Acoustic Emission Techniques for Condition Monitoring in Rotating Machinery

Acoustic emission (AE) monitoring captures high-frequency stress waves generated by rapid energy releases within materials, offering a highly sensitive approach for early fault detection in bearings, gears and other rotating components. Unlike conventional vibration sensing, AE can detect incipient surface cracks, subsurface fatigue and lubrication anomalies before they manifest as macroscopic vibration or noise. Sensors mounted on housings acquire transient bursts that are analysed by time-frequency methods, such as wavelet and envelope analysis, or by statistical and learning algorithms. Signal processing pipelines typically involve noise reduction, feature extraction and pattern classification, enabling real-time condition assessment and predictive maintenance. AE techniques have been applied across industries—from gas turbines to wind generators—to track bearing wear, gear tooth damage and unsteady loads. Advances in sensor miniaturisation, wireless data transmission and machine learning have further enhanced diagnostic accuracy and reduced diagnostic latency, supporting global efforts to improve reliability and energy efficiency in rotating machinery.

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Acoustic Emission Techniques for Condition Monitoring in Rotating Machinery publication trend

The graph below shows the total number of articles in acoustic emission techniques for condition monitoring in rotating machinery across all publications each year (not limited to Nature Index journals).

Technical terms

Acoustic Emission (AE): High-frequency elastic waves generated by sudden localised stress releases within a material.

Wavelet Transform: A time-frequency analysis method that decomposes signals into components at different scales for transient feature extraction.

Envelope Analysis: Technique that derives the amplitude modulation profile of a signal to highlight periodic impacts or bursts.

Deep Convolutional Neural Network: A multilayer learning model that automatically extracts hierarchical features from time-frequency representations for classification tasks.

Information Entropy Penalty Factor: A combined parameter quantifying disorder in AE energy distribution, enhanced by deep learning to detect early fault signatures.

References

1. Gas turbine failure classification using acoustic emissions with wavelet analysis and deep learning. Expert Systems with Applications (2023).
2. Early sub-surface fault detection in rolling element bearing using acoustic emission signal based on a hybrid parameter of energy entropy and deep autoencoder. Measurement Science and Technology (2023).
3. Early Detection of Subsurface Fatigue Cracks in Rolling Element Bearings by the Knowledge-Based Analysis of Acoustic Emission. Sensors (2022).