Abstract: In real-world engineering environments, faults in rotating machines typically occur for concise periods, which leads to poor stability and low accuracy in fault diagnosis. The traditional fault diagnosis of rotating machinery relies on analyzing time-series data to detect system degradation and faulty components. However, the complexity of rotating machinery and the presence of multiple fault types across different operating conditions challenges for conventional classification techniques. This paper proposes a LASSO regression-based feature extraction method with adaptive window based on Dynamic Time Warping (DTW) for fault diagnosis in rotating machinery. The approach effectively extract features by modeling the relationship between shaft rotational speeds (25, 50, and 75 rpm) and vibration signals from piezoelectric accelerometers. This research focus on single and combination faults analysis to include 11 faults, enhancing its applicability to real-world fault conditions. To assess its effectiveness, the proposed method is evaluated against Principal Component Analysis (PCA) and Independent Component Analysis (ICA) using the K-Nearest Neighbors (KNN) classifier. The experimental results demonstrate that the LASSO-based approach consistently achieves high classification accuracy across different speeds, outperforming PCA and ICA in both single and double fault scenarios. These findings highlight LASSO regression as a robust feature extraction technique for improving fault detection and predictive maintenance in rotating machinery.

Jirayu Samkunta, Patinya Ketthong, Nghia Thi Mai, Md Abdus Samad Kamal, Iwanori Murakami, Kou Yamada and Nattagit Jiteurtragool, “LASSO-Based Feature Extraction with Adaptive Windowing via DTW for Fault Diagnosis in Rotating Machinery” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160683

@article{Samkunta2025,
title = {LASSO-Based Feature Extraction with Adaptive Windowing via DTW for Fault Diagnosis in Rotating Machinery},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160683},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160683},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Jirayu Samkunta and Patinya Ketthong and Nghia Thi Mai and Md Abdus Samad Kamal and Iwanori Murakami and Kou Yamada and Nattagit Jiteurtragool}
}