scholarly journals Fostering Sustainability through Visualization Techniques for Real-Time IoT Data: A Case Study Based on Gas Turbines for Electricity Production

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4556
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo
Keyword(s):  
Production Process ◽  
Real Time ◽  
Gas Turbines ◽  
Electricity Generation ◽  
Industrial Development ◽  
Sensor Data ◽  
Electricity Production ◽  
Proposal A ◽  
Visualization Techniques

Improving sustainability is a key concern for industrial development. Industry has recently been benefiting from the rise of IoT technologies, leading to improvements in the monitoring and breakdown prevention of industrial equipment. In order to properly achieve this monitoring and prevention, visualization techniques are of paramount importance. However, the visualization of real-time IoT sensor data has always been challenging, especially when such data are originated by sensors of different natures. In order to tackle this issue, we propose a methodology that aims to help users to visually locate and understand the failures that could arise in a production process.This methodology collects, in a guided manner, user goals and the requirements of the production process, analyzes the incoming data from IoT sensors and automatically derives the most suitable visualization type for each context. This approach will help users to identify if the production process is running as well as expected; thus, it will enable them to make the most sustainable decision in each situation. Finally, in order to assess the suitability of our proposal, a case study based on gas turbines for electricity generation is presented.

scholarly journals DOLARS, a Distributed On-Line Activity Recognition System by Means of Heterogeneous Sensors in Real-Life Deployments—A Case Study in the Smart Lab of The University of Almería

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 405
Author(s):  
Marcos Lupión ◽  
Javier Medina-Quero ◽  
Juan F. Sanjuan ◽  
Pilar M. Ortigosa
Keyword(s):  
Real Time ◽  
Activity Recognition ◽  
Real Life ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Heterogeneous Sensors ◽  
On Line ◽  
The University

Activity Recognition (AR) is an active research topic focused on detecting human actions and behaviours in smart environments. In this work, we present the on-line activity recognition platform DOLARS (Distributed On-line Activity Recognition System) where data from heterogeneous sensors are evaluated in real time, including binary, wearable and location sensors. Different descriptors and metrics from the heterogeneous sensor data are integrated in a common feature vector whose extraction is developed by a sliding window approach under real-time conditions. DOLARS provides a distributed architecture where: (i) stages for processing data in AR are deployed in distributed nodes, (ii) temporal cache modules compute metrics which aggregate sensor data for computing feature vectors in an efficient way; (iii) publish-subscribe models are integrated both to spread data from sensors and orchestrate the nodes (communication and replication) for computing AR and (iv) machine learning algorithms are used to classify and recognize the activities. A successful case study of daily activities recognition developed in the Smart Lab of The University of Almería (UAL) is presented in this paper. Results present an encouraging performance in recognition of sequences of activities and show the need for distributed architectures to achieve real time recognition.

Innovative real-time sensing of flow dynamics in groundwater and sediments to map contaminant spreading

2021 ◽  
Author(s):  
Goedele Verreydt ◽  
Niels Van Putte ◽  
Timothy De Kleyn ◽  
Joris Cool ◽  
Bino Maiheu
Keyword(s):  
Surface Water ◽  
Groundwater Flow ◽  
Real Time ◽  
Flow Direction ◽  
Flow Dynamics ◽  
Sensor Data ◽  
Time Data ◽  
Mass Fluxes ◽  
First Case

