Acoustic Emission Techniques in Tribological Analysis

Acoustic emission (AE) techniques have emerged as a powerful approach for real-time monitoring of friction, wear and lubrication phenomena in interacting surfaces. By detecting transient elastic waves generated by rapid energy release within a material, AE sensors capture high-frequency signals that correlate with microscale events such as asperity deformation, crack initiation and particle detachment. Unlike conventional vibration or temperature measurements, AE offers superior sensitivity to incipient damage and enables localisation of sources with appropriate sensor arrays. Signal processing in both time and frequency domains, often augmented by wavelet or time-frequency transforms, facilitates discrimination between distinct wear mechanisms and operational states. Recent advances in data-driven methods have further enhanced the interpretative power of AE, with machine-learning algorithms applied to feature extraction, classification of lubrication regimes and early fault detection. The combination of probabilistic modelling and deep-learning architectures has improved robustness against background noise and variable operating conditions. Practical applications span condition monitoring of bearings, bolted joints and mechanical seals in industries as diverse as aerospace, power generation and manufacturing. The global significance of AE in tribology lies in its ability to reduce unplanned downtime, extend component life and support predictive maintenance strategies, thereby improving energy efficiency and safety in critical mechanical systems.

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Acoustic Emission Techniques in Tribological Analysis publication trend

The graph below shows the total number of articles in acoustic emission techniques in tribological analysis across all publications each year (not limited to Nature Index journals).

Technical terms

Acoustic emission (AE): High-frequency elastic waves produced by rapid energy release in a material, used for monitoring friction and wear.

Burst-type AE: Discrete, high-amplitude signal packets associated with rapid events such as crack initiation.

Continuous-type AE: Sustained, lower-amplitude emissions linked to ongoing processes such as sliding or vibration.

Nonparametric Bayesian clustering: A probabilistic method that infers the number of data clusters from the observations, without predefined thresholds.

Variational autoencoder (VAE): A deep-learning model that learns compact representations of data distributions to detect anomalies by reconstruction error.

References

1. Time Domain Signal Analysis Using Wavelet Packet Decomposition Approach. International Journal of Communications Network and System Sciences (2010).
2. Analysis of friction‐related acoustic emission in bolted joint structures. International Journal of Mechanical System Dynamics (2023).
3. Friction and Wear Monitoring Methods for Journal Bearings of Geared Turbofans Based on Acoustic Emission Signals and Machine Learning. Lubricants (2020).
4. Tribological behaviour diagnostic and fault detection of mechanical seals based on acoustic emission measurements. Friction (2018).