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 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 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 A Novel approach for predicting monthly water demand by combining singular spectrum analysis with neural networks

2018 ◽  
Vol 561 ◽  
pp. 136-145 ◽  
Author(s):  
Salah L. Zubaidi ◽  
Jayne Dooley ◽  
Rafid M. Alkhaddar ◽  
Mawada Abdellatif ◽  
Hussein Al-Bugharbee ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spectrum Analysis ◽  
Water Demand ◽  
Singular Spectrum Analysis ◽  
Singular Spectrum ◽  
Novel Approach

scholarly journals Short-Term Net Load Forecasting with Singular Spectrum Analysis and LSTM Neural Networks

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4107
Author(s):  
Akylas Stratigakos ◽  
Athanasios Bachoumis ◽  
Vasiliki Vita ◽  
Elias Zafiropoulos
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Spectrum Analysis ◽  
Short Term Memory ◽  
Predictive Accuracy ◽  
Singular Spectrum Analysis ◽  
Hybrid Approach ◽  
Load Forecasting ◽  
Short Term ◽  
Singular Spectrum

Short-term electricity load forecasting is key to the safe, reliable, and economical operation of power systems. An important challenge that arises with high-frequency load series, e.g., hourly load, is how to deal with the complex seasonal patterns that are present. Standard approaches suggest either removing seasonality prior to modeling or applying time series decomposition. This work proposes a hybrid approach that combines Singular Spectrum Analysis (SSA)-based decomposition and Artificial Neural Networks (ANNs) for day-ahead hourly load forecasting. First, the trajectory matrix of the time series is constructed and decomposed into trend, oscillating, and noise components. Next, the extracted components are employed as exogenous regressors in a global forecasting model, comprising either a Multilayer Perceptron (MLP) or a Long Short-Term Memory (LSTM) predictive layer. The model is further extended to include exogenous features, e.g., weather forecasts, transformed via parallel dense layers. The predictive performance is evaluated on two real-world datasets, controlling for the effect of exogenous features on predictive accuracy. The results showcase that the decomposition step improves the relative performance for ANN models, with the combination of LSTM and SAA providing the best overall performance.

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