scholarly journals MIMO LS-SVR-Based Multi-Point Vibration Response Prediction in the Frequency Domain

2020 ◽  
Vol 10 (24) ◽  
pp. 8784
Author(s):  
Cheng Wang ◽  
Delei Chen ◽  
Haiyang Huang ◽  
Wei Zhan ◽  
Xiongming Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Linear Regression ◽  
Frequency Domain ◽  
Matrix Method ◽  
Response Prediction ◽  
Vibration Response ◽  
Data Driven ◽  
Training Dataset ◽  
Network Method

To predict the multi-point vibration response in the frequency domain when the uncorrelated multi-source loads are unknown, a data-driven and multi-input multi-output least squares support vector regression (MIMO LS-SVR)-based method in the frequency domain is proposed. Firstly, the relationship between the measured multi-point vibration response and unmeasured multi-point vibration response is formulated using the transfer function in the frequency domain. Secondly, the data-driven multiple regression analysis problem of multi-point vibration response prediction in the frequency domain is described formally, and its mathematical model is established. With the measured multi-point vibration response as the input and the unmeasured multi-point vibration response as the output, the vibration response history data are assembled as a MIMO training dataset at each frequency. Thirdly, using the MIMO LS-SVR algorithm and MIMO history training dataset, the multi-point vibration response prediction model is built at each frequency point. By comparing the transmissibility matrix method, multiple linear regression model-based method, and MIMO neural network method, the application scope of the proposed method and its advantages are analyzed. The experimental results for acoustic and vibration experiment on a cylindrical shell verified that the MIMO LS-SVR-based method predicts the multi-point vibration response effectively when the loads are unknown, and has higher precision than the transfer function method, multiple linear regression method, MIMO neural network method, and transmissibility matrix method.

scholarly journals Multipoint Vibration Response Prediction under Uncorrelated Multiple Sources Load Based on Elastic-Net Regularization in Frequency Domain

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
ZhenKai Cui ◽  
Cheng Wang ◽  
Jianwei Chen ◽  
Ting He
Keyword(s):  
Linear Regression ◽  
Frequency Domain ◽  
Response Prediction ◽  
Acoustic Vibration ◽  
Vibration Response ◽  
Elastic Net ◽  
Multiple Sources ◽  
Regression Methods ◽  
Elastic Net Regularization ◽  
Working Situation

In order to solve the problems of large number of conditions at inherent frequencies and low prediction accuracy when using multiple multivariate linear regression methods for vibration response prediction alone, an elastic-net regularization method is proposed. Firstly, a multi-input and multioutput linear regression model of the multipoint frequency domain vibration response is trained using historical data at each frequency point. Secondly, the trained model under each frequency point is improved by the elastic regularization. Finally, the model is used in a working situation. The predicted vibration response on the experimental dataset of cylindrical shell acoustic vibration showed that the improvement of the multivariate regression vibration response prediction model by elastic regularization can better improve the accuracy and reduce the large number of conditions at some frequencies.

Railway crossing vertical vibration response prediction using a data-driven neuro-fuzzy model – Influence of train factors

2021 ◽  
pp. 095440972098666
Author(s):  
Kaveh Mehrzad ◽  
Shervan Ataei
Keyword(s):  
Model Comparison ◽  
Selection Process ◽  
Fuzzy Model ◽  
Response Prediction ◽  
Vibration Response ◽  
Vibration Monitoring ◽  
Data Driven ◽  
Steady Increase ◽  
Neuro Fuzzy ◽  
Railway Crossing

This paper provides a data-driven model of the vibration response of a railway crossing during vehicle passages. Many of the features of trains passing through instrumented crossing are extracted from measured data. Based on the feature selection process, speed, dynamic axle load and the number of wagons are found proper inputs in the prediction model. Train-crossing interaction response at a crossing due to passing trains is modeled from a data-driven Neuro-Fuzzy soft computing approach. Locally Linear Model Tree (LOLIMOT) is applied to predict the crossing nose acceleration. The model comparison against measurements shows that the ability to predict the extrapolation cases at off-range speeds has satisfactory compatibility. The monitored passing trains are ranked based on the LOLIMOT input space dimension cuts and extrapolation of the model up to higher train speeds. The influence of train factors (i.e. speed, dynamic axle load, number of wagons) on crossing response is demonstrated. Also, based on the analysis results, it is concluded that with a steady increase in train speeds, some trains show a greater amplification in vibration response than others. The results can be applied in data processing in the crossing vibration monitoring and detection of trains with crossing impact sensitive to speed increasing that can lead to proper operation policies to reduce damages and maintenance costs.

scholarly journals Data-Driven Air-Cooled Condenser Performance Assessment: Model and Input Variable Selection Comparison

2020 ◽  
Vol 197 ◽  
pp. 10003
Author(s):  
Simone Ghettini ◽  
Alessandro Sorce ◽  
Roberto Sacile
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Ambient Temperature ◽  
Mass Flow Rate ◽  
Mass Flow ◽  
Flow Rate ◽  
Empirical Model ◽  
Power Plants ◽  
Data Driven ◽  
Semi Empirical

