Data Analytics Development from Military Operational Data

Each year, the National Training Center (NTC) located at Fort Irwin, California, hosts multiple Brigade-level rotational units to conduct training exercises. NTC’s Instrumentation Systems (NTC-IS) digitally capture and store characteristics of movement and maneuver, use of fires, and other tactical operations in a vast database. The Army’s Engineer Research and Development Center (ERDC) recently partnered with Training and Doctrine Command (TRADOC) to make some of the data available for introductory analysis within a relational database. While this data has the potential to expose capability gaps, uncover the truth behind doctrinal assumptions, and create a sophisticated feedback platform for Army leaders at all levels, it is largely unexplored and underutilized. The purpose of this project is to demonstrate the value of this data by developing a prototype information system that supports post-rotation analytics, playback capabilities, and repeatable workflows that measure and expose ground-truth operational and logistical behavior and performance during a rotation. The Army modeling and analysis community will use these products to systematically curate and archive the database and enable future analysis of the NTC-IS data.

A Review of Predictive and Prescriptive Offshore Wind Farm Operation and Maintenance

Offshore wind farms are a rapidly developing source of clean, low-carbon energy and as they continue to grow in scale and capacity, so does the requirement for their efficient and optimised operation and maintenance. Historically, approaches to maintenance have been purely reactive. However, there is a movement in offshore wind, and wider industry in general, towards more proactive, condition-based maintenance approaches which rely on operational data-driven decision making. This paper reviews the current efforts in proactive maintenance strategies, both predictive and prescriptive, of which the latter is an evolution of the former. Both use operational data to determine whether a turbine component will fail in order to provide sufficient warning to carry out necessary maintenance. Prescriptive strategies also provide optimised maintenance actions, incorporating predictions into a wider maintenance plan to address predicted failure modes. Beginning with a summary of common techniques used across both strategies, this review moves on to discuss their respective applications in offshore wind operation and maintenance. This review concludes with suggested areas for future work, underlining the need for models which can be simply incorporated by site operators and integrate live data whilst handling uncertainties. A need for further focus on medium-term planning strategies is also highlighted along with consideration of the question of how to quantify the impact of a proactive maintenance strategy.

Partial Discharge Diagnostics: Data Cleaning and Feature Extraction

Detection of partial discharge (PD) in switchgears requires extensive data collection and time-consuming analyses. Data from real live operational environments pose great challenges in the development of robust and efficient detection algorithms due to overlapping PDs and the strong presence of random white noise. This paper presents a novel approach using clustering for data cleaning and feature extraction of phase-resolved partial discharge (PRPD) plots derived from live operational data. A total of 452 PRPD 2D plots collected from distribution substations over a six-month period were used to test the proposed technique. The output of the clustering technique is evaluated on different types of machine learning classification techniques and the accuracy is compared using balanced accuracy score. The proposed technique extends the measurement abilities of a portable PD measurement tool for diagnostics of switchgear condition, helping utilities to quickly detect potential PD activities with minimal human manual analysis and higher accuracy.

Prediction and Optimization of the Cost of Energy Resources in Greek Public Hospitals

The continuous operation and the specialized conditions needed for safely delivering healthcare services make hospitals among the most expensive buildings. Several studies in different countries have investigated the potential role and contribution of macroscopic indices of hospitals in total energy requirements. In this work, we tried to investigate the energy requirements of Greek hospitals in terms of cost. We collected data from all public hospitals in Greece over a 2 year period (2018–2019) and evaluated the contribution of various factors in the total energy cost. The data revealed large variability by region and by hospital, even regarding structures of the same category and size. The analysis also showed that structural and operational data of each hospital differently influence the hospitals’ energy requirements. Using regression methods, we developed two models for calculating annual energy costs. One only contains hospital structural data (number of beds, type of hospital, number of employees, and the non/use of alternative energy sources such as natural gas), and it reached an R² of 0.84. The second model contains not only structural but also operational data from each hospital (number of the internal patients, number of surgeries and number of medical imaging tests), and it reached an R² of 0.87. The former model is easier to compute since it only relies on data that can be easily gathered, but the latter has slightly better performance. These tools can help the Ministry of Health and hospitals’ management to identify the factors that contribute to the energy cost in order to plan targeted interventions, be well-prepared regarding budgeting, and be able to progressively measure, monitor, and improve the environmental footprint of hospitals by investing in renewable energy resources.

Four years of operational data for five hydrogen refueling stations

Worldwide about 550 hydrogen refueling stations (HRS) were in operation in 2021, of which 38%. were in Europe. With their number expected to grow even further, the collection and investigation of real-world station operative data are fundamental to tracking their activity in terms of safety issues, performances, costs, maintenance, reliability, and energy use. This paper shows and analyses the parameters that characterize the refueling of 350 bar fuel cell buses in four HRS within the 3Emotion project. The HRS are characterized by different refueling capacities, hydrogen supply schemes, storage volumes and pressures, and operational strategies. From data logs provided by the operators, a dataset of three years of operation has been created. In particular total hydrogen quantity, the fill amount dispensed to each bus, the refueling duration, the average mass flow rate, the number of refueling events and the daily number of refills, the daily profile, the utilization factor, and the availability are investigated. The results show similar hydrogen amount per fill distribution, but quite different refueling times among the stations. The average daily mass per bus is around 12.95 kg, the most frequent value 15 kg, the standard deviation 7.46. About 50% of the total amount of hydrogen is dispensed overnight and the refueling events per bus are typically every 24 hours. Finally, the station utilization is below 30% for all sites.

Application of virtualisation environment for data security in operational data processing systems

This paper presents a concept, developed and tested by the authors, of a virtualisation environment enabling the protection of aggregated data through the use of high availability (HA) of IT systems. The presented solution allows securing the central database system and virtualised server machines by using a scalable environment consisting of physical servers and disk arrays. The authors of this paper focus on ensuring the continuity of system operation and on minimising the risk of failures related to the availability of the operational data analysis system.

A Comprehensive Study on Safety of New Energy Vehicles

New energy vehicles (NEVs) have become a fundamental part of transportation system. Performance of an NEV is hugely determined by batteries, motors, and embedded electric control units. In this paper, a comprehensive study that covers all these key components is presented. Mechanisms and characterizations of failures are given in detail. On top of these, algorithms for fault diagnosis are established based on big data of real-world NEVs with joint considerations of design flaws, usage behaviors, and environmental conditions. In this way, multiple types of faults can be detected ahead of time to avoid accident. Proposed methods have been verified by real-world operational data, indicating effectiveness while providing insights for NEV design optimization.