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Elevating Smart Manufacturing with a Unified Predictive Maintenance Platform: The Synergy between Data Warehousing,

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Smart manufacturing faces equipment failure risks. This research introduces an end-to-end platform using Apache Spark and machine learning for real-time fault detection, enhancing operational reliability.

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Area of Science:

  • Manufacturing Engineering
  • Data Science
  • Artificial Intelligence

Background:

  • Smart manufacturing integrates AI, IoT, and big data, increasing complexity and risks of equipment failure.
  • The need for advanced predictive maintenance is critical to prevent costly downtime in automated environments.

Purpose of the Study:

  • To present an end-to-end platform for proactive equipment maintenance in smart manufacturing.
  • To address the challenges of managing large volumes of sensor data and detecting anomalies in real-time.

Main Methods:

  • Developed a platform merging data warehousing with Apache Spark for efficient time-series sensor data management.
  • Utilized big data analytics for machine learning model creation and an Apache Spark engine for real-time streaming data processing.
  • Implemented fault detection algorithms for instantaneous anomaly identification.

Main Results:

  • Successfully managed voluminous time-series sensor data.
  • Enabled seamless creation of machine learning models for predictive maintenance.
  • Achieved instantaneous processing of streaming data for effective fault detection.

Conclusions:

  • The developed platform offers a proactive maintenance model for smart manufacturing.
  • Enhances operational reliability and sustainability in the digital manufacturing era.
  • Represents a significant advancement in intelligent industrial systems.