Environmental Assessment in the Pre-launch Phase of Hayabusa2 for Safety Declaration of Returned Samples From Asteroid (162173) Ryugu: Background Monitoring of Possible Contaminants During Development of the Sampler System

We report the ground-based environmental assessments during development of the sampler system until the launch of the Hayabusa2 spacecraft. We conducted static monitoring of potential contaminants to assess the environmental cleanliness during (1) laboratory work throughout the development and manufacturing of the sampler devices, (2) installation of the sampler system on the spacecraft, and (3) transportation to the launch site at the JAXA’s Tanegashima Space Center. Major elements and ions detected in our inorganic analysis were sodium (Na), potassium (K), and ionized chloride (Cl–); those were positively correlated with the total organic content and with exposure duration in the range from 101 to 103 nanogram per monitor coupon within a ~30 mm-diameter scale. We confirmed that deposits on the coupon were totally less than the microgram-scale order during manufacturing, installation, and transportation in the pre-launch phase. The present assessment yields a nominal safety declaration for sample analysis of the pristine sample (>5 g) returned from asteroid (162173) Ryugu combined with a highly clean environmental background level. We expect that the Hayabusa2-returned sample from Ryugu without severe and/or unknown contamination will allow us to provide native profiles recorded in the carbonaceous asteroid history.

Resolution-Enhancing Structure for the Electric Field Microsensor Chip

Electrostatic voltage is a vital parameter in industrial production lines, for reducing electrostatic discharge harms and improving yields. Due to such drawbacks as package shielding and low resolution, previously reported electric field microsensors are still not applicable for industrial static monitoring uses. In this paper, we introduce a newly designed microsensor package structure, which enhances the field strength inside the package cavity remarkably. This magnification effect was studied and optimized by both theoretical calculation and ANSYS simulation. By means of the digital synthesizer and digital coherent demodulation method, the compact signal processing circuit for the packaged microsensor was also developed. The meter prototype was calibrated above a charged metal plate, and the electric field resolution was 5 V/m, while the measuring error was less than 3 V, from −1 kV to 1 kV in a 2 cm distance. The meter was also installed into a production line and showed good consistency with, and better resolution than, a traditional vibratory capacitance sensor.

Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.

Strategically Patrolling in a Chemical Cluster Addressing Gas Pollutants’ Releases through a Game-Theoretic Model

Chemical production activities in chemical clusters, if not well managed, will pose great threats to the surrounding air environment and impose great burden on emergency handling. Therefore, it is urgent and substantial in a chemical cluster to develop proper and suitable pollution controlling strategies for an inspection agency to monitor chemical production processes. Apart from the static monitoring resources (e.g., monitoring stations and gas sensor modules), patrolling by mobile vehicle resources is arranged for better detecting the illegal releasing behaviors of emission spots in different chemical plants. However, it has been proven that the commonly used patrolling strategies (i.e., the fixed route strategy and the purely randomized route strategy) are non-optimal and fail to interact with intelligent chemical plants. Therefore, we proposed the Chemical Cluster Environmental Protection Patrolling (CCEPP) game to tackle the problem in this paper. Through combining the source estimation process, the game is modeled to detect the illegal releasing behaviors of chemical plants by randomly and strategically arranging the patrolling routes and intensities in different chemical sites. In this game-theoretic model, players (patroller and chemical sites), strategies, payoffs, and game solvers are modeled in sequence. More importantly, this game model also considers traffic delays or bounded cognition of patrollers on patrolling plans. Therefore, a discrete Markov decision process was used to model this stochastic process. Further, the model is illustrated by a case study. Results imply that the patrolling strategy suggested by the CCEPP game outperforms both the fixed route strategy and the purely randomized route strategy.

Abstract WP333: Utilizing Data to Drive Stroke Program Management

Introduction: There is a lack of standard data management tools for neurovascular service lines and stroke programs. Many hospitals use “home grown” spreadsheets or upload to various registries which are designed more toward research initiatives than daily operations. Hypothesis: Installation of a dedicated Neurovascular Information System (NVIS), especially when electronically interfaced with other systems, will result in improved efficiency in operating a stroke program and/or neurovascular service line. Methods: A large health system in Tennessee has installed an NVIS for daily use within its Comprehensive Stroke Center. The system has been interfaced with other information systems in the hospital to support automated data entry. The service line directors and physician leaders have monitored data related to time spent in various aspects of program management (e.g. data entry/management, interaction with staff, interaction with patients, etc.) to understand how the use of a system can impact resource allocation. The system is being utilized through mobile technology, such as tablets, and static monitoring units in identified key locations. Results: The data collection time required has been reduced by more than 50% because of the system’s automated collection and reporting features. The leaders of the service line have also developed more detailed, data-driven dashboards which are being used for management decisions. Education is also data-driven as any process fall-outs are revealed through program dashboards. Conclusion: Utilization of a dedicated NVIS reverses the narrative related to stroke program management. It has allowed the program leaders to use data to drive programmatic decisions and development, rather than being tied to data entry requirements and struggling to enter post-dated information. This novel approach continues to support research endeavors and registry participation, while increasing efficiency and improving access to meaningful analytics.