Deng, HaoxuanKhan, SamirErkoyuncu, John Ahmet2025-07-152025-07-152025Deng H, Khan S, Erkoyuncu JA. (2025) Random wavelet kernels for interpretable fault diagnosis in industrial systems. CIRP Annals, Volume 74, Issue 1, 2025, pp. 49-530007-8506https://doi.org/10.1016/j.cirp.2025.04.083https://dspace.lib.cranfield.ac.uk/handle/1826/23997Deep learning is a powerful method for fault diagnosis, but its "black-box" nature raises concerns in critical applications. This paper presents an interpretable, lightweight method combining random convolution kernel transformation (ROCKET) with wavelet kernels, which offer systematic time-frequency analysis and intuitive insights. Principal component analysis (PCA) is used to extract relevant patterns, forming a health indicator that guides maintenance decisions. A case study on linear actuator fault diagnosis demonstrates the method's balance of interpretability and computational efficiency, making it a valuable tool for reliable asset health monitoring in resource-limited settings.pp. 49-53enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/40 Engineering4010 Engineering Practice and EducationIndustrial Engineering & Automation4014 Manufacturing engineering4017 Mechanical engineeringFailureMachine learningMaintenanceArticle1726-0604673340741