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Three-phase systems have two configurations: the wye and delta. A star configuration can be three or four wires; in a delta configuration, the components are connected in a closed loop. Instantaneous power refers to the power value at a precise moment, and in a balanced three-phase system, it is constant. This is because the sum of the instantaneous powers in the three phases remains steady over time, despite individual fluctuations, due to the symmetry and phase relationship. The total...
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FASTory assembly line power consumption data.

Mahboob Elahi1, Samuel Olaiya Afolaranmi1, Wael M Mohammed1

  • 1FAST-Lab, Faculty of Engineering and Natural Sciences, Tampere University, Finland.

Data in Brief
|May 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a dataset of conveyor belt motor power consumption from the FASTory assembly line. Machine learning models can use this data to predict equipment deterioration and optimize manufacturing processes.

Keywords:
Belt tensionConveyor belt deteriorationDiscrete manufacturing systemsPower consumptionPower consumption-based prognostics modelPredictive maintenance

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Area of Science:

  • Manufacturing Systems Engineering
  • Data Science
  • Industrial IoT

Background:

  • Machine learning (ML) is crucial for analyzing shopfloor data in manufacturing.
  • ML models optimize parameters, predict equipment failure, and aid maintenance planning.
  • This work focuses on power consumption data from an assembly line conveyor belt motor driver.

Purpose of the Study:

  • To present a dataset of conveyor belt motor driver power consumption from the FASTory assembly line.
  • To enable the use of machine learning for predicting equipment deterioration and optimizing processes.
  • To investigate the relationship between belt tension, load, and power consumption.

Main Methods:

  • Collected over 4,000 data samples of power consumption from a conveyor belt motor driver.
  • Utilized web-based industrial controllers and smart 3-phase energy monitoring modules.
  • Developed an application to collect data in JSON format every second, storing it in a MySQL database.
  • Collected data in static (varying loads/tension) and dynamic (pallet movement) cases.
  • Employed an Artificial Neural Network (ANN) for pattern learning in the static case.

Main Results:

  • The dataset captures power consumption under various static load and belt tension conditions.
  • Dynamic case data reveals the impact of belt tension on pallet movement between zones.
  • The collected data facilitates the forecasting of conveyor belt deterioration.

Conclusions:

  • The FASTory dataset provides valuable insights into conveyor belt motor driver behavior.
  • This data supports the development of predictive maintenance strategies for manufacturing equipment.
  • Understanding power consumption patterns aids in forecasting equipment lifespan and optimizing assembly line operations.