Robust fault detection and severity classification in rotating machinery using VMD-LSTM for limited data scenarios

Fault detection in rotating machinery is critical to reliability and safety. However, it faces difficulties due to complex, noisy fault signatures, non-stationary behavior, and the impracticality of obtaining large labeled datasets, limiting the effectiveness of both traditional and deep learning-based methods in real-world applications. This paper introduces a novel approach that combines Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) networks to improve gear and bearing defect detection, filling a gap in fault diagnostics by effectively handling limited training data. VMD decomposes signals into intrinsic mode functions (IMFs), while LSTM classifies fault types and severity levels based on time-domain features extracted from the IMFs. Tested on the Case Western Reserve University Dataset (CWRUDS) for bearing defects and the Laboratory of Mechanics and Structures Dataset (LMSDS) for combined gear and bearing defects, the method outperforms vibratory analysis and conventional classifiers such as MLP, 1D-CNN, 2D-CNN, and standalone LSTM. The results show that the VMD-LSTM model is superior at reliably detecting defects and accurately diagnosing faults in complex, data-limited scenarios, making it a promising solution for machinery health monitoring.