Acoustic detection of bearing faults through fractional harmonics lock-in amplification

Bearing failure detection in induction motors is vital to prevent breakdowns and dead times in industrial processes. Non-invasive detection of bearing faults can be performed by analyzing sound signals and the associated spectral signature. However, measured sound often contains signals from other surrounding sources of vibrations, which can pollute analysis or degrade SNR. This work proposes the use of Lock-In Amplification (LIA) for bearing fault detection in order to overcome SNR limitations and allow specific detection.In practice, LIA is synchronized to the motor shaft frequency through a fractional Phase -locked loop (PLL) frequency synthesizer, delivering characteristic frequencies for bearing faults, which are fractional harmonics of the shaft rotation frequency, and phase-locked to the motor under study.Comparison with Fourier spectrum measurements confirms the superior ability of LIA for extracting small signals from noise, but also allows specific detection, even in the presence of external sources vibrating at the same frequencies as the motor of interest. Additionally, fractional harmonics LIA was able to detect characteristic frequencies from fault-free conditions, paving the way for earlier failure detection and continuous monitoring. This work is the first reported experimental implementation of fractional harmonics LIA for bearing fault detection purposes.

Automated summary

This paper presents a method using Lock-In Amplification (LIA) for bearing fault detection to overcome the limitations of signal-to-noise ratio (SNR) and enable specific detection. LIA is capable of extracting small signals from noise and detecting characteristic frequencies, even in the presence of external sources vibrating at the same frequencies as the motor of interest. Moreover, fractional harmonics LIA can detect characteristic frequencies from fault-free conditions, allowing for earlier failure detection and continuous monitoring.