A Hybrid Data-Driven Algorithm for Real-Time Friction Force Estimation in Hydraulic Cylinders

Hydraulic systems are widely utilized in industrial applications due to their high force generation, precise control, and ability to function in harsh environments. Hydraulic cylinders, as actuators in these systems, apply force and position through the displacement of hydraulic fluid, but their operation is significantly influenced by friction force. Achieving precision in hydraulic cylinders requires an accurate friction model under various operating conditions. Existing analytical models, often derived from experimental tests, necessitate the identification or estimation of influencing factors but are limited in adaptability and computational efficiency. This research introduces a data-driven, hybrid algorithm based on Long Short-Term Memory (LSTM) networks and Random Forests for nonlinear friction force estimation. The algorithm effectively combines feature detection and estimation processes using training data acquired from an experimental hydraulic test setup. It achieves a consistent and stable model error of less than 10% across diverse operating conditions and external load variations, ensuring robust performance in complex situations. The computational cost of the algorithm is 1.51 milliseconds per estimation, making it suitable for real-time applications. The proposed method addresses the limitations of analytical models by delivering high precision and computational efficiency. The algorithm's performance is validated through detailed analysis and experimental results, including direct comparisons with the LuGre model. The comparison highlights that while the LuGre model offers a theoretical foundation for friction modeling, its performance is limited by its inability to dynamically adjust to varying operational conditions of the hydraulic cylinder, further emphasizing the advantages of the proposed hybrid approach in real-time applications.

Detection and Classification of Internal Leakage in Hydraulic Cylinders

Hydraulic systems have been one of the most used technologies in many industries due to their reliance on incompressible fluids that facilitate energy and power transfer. Within such systems, hydraulic cylinders are prime devices that convert hydraulic energy into mechanical energy. Some of the genuine and very common problems related to hydraulic cylinders are leakages. Leakage in hydraulic systems can cause a drop in pressure, general inefficiency, and even complete failure of such systems. The various ways leakage can occur define the major categorization of leakage: internal and external leakage. External leakage is easily noticeable, while internal leakage, which involves fluid movement between pressure chambers, can be harder to detect and may gradually impact system performance without obvious signs. When leakage surpasses acceptable limits, it is classified as a fault or failure. In such cases, leakage is divided into three categories: no leakage, low leakage, and high leakage. It suggests a fault detection algorithm with the basic responsibility of detecting minimum leakage within the Hydraulic system, and minimizing detection time is the core idea of this paper. In order to fully develop this idea, experimental data collection of Hydraulic systems is required. The collected data uses pressure sensors and other signals that are single-related. Due to the utilization of Long Short-Term Memory (LSTM) recurrent neural networks, more complex data analysis was enabled, which the LSTM-based leakage detection algorithm successfully achieved, providing almost 96% accuracy in classifying leakage types. Results demonstrate that the proposed method can perform real-time and online fault diagnosis for each cycle, reducing maintenance costs and prolonging the hydraulic system's lifespan.