<p>Groundwater dynamics play a crucial role in the spreading of a soil and groundwater contamination. However, there is still a big gap in the understanding of the groundwater flow dynamics. Heterogeneities and dynamics are often underestimated and therefore not taken into account. They are of crucial input for successful management and remediation measures. The bulk of the mass of mass often is transported through only a small layer or section within the aquifer and is in cases of seepage into surface water very dependent to rainfall and occurring tidal effects.</p><p> </p><p>This study contains the use of novel real-time iFLUX sensors to map the groundwater flow dynamics over time. The sensors provide real-time data on groundwater flow rate and flow direction. The sensor probes consist of multiple bidirectional flow sensors that are superimposed. The probes can be installed directly in the subsoil, riverbed or monitoring well. The measurement setup is unique as it can perform measurements every second, ideal to map rapid changing flow conditions. The measurement range is between 0,5 and 500 cm per day.</p><p> </p><p>We will present the measurement principles and technical aspects of the sensor, together with two case studies.</p><p> </p><p>The first case study comprises the installation of iFLUX sensors in 4 different monitoring wells in a chlorinated solvent plume to map on the one hand the flow patterns in the plume, and on the other hand the flow dynamics that are influenced by the nearby popular trees. The foreseen remediation concept here is phytoremediation. The sensors were installed for a period of in total 4 weeks. Measurement frequency was 5 minutes. The flow profiles and time series will be presented together with the determined mass fluxes.</p><p> </p><p>A second case study was performed on behalf of the remediation of a canal riverbed. Due to industrial production of tar and carbon black in the past, the soil and groundwater next to the small canal ‘De Lieve’ in Ghent, Belgium, got contaminated with aliphatic and (poly)aromatic hydrocarbons. The groundwater contaminants migrate to the canal, impact the surface water quality and cause an ecological risk. The seepage flow and mass fluxes of contaminants into the surface water were measured with the novel iFLUX streambed sensors, installed directly in the river sediment. A site conceptual model was drawn and dimensioned based on the sensor data. The remediation concept to tackle the inflowing pollution: a hydraulic conductive reactive mat on the riverbed that makes use of the natural draining function of the waterbody, the adsorption capacity of a natural or secondary adsorbent and a future habitat for micro-organisms that biodegrade contaminants. The reactive mats were successfully installed and based on the mass flux calculations a lifespan of at least 10 years is expected for the adsorption material.  </p>

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

A Case Study of Embedding Real-time Infrastructure Sensor Data to BIM

2014 ◽  
Author(s):  
Jianli Chen ◽  
Tanyel Bulbul ◽  
John E. Taylor ◽  
Guney Olgun
Keyword(s):  
Real Time ◽  
Sensor Data

Building a Trustworthy Product-level Shape-performance Integrated Digital Twin with Multi-fidelity Surrogate Model

2021 ◽  
pp. 1-28
Author(s):  
Shuo Wang ◽  
Xiaonan Lai ◽  
Xiwang He ◽  
Yiming Qiu ◽  
Xueguan Song
Keyword(s):  
Real Time ◽  
Sensor Data ◽  
Test Case ◽  
Digital Twin ◽  
Digital Twins ◽  
Training Samples ◽  
Practical Engineering ◽  
Simulation Results ◽  
Visualization Techniques ◽  
Quantities Of Interest

Abstract Digital twin has the potential for increasing production, achieving real-time monitor, and realizing predictive maintenance by establishing a real-time high-fidelity mapping between the physical entity and its digital model. However, the high accuracy and instantaneousness requirements of digital twins have hindered their applications in practical engineering. This paper presents a universal framework to fulfill the requirements and to build an accurate and trustworthy digital twin by integrating numerical simulations, sensor data, multi-fidelity surrogate (MFS) models, and visualization techniques. In practical engineering, the number of sensors available to measure quantities of interest is often limited, complementary simulations are necessary to compute these quantities. The simulation results are generally more comprehensive but not as accurate as the sensor data. Therefore, the proposed framework combines the benefits of both simulation results and sensor data by using an MFS model based on moving least squares, named MFS-MLS. The MFS-MLS was developed as an essential part to calibrate the continuous field of the simulation by limited sensor data to obtain accurate results for the digital twin. Then single-fidelity surrogate models are built on the whole domain using the calibrated results of the MFS-MLS as training samples and sensor data as inputs to predict and visualize the quantities of interest in real-time. In addition, the framework was validated by a truss test case, and the results demonstrate that the proposed framework has the potential to be an effective tool to build accurate and trustworthy digital twins.

