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.

The role of hydraulic systems is quite evident especially in the case of heavy machineries employed in industries, where the utilisation of high forces amid large stiffness is the prerequisite. Nevertheless, there has been substantial effort put forward in the development of advanced control strategies which finally addressed the issue of the position control. Proportional–integral–derivative control strategy happens to be one among them, which is a versatile and widely renowned approach involved in the position control in this study. Although, it is quite frequently observed that the hydraulic actuation system possesses strong nonlinearities. In this article, two different actuator position control strategies, that is, swash plate control of main pump and speed control strategy of prime mover are compared. In swash plate control strategy, the proportional–integral–derivative controller adjusts the swash plate of main pump through servo mechanism, whereas in the speed control strategy, the proportional–integral–derivative controller adjusts the speed of the electric motor through variable-frequency drive. For this purpose, two MATLAB-Simulink models are developed and validated experimentally. It is found that swash plate control strategy has better dynamic and control performance than the speed control strategy catering same position demand of the linear actuator.