Improved Acoustic Data Generation and Classification for Fault Detection in Prognostics and Health Management
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Abstract
This research is investigating how acoustic knowledge can be used to improve data classification and generation for acoustic and ultrasonic data. Supervised machine learning methods are first used to study feature selection for a water pipeline acoustic dataset via overall classification accuracy. While there is sufficient fault and no-fault data for binary fault diagnosis, some subclasses of faults have few representative samples and insufficient data. Generative methods are being investigated to supplement existing data for specific fault types based on statistical and acoustic features. This work is also investigating signal generation for weld crack detection and localization in order to extend the use of generative methods to multiple sensors. By understanding the dispersive signals that could be seen at several sensors, the effect of data generation for a broader range of acoustic data can be studied. Incorporating acoustic knowledge in model creation and training to generate time series data can hopefully provide better insights on what generative methods are most useful for system diagnostics. This work aims to quantify results using a variety of methods, including classification accuracy from the supervised machine learning methods and direct comparison of generated data and actual data for usability in actual systems.
How to Cite
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Acoustics, Machine Learning, Data Classification, Data Generation
Bianco, M. J., Gerstoft, P., Traer, J., Ozanich, E., Roch, M. A., Gannot, S., & Deledalle, C.-A. (2019, November). Machine learning in acoustics: Theory and applications. The Journal of the Acoustical Society of America, 146(5), 3590–3628.
Brophy, E., Wang, Z., She, Q., & Ward, T. (2023, February). Generative adversarial networks in time series: A systematic literature review. ACM Comput. Surv., 55(10). 10.1145/3559540
Gil, D., Grochowina, M., & Leniowska, L. (2019). Detecting of the rolling bearing state based on acoustic signal and the k-nn classifier. In 2019 signal processing: Algorithms, architectures, arrangements, and applications (spa) (pp. 246–249).
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., . . . Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
Jafari, F., & Dorafshan, S. (2021). Bridge inspection and defect recognition with using impact echo data, probability, and naive bayes classifiers. Infrastructures, 6(9), 132.
Liu, R., Zayed, T., & Xiao, R. (2024). Advanced acoustic leak detection in water distribution networks using integrated generative model. Water Research, 254, 121434.
Navada, A., Ansari, A. N., Patil, S., & Sonkamble, B. A. (2011). Overview of use of decision tree algorithms in machine learning. In 2011 ieee control and system graduate research colloquium (p. 37-42). 10.1109/ICSGRC. 2011.5991826
Naveed, H., Khan, A. U., Qiu, S., Saqib, M., Anwar, S., Usman, M., . . . Mian, A. (2025, August). A comprehensive overview of large language models. ACM Trans. Intell. Syst. Technol., 16(5). 10.1145/3744746
Papandrea, P. J., Frigieri, E. P., Maia, P. R., Oliveira, L. G., & Paiva, A. P. (2020). Surface roughness diagnosis in hard turning using acoustic signals and support vector machine: A pca-based approach. Applied Acoustics, 159, 107102.
Prajapati, A., Bechtel, J., & Ganesan, S. (2012). Condition based maintenance: a survey. Journal of Quality in Maintenance Engineering, 18(4), 384–400.
Ramashini, M., Abas, P. E., Grafe, U., & De Silva, L. C. (2019). Bird sounds classification using linear discriminant analysis. In 2019 4th international conference and workshops on recent advances and innovations in engineering (icraie) (pp. 1–6).
Yin, J., Wang, Y., Xu, Z., Yang, Y., & Zhu, M. (2025). Masgan: a malfunction sound detection model for industrial machines using the generative adversarial networks. Expert Systems with Applications, 293, 128602.
Zhao, X., Xu, F., Song, D., Shen, J., & Ma, T. (2023). A novel blade crack detection method based on diffusion model with acoustic-vibration fusion. In 2023 ieee 21st international conference on industrial informatics (indin) (pp. 1–7).
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