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Online Monitoring of Sensor Calibration Status to Support Condition-Based Maintenance.

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Summary

Reliable Condition-Based Maintenance (CBM) requires accurate sensor data. This study introduces an online monitoring strategy using AI and Machine Learning to assess sensor health and trigger calibrations only when necessary, improving industrial metrology.

Keywords:
HMMK-meansPCAcalibrationcondition-based maintenancefeatures generationonline calibration statussensors

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Area of Science:

  • Industrial Metrology
  • Artificial Intelligence
  • Machine Learning

Background:

  • Condition-Based Maintenance (CBM) relies on sensor data reliability.
  • Industrial metrology and metrological traceability are crucial for sensor data quality.
  • Current periodic calibration strategies are inefficient, leading to unnecessary or missed calibrations.

Purpose of the Study:

  • To develop a calibration strategy based on sensor condition, not just time.
  • To implement online monitoring of sensor calibration status (OLM) for necessity-based calibrations.
  • To classify the health status of production and reading equipment using a single dataset.

Main Methods:

  • Simulated measurement signals from four sensors.
  • Applied Artificial Intelligence and Machine Learning with unsupervised algorithms.
  • Utilized Principal Component Analysis (PCA), K-means clustering, and Hidden Markov Models (HMM).

Main Results:

  • Successfully classified equipment health status using three HMM hidden states.
  • Developed an HMM filter to eliminate errors from the original signal.
  • Individually assessed sensor failures using HMM and time-domain statistical features.

Conclusions:

  • A single dataset can yield distinct information for equipment and sensor health assessment.
  • The proposed strategy enables condition-based calibration, optimizing maintenance and improving data reliability.
  • Online monitoring of sensor calibration status (OLM) enhances the efficiency of industrial metrology.