Variational Mode Decomposition Applications in Fault Diagnosis

Variational Mode Decomposition (VMD) has emerged as a robust adaptive signal processing technique for the diagnosis of mechanical faults in rotating machinery. By decomposing a complex vibration or acoustic signal into a finite set of narrow-band components, VMD isolates characteristic fault-related frequencies with high spectral compactness and reduced mode mixing. Critical parameters such as the number of modes and penalty factor govern the quality of decomposition, and their careful selection is essential for accurate feature extraction. In recent years, enhancements to the standard framework have included noise‐assisted data analysis to mitigate end effects, optimisation of decomposition parameters through evolutionary and swarm‐based algorithms, and integration with energy operators or machine‐learning classifiers. These advancements have extended the practical reach of VMD across applications such as rolling bearing defect detection, gear box fault classification and rotor imbalance assessment, demonstrating increased sensitivity to incipient faults, improved noise resilience and generally higher diagnostic accuracy than empirical mode decomposition and wavelet‐based methods.

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Variational Mode Decomposition Applications in Fault Diagnosis publication trend

The graph below shows the total number of articles in variational mode decomposition applications in fault diagnosis across all publications each year (not limited to Nature Index journals).

Technical terms

Variational Mode Decomposition (VMD): A variational approach to decompose a non‐stationary signal into a predefined number of orthogonal narrow‐band components by solving an optimisation problem in the frequency domain.

Intrinsic Mode Function (IMF): A component extracted by VMD characterised by a well‐defined instantaneous frequency and limited bandwidth, representing a single physical resonance or fault mode.

Penalty Factor (α): A regularisation parameter in the VMD algorithm that controls the trade‐off between signal reconstruction fidelity and the bandwidth of each decomposed mode.

Particle Swarm Optimisation (PSO): A population‐based heuristic algorithm inspired by social behaviour patterns, used to optimise the VMD parameters by minimising a chosen objective function.

References

1. Gear Fault Diagnosis Based on Genetic Mutation Particle Swarm Optimization VMD and Probabilistic Neural Network Algorithm. IEEE Access (2020).
2. Research on a Novel Improved Adaptive Variational Mode Decomposition Method in Rotor Fault Diagnosis. Applied Sciences (2020).
3. Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing. Entropy (2021).