Detection Algorithm ◽  
Mode Shapes ◽  
Series Analysis ◽  
Modal Response

In this paper, a novel damage detection algorithm is developed based on blind source separation in conjunction with time-series analysis. Blind source separation (BSS), is a powerful signal processing tool that is used to identify the modal responses and mode shapes of a vibrating structure using only the knowledge of responses. In the proposed method, BSS is first employed to estimate the modal response using the vibration measurements. Time-series analysis is then performed to characterize the mono-component modal responses and successively the resulting time-series models are utilized for one-step ahead prediction of the modal response. With the occurrence of newer measurements containing the signature of damaged system, a variance-based damage index is used to identify the damage instant. Once the damage instant is identified, the damaged and undamaged modal parameters of the system are estimated in an adaptive fashion. The proposed method solves classical damage detection issues including the identification of damage instant, location as well as the severity of damage. The proposed damage detection algorithm is verified using extensive numerical simulations followed by the full scale study of UCLA Factor building using the measured responses under Parkfield earthquake.

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.

scholarly journals VIBRATION-BASED DAMAGE DETECTION IN FLEXIBLE RISERS USING TIME SERIES ANALYSIS

2007 ◽  
Vol 63 (3) ◽  
pp. 423-433 ◽  
Author(s):  
Carlos RIVEROS ◽  
Tomoaki UTSUNOMIYA ◽  
Katsuya MAEDA ◽  
Kazuaki ITOH
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Detection ◽  
Series Analysis ◽  
Flexible Risers
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