Data-driven Aspects of Engineering The Use of Operational Data in SoS Engineering: Chances and Challenges

2018 ◽  
Author(s):  
Michael Borth ◽  
Emile van Gerwen
Keyword(s):  
Data Driven ◽  
Operational Data

scholarly journals Research on a novel data-driven aging estimation method for battery systems in real-world electric vehicles

2021 ◽  
Vol 13 (7) ◽  
pp. 168781402110277
Author(s):  
Yankai Hou ◽  
Zhaosheng Zhang ◽  
Peng Liu ◽  
Chunbao Song ◽  
Zhenpo Wang
Keyword(s):  
Electric Vehicles ◽  
Real World ◽  
Regression Models ◽  
Estimation Method ◽  
Recursive Least Squares ◽  
Data Driven ◽  
Accurate Estimation ◽  
Support Vector ◽  
Battery Degradation ◽  
Operational Data

Accurate estimation of the degree of battery aging is essential to ensure safe operation of electric vehicles. In this paper, using real-world vehicles and their operational data, a battery aging estimation method is proposed based on a dual-polarization equivalent circuit (DPEC) model and multiple data-driven models. The DPEC model and the forgetting factor recursive least-squares method are used to determine the battery system’s ohmic internal resistance, with outliers being filtered using boxplots. Furthermore, eight common data-driven models are used to describe the relationship between battery degradation and the factors influencing this degradation, and these models are analyzed and compared in terms of both estimation accuracy and computational requirements. The results show that the gradient descent tree regression, XGBoost regression, and light GBM regression models are more accurate than the other methods, with root mean square errors of less than 6.9 mΩ. The AdaBoost and random forest regression models are regarded as alternative groups because of their relative instability. The linear regression, support vector machine regression, and k-nearest neighbor regression models are not recommended because of poor accuracy or excessively high computational requirements. This work can serve as a reference for subsequent battery degradation studies based on real-time operational data.

scholarly journals A Hybrid Learning Approach to Prognostics and Health Management Applied to Military Ground Vehicles Using Time-Series and Maintenance Event Data

2020 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
W Glenn Bond ◽  
Haley Dozier ◽  
Thomas L Arnold ◽  
Michael Y Lam ◽  
Quyen T Dong ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analytics ◽  
High Performance ◽  
Time Series Data ◽  
Data Driven ◽  
Series Data ◽  
Event Data ◽  
Ground Vehicles ◽  
Operational Data ◽  
Operational Time

Attempts to leverage operational time-series data in Condition Based Maintenance (CBM) approaches to optimize the life cycle management and Reliability, Availability, and Maintainability (RAM) of military vehicles have encountered several obstacles over decades of data collection. These obstacles have beset similar approaches on civilian ground vehicles, as well as on aircraft and other complex systems. Analysis of operational data is critical because it represents a continuous recording of the state of the system. Applying rudimentary data analytics to operational data can provide insights like fuel usage patterns or observed reliability of one vehicle or even a fleet. Monitoring trends and analyzing patterns in this data over time, however, can provide insight into the health of a vehicle, a complex system, or a fleet, predicting mean time to failure or compiling logistic or life cycle needs. Such High-Performance Data Analytics (HPDA) on operational time-series datasets has been historically difficult due to the large amount of data gathered from vehicle sensors, the lack of association between clusters observed in the data and failures or unscheduled maintenance events, and the deficiency of unsupervised learning techniques for time-series data. We present an HPDA environment and a method of discovering patterns in vehicle operational data that determines models for predicting the likelihood of imminent failure, referred to as Parameter-Based Indicators (PBIs). Our method is a data-driven approach that uses both time-series and relational maintenance data. This hybrid approach combines both supervised and unsupervised machine learning and data analytic techniques to correlate labeled, relational maintenance event data with unlabeled operational time-series data utilizing the DoD High Performance Computing (HPC) capabilities at the U.S. Army Engineer Research and Development Center. In leveraging both time-series and relational data, we demonstrate a means of fast, purely data-driven model creation that is more broadly applicable and requires less a priori information than physics informed, data-driven models. By blending these approaches, this system will be able to relate some lifecycle management goals through the workflow to generate specific PBIs that will predict failures or highlight appropriate areas of concern in individual or collective vehicle histories.

