Hydraulic System Fault Detection and Diagnostics
Summary
Hydraulic systems underpin a wide array of industrial, mobile and aerospace applications by converting fluid power into precise mechanical motion. The integrity of these systems is paramount, yet they are susceptible to diverse fault modes such as internal leakage, component wear, contamination ingress and seal degradation. Fault detection encompasses the timely identification of abnormal system behaviour, while diagnostics seeks to pinpoint the root cause and severity of the deviation. Together, these processes enable condition-based maintenance, reduce unplanned downtime and extend service life. Advances in signal processing, model-based analysis and machine learning have progressively enhanced the sensitivity and reliability of detection strategies. Notably, contemporary research emphasises early warning through subtle changes in pressure, flow and acoustic emissions, as well as the deployment of data-driven algorithms that can adapt to system non-linearities and time-varying dynamics. The global significance of hydraulic fault management extends from offshore wind-turbine pitch controls and heavy-duty construction equipment to aviation landing gear, where safety and cost-efficiency are critical. Practical implementations now integrate sensor fusion, prognostic modelling and on-board analytics to deliver real-time health assessment and remaining useful life estimates. By uniting robust detection with precise diagnostics, modern hydraulic systems achieve higher reliability, lower maintenance budgets and improved operational sustainability.
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Hydraulic System Fault Detection and Diagnostics publication trend
The graph below shows the total number of articles in hydraulic system fault detection and diagnostics across all publications each year (not limited to Nature Index journals).
Technical terms
Hydraulic system: A network of pumps, valves, actuators and fluid conduits that transmits power via pressurised fluid to perform mechanical work.
Fault detection: The process of recognising deviations from normal operating conditions, typically through analysis of sensor signals or system outputs.
Diagnostics: The procedure of isolating and identifying the specific cause, location and severity of a detected fault.
Contamination: The ingress or generation of solid particles, water or other unwanted substances in hydraulic fluid that impair component performance and accelerate wear.
Convolutional neural network (CNN): A class of deep learning model employing convolutional filters to automatically extract hierarchical features from input data streams for classification or regression tasks.
References
- Wear of hydraulic pump with real particles and medium test dust. Wear (2023).
- Degradation of Hydraulic System due to Wear Particles or Medium Test Dust. Applied Sciences (2023).
- Fault Diagnosis for Aircraft Hydraulic Systems via One-Dimensional Multichannel Convolution Neural Network. Actuators (2022).
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