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A Machine Condition Monitoring Framework Using Compressed Signal Processing.

Meenu Rani1, Sanjay Dhok1, Raghavendra Deshmukh1

  • 1Centre for VLSI and Nanotechnology, VNIT Nagpur, Nagpur 440010, India.

Sensors (Basel, Switzerland)
|January 16, 2020
PubMed
Summary
This summary is machine-generated.

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This study introduces compressed signal processing (CSP) for real-time machinery fault diagnosis. CSP bypasses signal reconstruction, reducing power and time costs for effective bearing condition monitoring.

Area of Science:

  • Mechanical Engineering
  • Signal Processing
  • Condition Monitoring

Background:

  • Vibration monitoring of rotating machinery is essential for plant operations.
  • Conventional Nyquist sampling for wireless monitoring is power-intensive due to high data rates.
  • Compressive Sensing (CS) reduces data but its reconstruction is computationally expensive for real-time applications.

Purpose of the Study:

  • To present a novel bearing condition monitoring framework using Compressed Signal Processing (CSP).
  • To enable real-time machinery fault diagnosis by avoiding signal reconstruction.
  • To reduce computational time and power consumption in vibration monitoring.

Main Methods:

  • Utilizing Compressed Signal Processing (CSP) to solve inference problems directly from compressed measurements.
Keywords:
bearing fault diagnosiscompressed signal processingcompressive sensingfeature extractionrandom demodulator

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  • Developing a framework for bearing condition monitoring based on CSP.
  • Comparing the proposed CSP method with conventional CS reconstruction techniques.

    • Main Results:

    • The CSP framework significantly reduces computational time and power costs.
    • The proposed method achieves comparable fault classification accuracy to conventional methods.
    • Real-time machinery fault diagnosis is enabled by omitting signal reconstruction.

    Conclusions:

    • Compressed Signal Processing (CSP) offers an efficient alternative to traditional CS for machinery condition monitoring.
    • The proposed CSP-based framework facilitates faster and more power-efficient real-time fault diagnosis.
    • This approach enhances the sustainability and reliability of rotating machinery through advanced monitoring.