scholarly journals A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Cheng Fan ◽  
Meiling Chen ◽  
Xinghua Wang ◽  
Jiayuan Wang ◽  
Bufu Huang
Keyword(s):  
Energy Management ◽  
Data Science ◽  
Data Preprocessing ◽  
Building Energy ◽  
Data Driven ◽  
Quality Data ◽  
Missing Value ◽  
Missing Value Imputation ◽  
Building Energy Management ◽  
Operational Data

The rapid development in data science and the increasing availability of building operational data have provided great opportunities for developing data-driven solutions for intelligent building energy management. Data preprocessing serves as the foundation for valid data analyses. It is an indispensable step in building operational data analysis considering the intrinsic complexity of building operations and deficiencies in data quality. Data preprocessing refers to a set of techniques for enhancing the quality of the raw data, such as outlier removal and missing value imputation. This article serves as a comprehensive review of data preprocessing techniques for analysing massive building operational data. A wide variety of data preprocessing techniques are summarised in terms of their applications in missing value imputation, outlier detection, data reduction, data scaling, data transformation, and data partitioning. In addition, three state-of-the-art data science techniques are proposed to tackle practical data challenges in the building field, i.e., data augmentation, transfer learning, and semi-supervised learning. In-depth discussions have been presented to describe the pros and cons of existing preprocessing methods, possible directions for future research and potential applications in smart building energy management. The research outcomes are helpful for the development of data-driven research in the building field.

scholarly journals Data-Driven Management and Interoperable Metrics for Special Collections and Archives User Services

2012 ◽  
Vol 13 (2) ◽  
pp. 129-151 ◽  
Author(s):  
Joyce Chapman ◽  
Elizabeth Yakel
Keyword(s):  
Decision Making ◽  
Quantitative Analysis ◽  
Data Driven ◽  
Significant Challenge ◽  
Special Collections ◽  
Quantitative Metrics ◽  
Day To Day Operations ◽  
Using Data ◽  
Institutional Boundaries ◽  
Operational Data

While special collections and archives managers have at times recognized the importance of using data to drive decision making, translating this objective into reality and integrating data analysis into day-to-day operations has proven to be a significant challenge. There have also been obstacles to formulating quantitative metrics for special collections and archives and rendering them interoperable across institutional boundaries. This article attempts to focus a conversation around two issues: 1) the importance of quantitative analysis of operational data for improving research services in special collections and archives; and 2) the need for the profession to achieve consensus on definitions for . . .

Mean-variance model for optimization of the timetable in urban rail transit systems

2017 ◽  
Vol 232 (4) ◽  
pp. 1005-1020
Author(s):  
Hui Yang ◽  
Xiang Li ◽  
Xin Yang
Keyword(s):  
Real Life ◽  
Urban Rail Transit ◽  
Data Driven ◽  
Rail Transit ◽  
Variance Model ◽  
The Real ◽  
Urban Rail ◽  
Mean Variance ◽  
Cooperative Scheduling ◽  
Operational Data

