Dynamic Balancing and Fault Diagnosis in Rotating Machinery

Rotating machinery underpins sectors from power generation and aerospace to manufacturing and transportation. Imbalances in rotor mass or stiffness lead to excessive vibration, reduced efficiency and accelerated component wear. Dynamic balancing seeks to distribute mass so that centrifugal forces are minimised throughout the operating speed range. Fault diagnosis combines vibration measurement, signal processing and model-based or data-driven methods to detect, localise and classify defects such as unbalance, misalignment, bearing degradation or shaft cracks. Advances in sensing, computational modelling and machine-learning have enabled real-time monitoring, predictive maintenance and adaptive balancing systems. Recent work integrates finite-element and modal analysis with optimisation algorithms to design counterweights and balancing actuators, while artificial-intelligence tools facilitate accurate distinction between similar fault signatures. These developments support more reliable, efficient and safe operation of high-speed rotors in critical applications worldwide.

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Dynamic Balancing and Fault Diagnosis in Rotating Machinery publication trend

The graph below shows the total number of articles in dynamic balancing and fault diagnosis in rotating machinery across all publications each year (not limited to Nature Index journals).

Technical terms

Dynamic balancing: The process of adding or redistributing mass in a rotating assembly to minimise vibration and centrifugal forces across operational speeds.

Unbalance: A condition where the mass distribution of a rotor is asymmetric about its axis, causing periodic forces and vibration.

Modal analysis: A technique for determining the vibration characteristics (natural frequencies and mode shapes) of a structure or rotor.

Prognostics: The prediction of a system’s remaining useful life or fault progression based on monitored data and models.

Wavelet time-scattering: A signal-processing method that extracts stable, low-variance features from vibration time series for fault classification.

References

1. A two-step optimization for crankshaft counterweights. Engineering Science and Technology an International Journal (2024).
2. Classification of Unbalanced and Bowed Rotors under Uncertainty Using Wavelet Time Scattering, LSTM, and SVM. Applied Sciences (2023).
3. A novel model-based unbalance monitoring and prognostics for rotor-bearing systems. Advances in Mechanical Engineering (2023).