Monitoring the propagation of mechanical discontinuity using data-driven causal discovery and supervised learning

Journal Article · 15 January 2022 · Mechanical Systems and Signal Processing

DOI: https://doi.org/10.1016/j.ymssp.2021.108791 · OSTI ID:1977722

Liu, Rui ^[1]; Misra, Siddharth ^[2]

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1. Texas A & M Univ., College Station, TX (United States); OSTI
2. Texas A & M Univ., College Station, TX (United States)

Mechanical wave transmission through a material is influenced by the mechanical discontinuity in the material. The propagation of embedded discontinuities can be monitored by analyzing the wave-transmission measurements recorded by a multipoint sensor system placed on the surface of the material. The proposed workflow monitors the propagation of mechanical discontinuity through three stages, namely initial, intermediate, and final stages, by using supervised learning followed by data-driven causal discovery. To the end, the workflow processes the multipoint waveform measurements resulting from a single impulse source, while considering the effects of wave attenuation, dispersion and multiple wave-propagation modes due to the discontinuity and material boundaries. Among various feature reduction techniques ranging from decomposition methods to manifold approximation methods, the features derived based on statistical parameterizations of the measured waveforms lead to reliable monitoring that is robust to changes in precision, resolution, and signal-to-noise ratio of the multipoint sensor measurements. The numbers of zero-crossing, negative-turning, and positive turning in the waveforms are the strongest causal signatures of the propagation of mechanical discontinuity. Higher order moments of the waveforms, such as variance, skewness and kurtosis, are also strong causal signatures of the propagation. Finally, the newly discovered causal signatures confirm that the statistical correlations and conventional feature rankings are not always statistically significant indicators of causality.

• Research Organization: Texas A & M Univ., College Station, TX (United States). Texas A & M Engineering Experiment Station
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)
• Grant/Contract Number: SC0020675
• OSTI ID: 1977722
• Journal Information: Mechanical Systems and Signal Processing, Journal Name: Mechanical Systems and Signal Processing Vol. 170; ISSN 0888-3270
• Publisher: ElsevierCopyright Statement
• Country of Publication: United States
• Language: English