A Fiber Bragg Grating-Based Measurement Method for Outer-Ring Fault Detection in Rolling Bearings

Area of Science:

• Mechanical Engineering
• Sensor Technology
• Signal Processing

Background:

• Rolling bearings are critical components in rotating machinery.
• Early detection of outer-ring faults is essential for preventing catastrophic failures.
• Traditional methods struggle to detect weak fault signatures buried in noise.

Purpose of the Study:

• To propose and validate a new method for detecting outer-ring faults in rolling bearings.
• To enhance the detection of weak fault-related vibration signals.
• To improve the diagnostic accuracy and reliability of bearing fault detection.

Main Methods:

• A fiber Bragg grating (FBG) sensor was embedded in a machined groove on the bearing outer ring for near-source dynamic strain measurement.
• Local Mean Decomposition (LMD) was used to decompose the strain signal.
• A correlation coefficient-kurtosis weighted criterion selected relevant LMD components for reconstruction.
• Maximum Correlated Kurtosis Deconvolution (MCKD) was applied to enhance weak fault impulses.
• Experimental validation was performed on a cylindrical roller bearing with an artificial outer-ring defect.

Main Results:

• The raw FBG signal contained faint outer-ring fault characteristic frequencies, obscured by noise.
• LMD-MCKD processing significantly enhanced the distinguishability of fault characteristic frequencies and harmonics in the envelope spectrum.
• The fault feature amplitude ratio increased from 12.257 (MED) to 16.985 (LMD-MCKD).
• The local signal-to-noise ratio improved from 21.767 dB (MED) to 24.555 dB (LMD-MCKD).

Conclusions:

• The proposed FBG-based near-source strain measurement is effective for outer-ring fault detection.
• The LMD-MCKD signal processing technique significantly improves the detection of weak fault signatures.
• This combined approach offers a robust and sensitive method for rolling bearing diagnostics.