Multi-fault detection of rolling element bearings under harsh working condition using IMF-based adaptive envelope...

Area of Science:

• Mechanical Engineering
• Signal Processing
• Condition Monitoring

Background:

• Harsh operating conditions (varying speed, load, shocks) degrade vibration signal quality.
• Low signal-to-noise ratio and non-stationary parameters challenge existing bearing diagnostic methods.

Purpose of the Study:

• To propose a novel method for reliable rolling element bearing fault detection under adverse conditions.
• To enhance diagnostic accuracy by addressing signal noise and speed variations.

Main Methods:

• Ensemble Empirical Mode Decomposition (EEMD) for adaptive signal decomposition into intrinsic mode functions (IMFs).
• Envelope order tracking to convert IMF envelopes to the angular domain, mitigating speed variation effects.
• Fault sensitive analysis using a matrix to select the optimal IMF for fault diagnosis.

Main Results:

• The proposed IMF-based adaptive envelope order analysis (IMF-AEOA) effectively decomposes vibration signals.
• Envelope order tracking clarifies bearing characteristic frequencies in the envelope order spectrum.
• Accurate identification of single and multiple bearing faults was achieved using simulated and experimental data, even under challenging conditions.

Conclusions:

• IMF-AEOA demonstrates superior performance in bearing fault detection under harsh operating environments.
• The method provides a robust solution for identifying bearing faults characterized by time-varying speeds and significant extraneous shocks.