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Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of

Chuancang Ding1, Ming Zhao1, Jing Lin2

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Summary
This summary is machine-generated.

A new multi-objective iterative optimization algorithm (MOIOA) effectively detects multiple faults in rolling element bearings (REBs). This method excels at extracting weak fault features from noisy vibration signals, improving predictive maintenance.

Keywords:
Correlated kurtosis (CK)Morlet wavelet filterMulti-fault diagnosisMulti-objective iterative optimization algorithm (MOIOA)Rolling element bearingsWhale optimization algorithm (WOA)

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Area of Science:

  • Mechanical Engineering
  • Condition Monitoring
  • Signal Processing

Background:

  • Rolling element bearings (REBs) are critical in machinery, making their condition monitoring essential for predictive maintenance.
  • Harsh operating conditions can lead to multiple co-existing faults in REBs, often masked by low signal-to-noise ratio (SNR) vibration data.
  • Existing methods like Maximum Correlated Kurtosis Deconvolution (MCKD) face challenges due to strict parameter requirements.

Purpose of the Study:

  • To propose a novel Multi-Objective Iterative Optimization Algorithm (MOIOA) for robust multi-fault diagnosis in REBs.
  • To enhance fault feature extraction from noisy and complex vibration signals.
  • To overcome the limitations of traditional methods like MCKD in parameter selection.

Main Methods:

  • Developed a MOIOA incorporating Correlated Kurtosis (CK) as a filter selection criterion.
  • Utilized the Whale Optimization Algorithm (WOA) to optimize Morlet wavelet filter parameters.
  • Integrated an iterative period update process to mitigate inaccuracies in CK calculations.

Main Results:

  • The MOIOA demonstrated superior performance in extracting weak fault features compared to MCKD.
  • The algorithm effectively handled signals with significant noise and harmonic interferences.
  • Validation was performed using both simulated and experimental bearing vibration data.

Conclusions:

  • The proposed MOIOA offers an efficient and robust solution for multi-fault diagnosis in rolling element bearings.
  • It overcomes the parameter sensitivity issues associated with MCKD.
  • MOIOA shows significant potential for improving the reliability of predictive maintenance in machinery.