Diesel engine vibration signal decomposition method based on successive multivariate variational mode decomposition

To address the challenges of mode aliasing and endpoint effects inherent in current diesel engine vibration signal decomposition methods, and to enhance the reliability of subsequent anomaly detection and fault diagnosis, this paper proposes a novel approach by integrating successive variational mode decomposition (SVMD) with multivariate variational mode decomposition (MVMD), resulting in the Successive Multivariate Variational Mode Decomposition (SMVMD) method tailored for diesel engine vibration signals. The technical principles of SMVMD are elucidated, and its feasibility and effectiveness for processing diesel engine vibration signals are substantiated through both simulated signal analysis and real diesel engine condition monitoring data. Comparative analyses and example verifications demonstrate that SMVMD offers improved decomposition accuracy and computational efficiency relative to comparable methods, thereby presenting a significant advancement in the field of diesel engine diagnostics.