This paper presents a data–driven model for the estimation of the performance of an aircooled steam condenser (ACC) with the aim to develop an efficient online monitoring, summarized by the condenser pressure (or vacuum) as Key Performance Indicator. The estimation of the ACC performance model was based on different dataset from three different combined cycle power plants with a gross power of above 380 MWe each, focusing on stationary condition of the steam turbine. The datasets include both boundary (e.g. Ambient Temperature, Wind Speed) and operative parameters (e.g. steam mass flow rate, Steam turbine power, electrical load of the ACC fans) acquired from the power plants and some derived variable as the incondensable fraction, which calculation is here proposed as additional parameter. After a preliminary sensitivity analysis on data correlation, the paper focuses on the evaluation of different ACC Condenser models: Semi-Empirical model is described trough curves typically based on steam mass flow rate (or condenser load) and the ambient temperature as main parameters. Since monitoring based on ACC design curves Semi-Empirical models, provides biased poor results, with an error of about 15%, the curves parameters were estimated basing on training data set. Other two data driven models were presented, basing on a neural network modelling and multi linear regression technique and compared on the base of the reduced number of input at first and then including aldo the other process variables in the prediction of the condenser back pressure. Estimate the parameters of the Semi-Empirical model, results in a better prediction if just steam mass flow rate and ambient temperature are available, with an error of the 7%, thanks to the knowledge contained within the “curves shapes”, with respect to linear regression (8.3%) and Neural Network models (7.6%). Higher accuracy can be then obtained by considering a larger number of operative parameters and exploiting more complex data-driven model. With a higher number of features, the neural network model has proved a higher accuracy than the linear regression model. In fact, the mean percentage error of the NN model (2.6%), in all plant operating conditions, is slightly lower than the error of the linear regression model, but presents and much lower than the mean error of the Semi-Empirical model thanks to the additional data-based knowledge.

A convolutional neural network for nowcasting rainfall intensities at fine temporal and spatial scales

2020 ◽  
Author(s):  
Bora Shehu ◽  
Malkin Gerchow ◽  
Uwe Haberlandt
Keyword(s):  
Neural Network ◽  
Spatial Scales ◽  
Radar Data ◽  
Data Driven ◽  
Training Dataset ◽  
Lead Times ◽  
Linear Transformations ◽  
Urban Flood ◽  
Deep Cnn ◽  
Temporal And Spatial

<p>The short term forecast of rainfall intensities for fine temporal and spatial resolutions, has always been challenging due to the unpredictable nature of rainfall. Commonly at such scales, radar data are employed to track and extrapolate rainfall storms in the future. For very short lead times, the Lagrange persistence can produce reliable results up to 20 min whilst for longer lead times hybrid models are necessary, in order to account for the birth, death and non-linear transformations of storms that might increase the predictability of rainfall. Recently, data driven techniques, are gaining popularity due to their high learning skills, although their performance is highly dependent on the size of the training dataset and don’t include any physical background. Thus the aim of this study is to investigate the use of data driven techniques in increasing the predictability of rainfall forecast at very fine scales.</p><p>For this purpose, a deep convolutional artificial neural network (CNN) is employed to predict rainfall intensities at 5min and 1km<sup>2</sup> resolutions for the Hannover radar range area at lead times from 5min to 3 hours. The deep CNN is trained for each lead time based on a past window of 15 minutes. The training dataset consist of 93 events (convective, stratiform and mixed events) from the period 2003-2012 and the validating dataset of 17 convective events from the period 2013-2018. The performance is assessed by computing the correlation and the root mean square error of the forecasted fields from the observed radar field, and is compared against the performance of an existing Lagrange-based nowcast method; the Lucas-Kanade optical flow. Special attention is given to the quality of the radar input by using a merged product between radar and gauge data (100 recording stations are used) instead of the raw radar one.  </p><p>The results of this study reveal that the deep CNN is able to learn complex relationship and improve the nowcast for short lead times. However there is a limit that a CNN cannot pass; for those lead times a blending of the radar based nowcast with NWP might be more desirable. Moreover, since most urban models are validated on gauge observations, forecasting on merged data yields more reliable results for urban flood forecasting as the forecast agrees better with the gauge observation.</p>

A Frequency-Domain INS/GPS Dynamic Response Method for Bridging GPS Outages

2010 ◽  
Vol 63 (4) ◽  
pp. 627-643 ◽  
Author(s):  
Mohammed El-Diasty ◽  
Spiros Pagiatakis
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Dynamic Response ◽  
Dynamic System ◽  
Least Squares ◽  
Frequency Domain ◽  
Impulse Response ◽  
Time Domain ◽  
Response Model ◽  
Response Method

We develop a new frequency-domain dynamic response method to model integrated Inertial Navigation System (INS) and Global Positioning System (GPS) architectures and provide an accurate impulse-response-based INS-only navigation solution when GPS signals are denied (GPS outages). The input to such a dynamic system is the INS-only solution and the output is the INS/GPS integration solution; both are used to derive the transfer function of the dynamic system using Least Squares Frequency Transform (LSFT). The discrete Inverse Least Squares Frequency Transform (ILSFT) of the transfer function is applied to estimate the impulse response of the INS/GPS system in the time domain. It is shown that the long-term motion dynamics of a DQI-100 IMU/Trimble BD950 integrated system are recovered by 72%, 42%, 75%, and 40% for north and east velocities, and north and east positions respectively, when compared with the INS-only solution (prediction mode of the INS/GPS filter). A comparison between our impulse response model and the current state-of-the-art time-domain feed-forward neural network shows that the proposed frequency-dependent INS/GPS response model is superior to the neural network model by about 26% for 2D velocities and positions during GPS outages.

Sign in / Sign up

Export Citation Format

Share Document