Track segment automated characterisation via railway–vehicle–based random vibration signals and statistical time series methods

2021 ◽  
pp. 1-22
Author(s):  
I. A. Iliopoulos ◽  
J. S. Sakellariou ◽  
S. D. Fassois
Keyword(s):  
Time Series ◽  
Random Vibration ◽  
Railway Vehicle ◽  
Vibration Signals ◽  
Track Segment ◽  
Statistical Time

AR Bispectrum Characteristics of Block Forming Machine’s Vibration Driven by Hydraulic Exciter

2011 ◽  
Vol 295-297 ◽  
pp. 2249-2253
Author(s):  
Jin Song Zhuang ◽  
Yi Jian Huang ◽  
Fu Sen Wu
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Spectrum Analysis ◽  
Gaussian Noise ◽  
Random Vibration ◽  
Work Conditions ◽  
Nonlinear Coupling ◽  
Vibration Signals ◽  
Order Spectrum ◽  
Sample Data

Block forming machine, as a kind of automatic equipments, can quickly compact blocks. Higher-order spectrum analysis emerges as a new effective method in signal processing, which can describe nonlinear coupling, restrain Gaussian noise and reserve phase components. In the paper, a hydraulic exciter applying to block forming machine will be introduced. Then block forming machine’s random vibration signals during the compacting process would be collected, in order to make use of the sample data to build up a time series autoregressive model and bispectrum of three-order accumulation, to analyze AR bispectrum characteristics of the machine’s vibrate signals under different work conditions.

Modeling Machine Tool Chatter by Time Series

1975 ◽  
Vol 97 (1) ◽  
pp. 211-215 ◽  
Author(s):  
S. M. Pandit ◽  
T. L. Subramanian ◽  
S. M. Wu
Keyword(s):  
Time Series ◽  
Machine Tool ◽  
Random Vibration ◽  
Vibration Signals ◽  
Forcing Function ◽  
Machine Tool Chatter ◽  
Random Disturbances ◽  
Noise Forcing ◽  
Modeling Data ◽  
Tool Chatter

Machine tool chatter is formulated as self-excited random vibration with white noise forcing function. The formulation takes into account the unknown factors and random disturbances present in the cutting process when chatter occurs. Based on this formulation, a procedure for modeling chatter using the time series of sampled observations on vibration signals is developed. Feasibility of this procedure is established by modeling data obtained from a turning operation under conditions of severe chatter.

scholarly journals A Study on The Genesis of "F" Waves of Atrial Flutter in Man Considered as Multiple Statistical Time Series

1980 ◽  
Vol 21 (1) ◽  
pp. 73-83
Author(s):  
Etsu HASHIDA ◽  
Naohiro YOSHITANI ◽  
Takenobu TASAKI
Keyword(s):  
Time Series ◽  
Atrial Flutter ◽  
Statistical Time

Multiscale convolutional neural network and decision fusion for rolling bearing fault diagnosis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Defeng Lv ◽  
Huawei Wang ◽  
Changchang Che
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Decision Fusion ◽  
Fusion Model ◽  
Vibration Signals ◽  
Content Type ◽  
Proposed Model

Purpose The purpose of this study is to achieve an accurate intelligent fault diagnosis of rolling bearing. Design/methodology/approach To extract deep features of the original vibration signal and improve the generalization ability and robustness of the fault diagnosis model, this paper proposes a fault diagnosis method of rolling bearing based on multiscale convolutional neural network (MCNN) and decision fusion. The original vibration signals are normalized and matrixed to form grayscale image samples. In addition, multiscale samples can be achieved by convoluting these samples with different convolution kernels. Subsequently, MCNN is constructed for fault diagnosis. The results of MCNN are put into a data fusion model to obtain comprehensive fault diagnosis results. Findings The bearing data sets with multiple multivariate time series are used to testify the effectiveness of the proposed method. The proposed model can achieve 99.8% accuracy of fault diagnosis. Based on MCNN and decision fusion, the accuracy can be improved by 0.7%–3.4% compared with other models. Originality/value The proposed model can extract deep general features of vibration signals by MCNN and obtained robust fault diagnosis results based on the decision fusion model. For a long time series of vibration signals with noise, the proposed model can still achieve accurate fault diagnosis.

Soft Computing Based Statistical Time Series Analysis, Characterization of Chaos Theory, and Theory of Fractals

2013 ◽  
pp. 30-61 ◽  
Author(s):  
Mofazzal H. Khondekar ◽  
Dipendra N. Ghosh ◽  
Koushik Ghosh ◽  
Anup Kumar Bhattacharya
Keyword(s):  
Time Series ◽  
Fractal Dimension ◽  
Time Series Analysis ◽  
Soft Computing ◽  
Chaos Theory ◽  
Filter Design ◽  
Theoretical Perspective ◽  
Series Analysis ◽  
Statistical Time

The present work is an attempt to analyze the various researches already carried out from the theoretical perspective in the field of soft computing based time series analysis, characterization of chaos, and theory of fractals. Emphasis has been given in the analysis on soft computing based study in prediction, data compression, explanatory analysis, signal processing, filter design, tracing chaotic behaviour, and estimation of fractal dimension of time series. The present work is a study as a whole revealing the effectiveness as well as the shortcomings of the various techniques adapted in this regard.

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