This work presents a method for detecting internal leakage faults in hydraulic actuator cylinders using signal analysis and a supervised artificial neural network classifier. An artificial leakage is introduced into the hydraulic cylinder actuator, and a signal-based fault detection method is employed to process and transform the signals for internal leakage detection. The analysis focuses on extracting features from the pressure signal, particularly the peaks, which include information such as location, height, and width. After the neural network has undergone the training process, it is deployed for the purpose of categorizing fault levels into three distinct categories: “a healthy system,”“a system with a low fault,” and “a system with a high fault.” The proposed technique utilizes pressure difference signals from the cylinder chambers and extracts features from the peak signals to reduce dimensionality. The extracted features are then used for supervised training of an artificial neural network using a feed-forward algorithm. The trained network is capable of predicting the leakage class of unknown datasets. This method offers advantages such as reduced computational cost through feature extraction and dimensionality reduction, and it is capable of detecting multiple leakage classes. This study introduces a notably efficient approach, predicated on artificial neural networks, for the identification of internal leakage faults in hydraulic cylinders, with specific emphasis on faults arising from component wear and seal damage. The practical value of this research lies in its potential to significantly improve the reliability and efficiency of hydraulic systems used in heavy machinery. By utilizing neural networks for internal leakage detection, this research addresses a critical issue that can lead to costly downtime and maintenance in industrial operations.

Additive manufacturing has witnessed significant growth in recent years, revolutionizing the automotive and aerospace industries amongst others. Despite the use of additive manufacturing for creating complex geometries and reducing material consumption, there is a critical need to enhance the mechanical properties of manufactured parts to broaden their industrial applications. In this work, AISI 316L stainless steel is used to fabricate parts using three different strategies of the additively manufactured Laser Powder Bed Fusion (LPBF) technique, i.e., continuous, alternate, and island. This study aims to identify methods to optimize grain orientation and compaction support provided to the material under load, which influence the frictional and wear properties of the manufactured parts. The load-bearing capacity is evaluated by measuring the frictional and wear properties. The wear patch track is also examined to establish the physical mechanisms at the surface interface that lead to the smooth transition in response to the load. Grain orientation is compared across different strategies using Electron Backscatter Diffraction (EBSD) maps, and the influence of surface roughness on sliding behavior is also evaluated. The results demonstrate that the island scanning strategy yields the best performance for load-bearing applications, exhibiting superior grain orientation and hardness in the additively manufactured parts.

Hydrostatic transmissions are essential in applications demanding variable torque and speed, such as mining and agricultural machinery, due to their compact design, high power-to-weight ratio, and efficient variable speed control. Despite these advantages, their inherent nonlinearities and susceptibility to parametric uncertainties pose significant challenges for precise motion control. This study presents a comparative analysis of classical PID and robust H-infinity controllers for regulating the speed of hydraulic motors under varying torsional loads. A linearized uncertain system model is developed using upper Linear Fractional Transformations (LFTs) to capture key parametric uncertainties. A simplified H-infinity controller is designed to robustly manage system dynamics, particularly addressing phase lags induced by uncertain loads. Simulation results demonstrate that the H-infinity controller offers superior performance over the PID controller in terms of stability, disturbance rejection, and robustness to load fluctuations. This work contributes a practically viable robust control solution for improving the reliability and precision of electro-hydraulic systems, particularly in demanding, real-world environments.