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Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically...
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Steel manufacturing is a multi-stage process that begins by smelting iron ore into cast iron in a blast furnace. This initial stage involves layering iron ore with coke, a type of fuel, and crushed limestone within the furnace. The coke is ignited with a high volume of air, leading to the creation of carbon monoxide, which acts to reduce the iron ore to pure iron.
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Cycle-Informed Triaxial Sensor for Smart and Sustainable Manufacturing.

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Summary
This summary is machine-generated.

This study presents a new framework for predictive maintenance using only accelerometer data. It effectively detects machine degradation with low computational cost, ideal for industrial settings.

Keywords:
CNC machiningIndustry 5.0condition monitoringembedded systemsempirical mode decompositionpredictive maintenancesmart manufacturingtriaxial accelerometervibration analysis

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Area of Science:

  • Manufacturing Engineering
  • Signal Processing
  • Machine Health Monitoring

Background:

  • Industry 4.0 and 5.0 drive demand for intelligent, sustainable manufacturing.
  • Robust predictive maintenance is hindered by operational variability and limited data access.
  • Existing methods struggle with unstable process data segmentation, leading to misinterpretation.

Purpose of the Study:

  • To introduce a lightweight, embedded-compatible framework for health status signature extraction.
  • To enable predictive maintenance using only single-axis accelerometer data.
  • To address challenges in segmenting unstable process data for accurate diagnostics.

Main Methods:

  • Empirical Mode Decomposition (EMD) for health status signature extraction.
  • Cycle-synchronized segmentation using accelerometer-derived velocity and cross-correlation.
  • Frequency domain analysis of extracted intrinsic mode functions (IMFs).

Main Results:

  • The proposed framework effectively detects subtle machine degradations.
  • The method demonstrates a low computational footprint, suitable for embedded systems.
  • Successful evaluation on a real-world manufacturing benchmark.

Conclusions:

  • The developed framework is effective for health status signature extraction in dynamic machining conditions.
  • The cycle-synchronized segmentation strategy overcomes limitations of controller-less systems.
  • The approach is suitable for deployment in embedded predictive maintenance systems, especially on legacy machinery.