What are the best sensor fusion techniques for damage identification in different scenarios?

1 Vibration-based sensor fusion

Vibration is a frequent indicator of damage or deterioration in mechanical systems, such as bearings, gears, or shafts. However, the signals can be noisy, complex, and affected by environmental factors. To improve signal quality and reduce uncertainty in damage detection, sensor fusion techniques can be used. Wavelet transform can decompose signals into different frequency components and analyze them at different scales to capture both time and frequency information. Principal component analysis is a statistical method that reduces the dimensionality of a data set by finding the most significant variables to explain variance. Neural networks are computational models that learn from data through training and optimization and can be used to fuse multiple vibration signals and classify them into different damage categories or estimate the severity of damage.

2 Acoustic emission-based sensor fusion

Acoustic emission (AE) is a phenomenon in which elastic waves are released when a material undergoes deformation, fracture, or wear. This sensitive and non-invasive technique is used to detect damage in metals, composites, or concrete. However, AE signals can be affected by noise, attenuation, or interference. Therefore, sensor fusion can be used to improve the signal-to-noise ratio, locate the source of AE, and characterize the type and extent of damage. Some common AE-based sensor fusion techniques include time-frequency analysis which reveals temporal and spectral characteristics of a signal; cluster analysis which groups data points into similar or dissimilar clusters; and Bayesian inference which updates the probability of a hypothesis or parameter based on new evidence or data. All these techniques can help to identify the onset, duration, and frequency content of AE events; classify AE events into different types or sources; and estimate the location, size, or shape of damage.

3 Thermal imaging-based sensor fusion

Thermal imaging is a technique that captures the infrared radiation emitted by an object and converts it into a visible image to show the temperature distribution on the surface. This method is useful for detecting damage or defects that affect heat transfer or dissipation, such as cracks, corrosion, or delamination. However, external factors like ambient temperature, emissivity, or reflection can also affect thermal images. To address this, sensor fusion can help to correct the thermal images, enhance the contrast, and extract the thermal features that indicate damage. Examples of thermal imaging-based sensor fusion techniques include image registration which aligns two or more images of the same scene to correct geometric distortions or misalignments; image fusion which combines two or more images from different sensors or modalities into a single image; and image segmentation which partitions an image into homogeneous regions based on criteria or algorithms to isolate thermal features and measure their size, shape, or intensity.

4 Ultrasonic testing-based sensor fusion

Ultrasonic testing (UT) is a popular technique for inspecting various types of materials and structures, such as metals, composites, or welds. However, UT signals can be affected by noise, dispersion, or scattering. To enhance signal quality, locate the position of flaws, and quantify the severity of defects, sensor fusion is used. Signal processing is a technique that applies mathematical operations or transformations to a signal to modify its characteristics or extract useful information. Array processing uses multiple sensors or transducers to form an array and applies beamforming or imaging algorithms to steer or focus sound waves in a desired direction or region. Inversion methods use mathematical models or optimization algorithms to estimate unknown parameters or properties of a system based on measured data or signals.

5 Here’s what else to consider

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