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 demand for sustainable alternative-fuels in the transportation and agriculture domains is essential due to the quick depletion of petroleum supplies and the growing environmental challenges. The ternary-blends (diesel, biodiesel, and Methyl oleate) have the ability to report the existing challenges in this area because they offer significant promise for reducing exhaust emissions and improving engine performance. In the current work, soy methyl ester is blended with methyl oleate and diesel. The emissions and performance of blended biodiesel was conducted in common rail direct injection engine (CRDI). The characterization and physical properties were also evaluated by utilizing various methods like Fourier-Transform Infrared Spectroscopy (FTIR), UV-vis Spectroscopy (UV-vis), and Nuclear Magnetic Resonance. FTIR spectra showed the existence of the strong C=O, indicating the presence of FAME at 1745 cm⁻¹. Again, UV-vis has reported the appearance of conjugated dienes in the oxidized biodiesel. The results indicated all blended samples retained the properties of diesel. The addition of methyl oleate improved brake specific fuel consumption of blended biodiesel almost near to diesel. D50::S80:M20 produced a mean reduction in hydrocarbon 42.64% compared to diesel. The average carbon monoxide emission reduction for D50::S80:M20 was 49.36% as against diesel.

Hydraulic excavators operate in harsh environments where direct measurement of actuator chamber pressures and boom displacement is often unreliable or infeasible. This study presents a novel control strategy that estimates actuator chamber pressures from work port pressures using differential equations, eliminating the need for direct pressure or position sensors. A fuzzy logic-based proportional–derivative (PD) controller is developed to mitigate boom oscillations, particularly under high-inertia load conditions and variable operator inputs. The controller dynamically adjusts gains through fuzzy logic-based gain scheduling, enhancing adaptability across a wide range of operating conditions. The proposed method addresses the limitations of classical PID controllers, which struggle with the nonlinearities, parameter uncertainties, and instability introduced by counterbalance valves and pressure-compensated proportional valves. Experimental data is used to design fuzzy rules and membership functions, ensuring robust performance. Simulation and full-scale experimental validation demonstrate that the fuzzy PD controller significantly reduces pressure overshoot (by 23% during extension and 32% during retraction) and decreases settling time (by 31.23% and 28%, respectively) compared to conventional systems. Frequency-domain stability analysis confirms exponential stability and improved damping characteristics. The proposed control scheme enhances system reliability and safety, making it ideal for excavators operating in remote or rugged terrains where conventional sensor-based systems may fail. This approach is generalizable and does not require modifications to the existing hydraulic circuit, offering a practical and scalable solution for modern hydraulic machinery.

In this article, the heavy earth moving machinery like hydraulic excavator used in the construction and mining industries has been taken into consideration. Most of the heavy earth moving machineries used in these industries are mobile with engine as the main source of power. The work deals with two different hydraulic circuits: first the proposed one and second the conventional one, and both are studied at laboratory scale, which resembles the circuit of the hydraulic excavator. In the hydraulic circuit, out of the three hydraulic linear actuators, two are connected with hydro-pneumatic accumulators and one hydro-motor is also taken into consideration, which resembles the boom, arm, bucket and swing motor, respectively. The idea behind the proposed circuit is to store substantial potential energy during downward movement of the two linear actuators in the accumulators, which resembles the boom and arm cylinder of the excavator. The stored energy is used for upward motion of the two actuators without utilizing any energy from the main pump. For the devised strategy, MATLAB/Simulink environment has been utilized for developing the simulation model and the same has been validated with the experimental data with reasonable accuracy. The effect of accumulator volume and pre-charge pressure has been studied for optimization of the accumulator size on the validated simulation model. This concept helps in saving 14.06% energy than its conventional counterpart which does not have the energy storage elements. The linear position control of the boom and the arm actuator in the proposed circuit have been accomplished using proportional–integral–derivative control and pressure-compensated proportional flow control valve and have been achieved with reasonably accuracy.

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.