A hybrid VMD-CNN-autoencoder approach for speed-invariant fault detection of parallel and angular misalignment in rotating machinery.

Early identification of unbalance and shaft misalignment is essential for averting unforeseen failures and minimizing maintenance expenses in rotor-bearing systems. This paper delineates an experimental examination succeeded by an intelligent condition monitoring system for the identification of healthy, parallel misalignment, and angular misalignment states. A custom-built Machinery Fault Simulator was utilized to get vibration responses from multi-axis accelerometers at speeds of 1200, 1500, and 1800 rpm. First, time- and frequency-domain analysis were done to look at the vibration characteristics that were associated to faults. After the vibration signals were collected in the lab, they were broken down using Variational Mode Decomposition (VMD) to get noise-robust intrinsic mode functions. A convolutional autoencoder and a one-dimensional convolutional neural network were then used to get statistical and deep features. Random Forest and XGBoost classifiers with early stopping were used to avoid overfitting and classify the faults. An ensemble learning technique was then employed to obtain the final fault classification. The suggested framework achieved an overall classification accuracy of 95.25%. The F1-scores for healthy, parallel misalignment, and angular misalignment conditions were 0.99, 0.88, and 0.87, respectively. The experimental findings indicated that parallel misalignment is defined by predominant and speed-invariant 2X harmonic components exhibiting radial energy concentration, while angular misalignment displays impulsive broadband responses characterized by substantial axial energy dominance and increased kurtosis. The results demonstrate that the proposed hybrid technique, which combines experimental and data-driven methods, provides a reliable and scalable solution for intelligent condition monitoring of variable-speed rotor-bearing systems.