Wireless technology is a key enabler of the vision of the future factory work-cell. Such work-cell will operate autonomously with a high degree of mobility enabled by wireless technology. This model describes the work-cell using the Systems Modeling Language (SysML). Using SysML the structural and parametric characteristics of the work-cell are described. Our model provides the architectural components and performance constraints of the work-cell in which wireless is used for a significant portion of connectivity. It identifies the structural components, interfaces, and data flows. Parametric characteristics that impact work-cell performance are included in the model. Using this model, industrial wireless networking requirements and work-cell behaviors may be developed and performance limits may be evaluated. Note: This dataset is stored in MagicDraw XML format. To open the XML file, MagicDraw 18.4 or higher with the SysML plugin is required. An HTML report is included. To view the HTML report, and browser that supports ActiveX is required.

The work-cell is an essential industrial environment for testing wireless communication techniques in factory automation processes. A graph database approach to storing and analyzing network performance data from a manufacturing factory work-cell is introduced. A robotic testbed performs a pick-and-place task using two collaborative grade robot arms, machine emulators, and wireless communication devices. A graph database is implemented to capture network data and operational event data among the actors within the testbed. Using a proposed schema, the database is then populated with events from the testbed and the resulting graph is constructed. Query commands are then presented to examine and analyze network performance and relationships within the actors of the network. The resulting data from the experiments conducted are included in this dataset.

This repository contains code for analyzing industrial wireless sounder measurements and the generation of wireless scenarios. DISCLAIMER: Certain commercial equipment, instruments, or materials are identified in the associated paper (https://doi.org/10.6028/nist.tn.1951) in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

This dataset includes the position data of a two-dimensional gantry system experiment in which the G-code commands for the gantry were transmitted through a wireless communications link. The testbed is composed of four main components related to the operation of the gantry system. These components are the gantry system, the Wi-Fi network, the RF channel emulator, and the supervisory computer. In the experimental study, we run a scenario in which the gantry tool moves sequentially between four positions and has a preset dwell at each of the positions. The wireless channel impact is produced through the RF channel emulator. First, we consider the benchmark channel with free-space log-distance path loss and ideal channel impulse response (CIR) which has no multi-path. Second, we consider a measured delay profile of an industrial environment where the CIR is experimentally measured and processed to be deployed using the channel emulator and to reflect the industrial environment impact. Moreover, time-varying log-normal shadowing is introduced due to the fluctuations in the signal level because of obstructions. The variance of zero-mean log-normal shadowing is set through the emulator. In order to collect the position information of the gantry system tool, we used a vision tracking system. In this dataset, we attached a meta_data.csv file to map various files to their corresponding parameters. A README.doc file is included to describe the measurement apparatus.

This computer code contains the Tennessee Eastman (TESIM) chemical process model to be simulated using hardware-based simulation approaches. The code is optimized for wireless system integration as a part of the NIST Industrial Wireless project. This code allows for integration with external sensor and actuator equipment and the evaluation of operational performance under different wireless scenarios. Disclaimer: Certain commercial equipment, instruments, or materials are identified in this paper in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose.

계측 센서로부터 측정된 전기 신호를 획득, 수집, 전송하는 장비인 로거에 대한 상세 데이터가 제공됩니다. 주요 데이터로는 로거명, 로거 유형, 제품 코드, 설치 위치 등이 포함됩니다. 이 데이터는 로거의 정상 작동 여부와 성능을 모니터링하는 데 활용되며, 유지보수와 점검 작업의 효율성을 높이는 데 중요한 역할을 합니다. 또한, 로거 관련 종합 데이터를 통해 안정적인 신호 수집과 전송을 지원하며, 실시간 최적화된 운영 환경 구축을 돕습니다. 정확한 로거 정보 제공과 실시간 관리를 통해 시스템 전반의 신뢰성과 안정성 강화에 기여합니다.

Time-sensitive networking (TSN) is an emerging topic for the advancement of wireless networking for industrial applications. TSN, as defined under the umbrella of IEEE 802.1 working group standards, addresses issues related to providing deterministic communications over IEEE 802-based Local Area Networks (LANs). TSN was originally designed to support real-time audio/video applications over Ethernet providing better reliability and lower, more deterministic latency with traffic shaping capabilities. TSN has since expanded its scope and applicability to other applications such as those in industrial environments and automotive. Industrial examples include machine-machine communications for robot control, end-effector actuation, real-time sensing, and safety integrated systems. Applications utilizing an wireless local area network (WLAN) can also benefit from scheduling and traffic shaping as defined in the 802.1Qbv standard; however, factors such as clock stability, synchronization, resource requirements and protocol options come into play when selecting a schedule to support multiple application types on the same network. In this article, we present a scenario for a collaborative robot heavy lift operation, in which, two robots communicate over an IEEE 802.11 WLAN with TSN capabilities to lift a rigid body in three dimensions. Scheduling is performed using 802.1Qbv over WLAN with the robot operating system (ROS) used as the software middleware utilizing the transport control protocol (TCP). As a part of the research, we describe our process for schedule selection to accommodate the time-sensitive traffic of the robotic scenario while allowing an industrial internet of things (IIoT) high data rate traffic to coexist. We then provide an analysis of the impacts of TSN schedule selection on the operational performance of the collaborative robot application. The data provided within this data set was collected as a result of experiments conducted under this research effort.

계룡시 상수도 무선원격검침기 설치 현황 및 운영방식을 법정동별로 분류하여 데이터 작성하였으며, 우리시는 기계식 계량기의 지침을 디지털 숫자로 변환하여 부과되는 시스템 구축

다양한 센서가 측정한 물리적 현상을 전기 신호로 변환한 데이터가 제공됩니다. 센서 종류, 모델명, 사용 단위, 설치 위치 등의 상세 정보와 함께 실시간 측정값을 조회할 수 있습니다. 이 데이터는 센서의 상태와 성능 점검에 활용되며, 센서 관리와 유지보수 계획 수립에 중요한 역할을 합니다. 산업 현장이나 스마트시티 등 다양한 분야에서 센서 데이터를 기반으로 의사결정과 운영 효율화에 기여합니다. 통합된 센서 정보와 실시간 모니터링 데이터를 통해 신뢰성 있는 분석과 관리가 가능하도록 지원합니다.