Intelligent Fault Diagnosis in Hydraulic Systems

Hydraulic systems underpin a vast array of industrial and mobile applications, from construction machinery to aerospace actuation. Faults in components such as axial piston pumps, valves and actuators can lead to substantial downtime, safety hazards and economic losses. Traditional fault diagnosis has relied on scheduled maintenance and expert analysis of vibration or pressure signals, but these approaches often struggle with the non‐linear, non‐stationary nature of hydraulic signals and the complexity of failure mechanisms. Intelligent fault diagnosis integrates advanced signal processing with machine learning and deep learning techniques to enable automatic feature extraction, real-time classification and early warning. By harnessing convolutional neural networks, transform-based decomposition methods and data-driven classifiers, recent developments have improved accuracy, robustness and onboard implementability. Key challenges include handling variable operating conditions, reducing computational burden for edge devices and fusing multi-sensor information. Continued refinement of model architectures and preprocessing techniques is enhancing the practicality of condition-based maintenance strategies and supporting the shift towards autonomous, self-diagnosing fluid power systems.

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Intelligent Fault Diagnosis in Hydraulic Systems publication trend

The graph below shows the total number of articles in intelligent fault diagnosis in hydraulic systems across all publications each year (not limited to Nature Index journals).

Technical terms

Convolutional Neural Network (CNN): A deep learning architecture that uses convolutional filters to automatically learn hierarchical features from input signals or images.

Wavelet Transform: A signal processing technique that decomposes signals into time-frequency representations, enabling analysis of transient or non-stationary components.

Ensemble Empirical Mode Decomposition (EEMD/MEEMD): An extension of empirical mode decomposition that adds noise to the original signal and averages decompositions to alleviate mode mixing.

Extreme Learning Machine (ELM): A feedforward neural network with a single hidden layer whose weights are randomly initialised and only output weights are trained, yielding rapid learning and classification.

References

1. Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet. Sensors (2021).
2. Hydraulic Pump Fault Diagnosis Method Based on EWT Decomposition Denoising and Deep Learning on Cloud Platform. Shock and Vibration (2021).
3. A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods. Sensors (2021).