Analog and Multi-modal Manufacturing Datasets Acquired on the Future
  Factories Platform
Ramy Harik Fadi El Kalach Jad Samaha Devon Clark Drew Sander Philip Samaha Liam Burns Ibrahim Yousif Victor Gadow Theodros Tarekegne Nitol Saha
Two industry-grade datasets are presented in this paper that were collected
at the Future Factories Lab at the University of South Carolina on December
11th and 12th of 2023. These datasets are generated by a manufacturing assembly
line that utilizes industrial standards with respect to actuators, control
mechanisms, and transducers. The two datasets were both generated
simultaneously by operating the assembly line for 30 consecutive hours (with
minor filtering) and collecting data from sensors equipped throughout the
system. During operation, defects were also introduced into the assembly
operation by manually removing parts needed for the final assembly. The
datasets generated include a time series analog dataset and the other is a time
series multi-modal dataset which includes images of the system alongside the
analog data. These datasets were generated with the objective of providing
tools to further the research towards enhancing intelligence in manufacturing.
Real manufacturing datasets can be scarce let alone datasets with anomalies or
defects. As such these datasets hope to address this gap and provide
researchers with a foundation to build and train Artificial Intelligence models
applicable for the manufacturing industry. Finally, these datasets are the
first iteration of published data from the future Factories lab and can be
further adjusted to fit more researchers needs moving forward.