Intelligent Infrastructure Support Software Using Singular Spectrum Analysis and Artificial Neural Networks: Applications on Transportation Forecasting

2020 ◽  
pp. 449-461
Author(s):  
S. Z. Kolidakis ◽  
G. N. Botzoris ◽  
V. A. Profillidis ◽  
V. D. Balopoulos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Artificial Neural ◽  
Support Software

scholarly journals Fault diagnosis of rolling bearings using singular spectrum analysis and artificial neural networks

2021 ◽  
Vol 43 (2) ◽  
pp. 183-196
Author(s):  
Quang Thinh Tran ◽  
Kieu Nhi Ngo ◽  
Sy Dzung Nguyen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Frequency Noise ◽  
Singular Spectrum ◽  
Experimental Apparatus ◽  
Artificial Neural ◽  
Set Up

Singular spectrum analysis (SSA) has been employed effectively for analyzing in the time-frequency domain of time series. It can collaborate with data-driven models (DDMs) such as Artificial Neural Networks (ANN) to set up a powerful tool for mechanical fault diagnosis (MFD). However, to take advantage of SSA more effectively for MFD, quantifying the optimal component threshold in SSA should be addressed. Also, to exploit the managed mechanical system adaptively, the variation tendency of its physical parameters needs to be caught online. Here, we present a bearing fault diagnosis method (BFDM) based on ANN and SSA that targets these aspects. First, a multi-feature is built from pure mechanical properties distilled from the vibration signal of the system. Relied on SSA, the measured acceleration signal is analyzed to cancel the high-frequency noise. The remaining components take part in building a multi-feature to establish a database for training the ANN. Optimizing the number of the kept components is then carried out to obtain a dataset called Tr_Da. Based on Tr_Da, we receive the optimal ANN (OANN). In the next period, at each checking time, another database called Test_Da is set up online following the same way of building the Tr_Da. The compared result between the encoded output and the output of the OANN corresponding to the input to be Test_Da provides the bearing(s) health information. An experimental apparatus is built to evaluate the BFDM. The obtained results reflect the positive effects of the method.

scholarly journals Identifying undamaged-beam status based on singular spectrum analysis and wavelet neural networks

2016 ◽  
Vol 31 (4) ◽  
pp. 341
Author(s):  
Dung Sy Nguyen ◽  
Hung Quoc Nguyen ◽  
Nhi Kieu Ngo
Keyword(s):  
Neural Networks ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Vibration Signal ◽  
Measuring System ◽  
Wavelet Frame ◽  
Wavelet Neural Networks ◽  
Singular Spectrum ◽  
Frame Building ◽  
Experimental Apparatus

In this paper, the identifying undamaged-beam status  based on singular spectrum analysis (SSA) and wavelet neural networks (WNN)  is presented. First, a database is built from measured sets and SSA which  works as a frequency-based filter. A WNN model is then designed which consists of the wavelet frame building, wavelet structure designing and  establishing a solution for training the WNN. Surveys via an experimental  apparatus for estimating the method are carried out. In this work, a  beam-typed iron frame, Micro-Electro-Mechanical (MEM) sensors and a  vibration-signal processing and measuring system named LAM_BRIDGE are all  used.

scholarly journals Time series forecasting using singular spectrum analysis, fuzzy systems and neural networks

MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 101015
Author(s):  
Winita Sulandari ◽  
S. Subanar ◽  
Muhammad Hisyam Lee ◽  
Paulo Canas Rodrigues
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Spectrum Analysis ◽  
Fuzzy Systems ◽  
Singular Spectrum Analysis ◽  
Time Series Forecasting ◽  
Singular Spectrum

scholarly journals Real-time Intraday Traffic Volume Forecasting – A Hybrid Application Using Singular Spectrum Analysis and Artificial Neural Networks

2019 ◽  
Vol 48 (3) ◽  
pp. 226-235 ◽  
Author(s):  
Stylianos Kolidakis ◽  
George Botzoris ◽  
Vassilios Profillidis ◽  
Alexandros Kokkalis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spectrum Analysis ◽  
Mean Squared Error ◽  
Singular Spectrum Analysis ◽  
Traffic Volume ◽  
Forecasting Accuracy ◽  
Squared Error ◽  
Hybrid Forecasting ◽  
Toll Station

The present paper provides a comparative evaluation of hybrid Singular Spectrum Analysis (SSA) and Artificial Neural Networks (ANN) against conventional ANN, applied on real time intraday traffic volume forecasting. The main research objective was to assess the applicability and functionality of intraday traffic volume forecasting, based on toll station measurements. The proposed methodology was implemented and evaluated upon a custom developed forecasting software toolbox, based on the software Mathworks MatLab, by using real data from Iasmos-Greece toll station. Experimental results demonstrated a superior ex post forecasting accuracy of the proposed hybrid forecasting methodology against conventional ANN, when compared to performance of usual statistical criteria (Mean Absolute Error, Mean Squared Error, Root Mean Squared Error, Coefficient of Determination R2, Theil's inequality coefficient). The obtained results revealed that the hybrid model could advance forecasting accuracy of a conventional ANN model in intraday traffic volume forecasting, while embedding hybrid forecasting algorithm in an Intelligent Transport System could provide an advanced decision support module for transportation system maintenance, operation and management.

scholarly journals A hybrid model coupled with singular spectrum analysis for daily rainfall prediction

2010 ◽  
Vol 12 (4) ◽  
pp. 458-473 ◽  
Author(s):  
K. W. Chau ◽  
C. L. Wu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Regression ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Daily Rainfall ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Singular Spectrum ◽  
Artificial Neural

A hybrid model integrating artificial neural networks and support vector regression was developed for daily rainfall prediction. In the modeling process, singular spectrum analysis was first adopted to decompose the raw rainfall data. Fuzzy C-means clustering was then used to split the training set into three crisp subsets which may be associated with low-, medium- and high-intensity rainfall. Two local artificial neural network models were involved in training and predicting low- and medium-intensity subsets whereas a local support vector regression model was applied to the high-intensity subset. A conventional artificial neural network model was selected as the benchmark. The artificial neural network with the singular spectrum analysis was developed for the purpose of examining the singular spectrum analysis technique. The models were applied to two daily rainfall series from China at 1-day-, 2-day- and 3-day-ahead forecasting horizons. Results showed that the hybrid support vector regression model performed the best. The singular spectrum analysis model also exhibited considerable accuracy in rainfall forecasting. Also, two methods to filter reconstructed components of singular spectrum analysis, supervised and unsupervised approaches, were compared. The unsupervised method appeared more effective where nonlinear dependence between model inputs and output can be considered.

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