Use of Real-Time Rig-Sensor Data To Improve Daily Drilling Reporting, Benchmarking, and Planning—A Case Study

2007 ◽  
Vol 22 (03) ◽  
pp. 217-226 ◽  
Author(s):  
Wolfgang Mathis ◽  
Gerhard Thonhauser ◽  
Gerhard Wallnoefer ◽  
Johannes Ettl
Keyword(s):  
Real Time ◽  
Sensor Data

Comparison and Combination of GNSS and Strong-Motion Observations: A Case Study of the 2016 Mw 7.0 Kumamoto Earthquake

2020 ◽  
Vol 110 (6) ◽  
pp. 2647-2660
Author(s):  
Nikolaj Dahmen ◽  
Roland Hohensinn ◽  
John Clinton
Keyword(s):  
Real Time ◽  
Inertial Sensors ◽  
Strong Motion ◽  
Global Navigation Satellite Systems ◽  
Sensor Data ◽  
Motion Sensors ◽  
Frequency Range ◽  
Kumamoto Earthquake ◽  
Long Period

ABSTRACT The 2016 Mw 7.0 Kumamoto earthquake resulted in exceptional datasets of Global Navigation Satellite Systems (GNSS) and seismic data. We explore the spatial similarity of the signals and investigate procedures for combining collocated sensor data. GNSS enables the direct observation of the long-period ground displacements, limited by noise levels in regimes of millimeters to several centimeters. Strong-motion accelerometers are inertial sensors and therefore optimally resolve middle- to high-frequency strong ground motion. The double integration from acceleration to displacement amplifies long-period errors introduced by tilt, rotation, noise, and nonlinear instrument responses and can lead to large nonphysical drifts. For the case study of the Kumamoto earthquake, 39 GNSS stations (1  samples/s) with nearby located strong-motion accelerometers (100  samples/s) are investigated. The GNSS waveforms obtained by precise point positioning under real-time conditions prove to be very similar to the postprocessed result. Real-time GNSS and nearby located accelerometers show consistent observations for periods between ∼3–5 and ∼50–100  s. The matching frequency range is defined by the long-period noise of the accelerometer and the low signal-to-noise ratio (SNR) of GNSS, when it comes to small displacements close to its noise level. Current procedures in fusing the data with a Kalman filter are verified for the dataset of this event. Combined data result in a very broadband waveform that covers the optimal frequency range of each sensor. We explore how to integrate fused processing in a real-time network, including event detection and magnitude estimation. Carrying out a statistical test on the GNSS records allows us to identify seismic events and sort out stations with a low SNR, which would otherwise impair the quality of downstream products. The results of this study reinforce the emerging consensus that there is real benefit to collocation GNSS and strong-motion sensors for the monitoring of moderate-to-large earthquakes.

scholarly journals Evolution of Energy Strategies in Turkey: Forecasts by Time Series

2018 ◽  
pp. 1-16
Author(s):  
Soner Top ◽  
Hüseyin Vapur
Keyword(s):  
Time Series ◽  
Natural Gas ◽  
Energy Demand ◽  
Electricity Generation ◽  
Industrial Development ◽  
Fossil Fuels ◽  
Electricity Production ◽  
Energy Sources ◽  
Greenhouse Emissions ◽  
Increasing Demand

As a developing country with over 70% external dependence on energy, there is an increasing demand for electricity in Turkey. In this study, energy resources strategies in Turkey have been investigated and the historical development of its energy usage was summarised. Turkey's energy demand has increased as a result of industrial development and the various energy sources have been selected in different periods to meet this need. In all periods, fossil fuels have taken the lead in energy production. Although investments in renewable and nuclear energy sources have increased, fossil energy sources will not be replaced in the near future. The future fossil fuel production, the electricity production and the greenhouse emissions have been calculated and interpreted by time series (ARIMA), statistically. The forecasts mainly show that natural gas based electricity generation will decrease to 9.3% and renewable energy based electricity generation will increase to 25.6% in the next decade. It is obvious that the fossil fuels based greenhouse emissions will be 375.61 million tons CO2 equivalent in 2026 and the largest share of this emission will be derived from the natural gas by 66.3 billion m3.

Sign in / Sign up

Export Citation Format

Share Document