Regenerative braking is an energy-efficient technology that converts kinetic energy to electrical energy during braking phases. For more efficient recovered energy utilization, the stochastic cooperative scheduling approach has been proposed for determining the dwell times at stations, wherein the accelerating trains can use the energy recovered from the adjacent braking trains as much as possible. Here, running times at the sections are considered as random variables with given probability functions. In this paper, the authors develop a data-driven stochastic cooperative scheduling approach in which the real data of the speed of trains are recorded and used in the place of motion equations. First, the authors formulate a stochastic mean-variance model, which maximizes the expected utilization and minimizes the variance of the quantity of the recovered energy. Second, a genetic algorithm that utilizes particle swarm optimization has been designed to find the optimal dwell times at stations. Finally, numerical examples are presented based on the real-life operational data from Beijing Yizhuang urban rail transit line in China. The results illustrate that the real-life operational data in the data-driven stochastic cooperative scheduling approach can provide a more accurate description about the movement of trains, which would result in more efficient energy saving, i.e. by 1.66%, in comparison with the stochastic cooperative scheduling approach. Most importantly, the data-driven stochastic cooperative scheduling approach results in lower variance by 68.69% and higher robustness.

scholarly journals An Integrated Energy System Operating Scenarios Generator Based on Generative Adversarial Network

2019 ◽  
Vol 11 (23) ◽  
pp. 6699
Author(s):  
Suyang Zhou ◽  
Zijian Hu ◽  
Zhi Zhong ◽  
Di He ◽  
Meng Jiang
Keyword(s):  
Control Strategy ◽  
Energy System ◽  
Original Data ◽  
Data Driven ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Stage Of Development ◽  
Integrated Energy System ◽  
Operational Data

The convergence of energy security and environmental protection has given birth to the development of integrated energy systems (IES). However, the different physical characteristics and complex coupling of different energy sources have deeply troubled researchers. With the rapid development of AI and big data, some attempts to apply data-driven methods to IES have been made. Data-driven technologies aim to abandon complex IES modeling, instead mining the mapping relationships between different parameters based on massive volumes of operating data. However, integrated energy system construction is still in the initial stage of development and operational data are difficult to obtain, or the operational scenarios contained in the data are not enough to support data-driven technologies. In this paper, we first propose an IES operating scenario generator, based on a Generative Adversarial Network (GAN), to produce high quality IES operational data, including energy price, load, and generator output. We estimate the quality of the generated data, in both visual and quantitative aspects. Secondly, we propose a control strategy based on the Q-learning algorithm for a renewable energy and storage system with high uncertainty. The agent can accurately map between the control strategy and the operating states. Furthermore, we use the original data set and the expanded data set to train an agent; the latter works better, confirming that the generated data complements the original data set and enriches the running scenarios.

scholarly journals Construction of Operational Data-Driven Power Curve of a Generator by Industry 4.0 Data Analytics

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1227
Author(s):  
Waqar Muhammad Ashraf ◽  
Ghulam Moeen Uddin ◽  
Muhammad Farooq ◽  
Fahid Riaz ◽  
Hassan Afroze Ahmad ◽  
...  
Keyword(s):  
Industry 4.0 ◽  
Data Analytics ◽  
Large Scale ◽  
External Validation ◽  
Data Driven ◽  
Power Curve ◽  
Modeling Tools ◽  
Analytical Technique ◽  
Performance Curves ◽  
Operational Data

Constructing the power curve of a power generation facility integrated with complex and large-scale industrial processes is a difficult task but can be accomplished using Industry 4.0 data analytics tools. This research attempts to construct the data-driven power curve of the generator installed at a 660 MW power plant by incorporating artificial intelligence (AI)-based modeling tools. The power produced from the generator is modeled by an artificial neural network (ANN)—a reliable data analytical technique of deep learning. Similarly, the R2.ai application, which belongs to the automated machine learning (AutoML) platform, is employed to show the alternative modeling methods in using the AI approach. Comparatively, the ANN performed well in the external validation test and was deployed to construct the generator’s power curve. Monte Carlo experiments comprising the power plant’s thermo-electric operating parameters and the Gaussian noise are simulated with the ANN, and thus the power curve of the generator is constructed with a 95% confidence interval. The performance curves of industrial systems and machinery based on their operational data can be constructed using ANNs, and the decisions driven by these performance curves could contribute to the Industry 4.0 vision of effective operation management.

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