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VODCA: Verification of Diagnosis Using CAM-Based Approach for Explainable Process Monitoring.

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Summary
This summary is machine-generated.

This study introduces a new framework for industrial process monitoring that enhances fault detection and diagnosis. By using class activation maps, it provides more reliable system abnormality analysis for improved industrial stability.

Keywords:
anomaly detectionclass activation mapdeep neural networkfault detection and diagnosisstatistical process control

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Area of Science:

  • Industrial Process Monitoring
  • Artificial Intelligence in Engineering
  • System Diagnostics

Background:

  • Industrial sites generate vast amounts of data due to advancements in communication technology.
  • Effective process monitoring is crucial for system stability, detecting and diagnosing unexpected changes.
  • Deep neural networks (DNNs) are used for fault detection but struggle with diagnosis due to their black-box nature.

Purpose of the Study:

  • To propose a novel process-monitoring framework capable of both detecting and diagnosing system faults and abnormalities.
  • To enhance the interpretability and reliability of fault diagnosis in industrial systems.
  • To improve the accuracy of fault detection and provide verified diagnostic information.

Main Methods:

  • Developed a process-monitoring framework utilizing deep neural networks.
  • Employed class activation maps (CAMs) for fault diagnosis.
  • Implemented post-processing techniques to verify CAM-based diagnoses.

Main Results:

  • The proposed method demonstrated improved detection of faults and abnormalities.
  • Generated class activation maps provided a more verified and interpretable diagnosis.
  • Simulations using industrial motor datasets and a case study in sapphire nozzle manufacturing validated the framework's effectiveness.

Conclusions:

  • The novel framework successfully integrates fault detection and diagnosis in industrial process monitoring.
  • Class activation map verification enhances the reliability and interpretability of diagnostic outcomes.
  • The approach is applicable to real-world manufacturing scenarios, improving system stability and operational insights.