Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform V2

• URL: http://arxiv.org/abs/2502.05020v1
• Date: Fri, 07 Feb 2025 15:47:27 GMT
• Title: Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform V2
• Authors: Ramy Harik, Fadi El Kalach, Jad Samaha, Philip Samaha, Devon Clark, Drew Sander, Liam Burns, Ibrahim Yousif, Victor Gadow, Ahmed Mahmoud, Thorsten Wuest,
• Abstract summary: This paper presents two industry-grade datasets captured during an 8-hour continuous operation of a manufacturing assembly line. The datasets adhere to industry standards, covering communication protocols, actuators, control mechanisms, transducers, sensors, and cameras.
• Score: 0.0
• License:
• Abstract: This paper presents two industry-grade datasets captured during an 8-hour continuous operation of the manufacturing assembly line at the Future Factories Lab, University of South Carolina, on 08/13/2024. The datasets adhere to industry standards, covering communication protocols, actuators, control mechanisms, transducers, sensors, and cameras. Data collection utilized both integrated and external sensors throughout the laboratory, including sensors embedded within the actuators and externally installed devices. Additionally, high-performance cameras captured key aspects of the operation. In a prior experiment [1], a 30-hour continuous run was conducted, during which all anomalies were documented. Maintenance procedures were subsequently implemented to reduce potential errors and operational disruptions. The two datasets include: (1) a time-series analog dataset, and (2) a multi-modal time-series dataset containing synchronized system data and images. These datasets aim to support future research in advancing manufacturing processes by providing a platform for testing novel algorithms without the need to recreate physical manufacturing environments. Moreover, the datasets are open-source and designed to facilitate the training of artificial intelligence models, streamlining research by offering comprehensive, ready-to-use resources for various applications and projects.

Related papers

• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• Unsupervised Multimodal Fusion of In-process Sensor Data for Advanced Manufacturing Process Monitoring [0.0]
  This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data. Our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance.
  arXiv  Detail & Related papers  (2024-10-29T21:52:04Z)
• IPAD: Industrial Process Anomaly Detection Dataset [71.39058003212614]
  Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames. We propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage.
  arXiv  Detail & Related papers  (2024-04-23T13:38:01Z)
• Analog and Multi-modal Manufacturing Datasets Acquired on the Future Factories Platform [0.0]
  Two industry-grade datasets are presented in this paper. They were collected at the Future Factories Lab at the University of South Carolina on December 11th and 12th of 2023.
  arXiv  Detail & Related papers  (2024-01-28T02:26:58Z)
• A Systematic Review of Available Datasets in Additive Manufacturing [56.684125592242445]
  In-situ monitoring incorporating visual and other sensor technologies allows the collection of extensive datasets during the Additive Manufacturing process. These datasets have potential for determining the quality of the manufactured output and the detection of defects through the use of Machine Learning. This systematic review investigates the availability of open image-based datasets originating from AM processes that align with a number of pre-defined selection criteria.
  arXiv  Detail & Related papers  (2024-01-27T16:13:32Z)
• Open-sourced Data Ecosystem in Autonomous Driving: the Present and Future [130.87142103774752]
  This review systematically assesses over seventy open-source autonomous driving datasets. It offers insights into various aspects, such as the principles underlying the creation of high-quality datasets. It also delves into the scientific and technical challenges that warrant resolution.
  arXiv  Detail & Related papers  (2023-12-06T10:46:53Z)
• Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers [39.52789559084336]
  Dynamic PicoProbe at Argonne National Laboratory is undergoing upgrades that will enable it to produce up to 100s of GB of data per day. While this data is highly important for both fundamental science and industrial applications, there is currently limited on-site infrastructure to handle these high-volume data streams. We address this problem by providing a software architecture capable of supporting neighboring large-scale data transfers to neighboring supercomputers at the Argonne Leadership Computing Facility. This infrastructure supports expected workloads and also provides domain scientists the ability to reinterrogate data from past experiments to yield additional scientific value and derive new insights.
  arXiv  Detail & Related papers  (2023-08-25T23:07:58Z)
• TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
  Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations. We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
  arXiv  Detail & Related papers  (2023-05-19T10:11:21Z)
• Federated Learning for Autoencoder-based Condition Monitoring in the Industrial Internet of Things [0.07646713951724012]
  Condition monitoring and predictive maintenance methods are key pillars for an efficient and robust manufacturing production cycle in the Industrial Internet of Things. The employment of machine learning models to detect and predict deteriorating behavior by analyzing a variety of data collected across several industrial environments shows promising results in recent works. Although collaborating and sharing knowledge between industry sites yields large benefits, it is often prohibited due to data privacy issues. We propose an Autoencoder-based Federated Learning method utilizing vibration sensor data from rotating machines, that allows for a distributed training on edge devices, located on-premise and close to the monitored machines.
  arXiv  Detail & Related papers  (2022-11-14T18:40:50Z)
• Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoT [82.33080550378068]
  The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue. This paper studies the FL technology applications to manage IIoT equipment data in wireless network environments.
  arXiv  Detail & Related papers  (2022-02-03T07:12:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.