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A Multidimensional Health Indicator Based on Autoregressive Power Spectral Density for Machine Condition Monitoring.

Roberto Diversi1, Nicolò Speciale1

  • 1Department of Electrical, Electronic and Information Engineering, University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy.

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|August 10, 2024
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Summary
This summary is machine-generated.

This study introduces a data-driven condition monitoring (CM) method using autoregressive modeling and frequency subbands for industrial equipment health. The approach effectively detects faults by analyzing signal spectrum changes, enhancing reliability and safety.

Keywords:
Fourier synchrosqueezing transformautoregressive modelingcondition monitoringdata-driven methodsfault diagnosismultidimensional health indicatorsignal processingspectral distances

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Area of Science:

  • Engineering
  • Data Science
  • Signal Processing

Background:

  • Condition monitoring (CM) is crucial for prognostics and health management (PHM) in industrial settings.
  • Effective CM enhances equipment reliability, safety, and operational efficiency.
  • Existing CM methods often require significant human intervention and system-specific tuning.

Purpose of the Study:

  • To propose a data-driven condition monitoring approach with minimal human intervention.
  • To develop a robust health indicator for detecting equipment faults.
  • To enable online condition monitoring and fault diagnosis applicable across various systems.

Main Methods:

  • Autoregressive (AR) modeling of sensor data within automatically determined frequency subbands.
  • Utilizing the synchrosqueezing transform for enhanced time-frequency signal energy distribution.
  • Constructing a multidimensional health indicator using AR power spectral density and Itakura-Saito spectral distance.

Main Results:

  • The proposed health indicator effectively detects spectral changes indicative of faults.
  • The method requires no further intervention after initial band definition and nominal spectrum calculation.
  • Successful validation was performed using the CWRU Bearing Data Center benchmark dataset.

Conclusions:

  • The developed data-driven CM approach offers an automated and effective solution for industrial equipment monitoring.
  • The method's system-agnostic nature allows broad applicability across different industrial applications.
  • This technique significantly contributes to improving industrial operational reliability and safety through advanced fault detection.