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Autoencoder-Based System for Detecting Anomalies in Pelletizer Melt Processes.

Mingxiang Zhu1,2, Guangming Zhang1, Lihang Feng1

  • 1College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing 211899, China.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
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This study introduces an autoencoder-based system for identifying melt anomalies in polyester pelletizers. The deep learning approach enhances production efficiency and product quality by effectively detecting and differentiating anomaly degrees.

Area of Science:

  • Industrial Manufacturing
  • Artificial Intelligence
  • Materials Science

Background:

  • Manual monitoring and traditional methods are insufficient for detecting melt anomalies in polyester pelletizers.
  • Melt extrusion processes require efficient anomaly identification to maintain production efficiency and product quality.

Purpose of the Study:

  • To propose and evaluate an autoencoder-based system for identifying melt anomalies in polyester pelletizers.
  • To address the limitations of current methods in detecting and differentiating melt anomaly states.

Main Methods:

  • Utilized autoencoder technology for deep learning-based anomaly detection.
  • Implemented data augmentation techniques, including random alteration of image brightness and rotation, to improve system robustness.
Keywords:
autoencoder technologydeep learningenvironmental robustnessmelt anomaly identificationpolyester pelletizers

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  • Trained and tested the system on polyester pelletizer melt extrusion data.

    • Main Results:

    • The proposed autoencoder system demonstrated high efficiency in detecting melt anomalies.
    • The system effectively differentiated various degrees of melt anomalies.
    • Enhanced robustness against environmental light intensity variations was achieved through data augmentation.

    Conclusions:

    • Autoencoder technology shows significant potential for industrial applications in melt anomaly identification.
    • The developed system offers improved detection efficiency and generalization performance for polyester pelletizers.
    • Further research can explore advanced deep learning techniques for enhanced industrial process monitoring.