Machine Learning Based Preprocessing to Ensure Validity of Cross-Correlated Ultrasound Signals for Time-of-Flight Measurements

High precision ultrasonic time-of-flight measurement is a well known part of non-destructive evaluation used in many scientific and industrial applications, for example stress evaluation or defect detection. Although ultrasonic time-of-flight measurements are widely used there are some limitations where high noise and distorted ultrasonic signals are conflicting with the demand for high precision measurements. Cross-correlation based time-of-flight measurement is one strategy to increase reliability but also exhibits some ambiguous correlation states yielding to wrong time-of-flight results. To improve the reliability of these measurements a new machine learning based approach is presented based on experimental data collected on tightened bolts. Due to the complex structure of the bolts the ultrasonic signal is influenced by boundary conditions of the geometry which lead to high number of the ambiguous cross-correlation results in practice. In this particular application, bolts are in practice evaluated discontinuously and without knowledge of the time-of-flight in the unloaded condition which prevents the use of all other available comparative preprocessing techniques to detect time-of-flight shifts. Three different preprocessing strategies were investigated based on variations in the bolting configurations to ensure a machine learning based model capable of predicting the state of the cross-correlation function for different bolting parameters. With this approach, we achieve up to 100% classification accuracy for both longitudinal and transversal ultrasonic signals under laboratory conditions. In the future the method should be extended to become more robust and be applicable in real-time for industrial applications.

Automated summary

The article introduces a machine learning based approach to improve the reliability of ultrasonic time-of-flight measurements, using experimental data and different preprocessing strategies based on variations in bolting configurations to ensure accuracy of the model.