Fault Diagnosis Method of Rotating Machinery Based on Collaborative Hybrid Metaheuristic Algorithm to Optimize VMD

With the improvement of complexity and reliability mechanical equipment, it has been difficult for commonly used variational modal decomposition method vibration signal rotating machinery to meet current practical engineering requirements. In order further improve adaptability, processing efficiency, robustness fault diagnosis methods, a collaborative hybrid element heuristic multiobjective optimization algorithm is introduced in this paper. Combined with (VMD) method, rolling bearing under complex working conditions studied. This paper mainly uses metaheuristic nondominated sorting genetic II (NSGA II) particle swarm (MOPSO), which improves convergence efficiency solves problem uneven distribution optimal solutions. Then, improved combined VMD solve parameter selection machinery. At same time, variation relationship between various features results compared studied, good effect are taken as objective function algorithm; ability denoising feature extraction improved. paper, proposed explored by using analog signals experimental data bearings. Through comparison, adaptive ability, operation speed, verified.