Enhancing bearing and gear fault diagnosis: A VMD-PSO approach with multisensory signal integration

In the domain of signal analysis for machinery health monitoring and fault diagnosis, this paper introduces a comprehensive methodology that integrates Variational Mode Decomposition (VMD), Particle Swarm Optimization (PSO), and advanced machine learning techniques. The primary objective of this framework is to establish a robust and precise approach for signal decomposition, determining the optimal number of Intrinsic Mode Functions (IMF), and calculating key indicators, including L2/L1, Hoyer Index, and Geometric Mean Improved Gini Index (GMIGI). The methodology initiates with VMD-based signal decomposition, followed by the utilization of PSO to identify the most appropriate number of IMFs for accurate feature extraction. Subsequently, each IMF’s performance is assessed by evaluating its correlation with the input signal, and the IMF with the highest Pearson coefficient is selected as the primary feature for diagnostic purposes. To ensure the robustness and comparability of these indicators, a standardization process is implemented. The standardized indicators are then employed for machinery fault diagnosis, utilizing a diverse set of machine learning algorithms such as support vector machines and discriminant analysis. The proposed methodology undergoes rigorous validation using vibration, acoustic, and current signals, providing a versatile solution for the condition monitoring and diagnosis of mechanical systems. For model validation, we utilize four datasets comprising two vibrational, one acoustic, and one electrical dataset. The experimental results affirm the effectiveness of our approach in accurately detecting and diagnosing faults, thereby contributing to the reliability and maintenance efficiency of industrial machinery.