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The heavy earth moving machineries (HEMM) like hydraulic excavator play a major role in construction and mining industries. In this context, the energy saving strategies in hydraulic excavator needs to be addressed considering its vital importance. Since the hydraulic excavators are subjected to heavy loads, hence the opportunity to harness the potential gravitational energy (GPE) remains a key area which can be effectively explored in order to minimize the energy consumption in consideration with hydraulic excavator. In the projected system, the potential energy is stored as pressure energy in hydro-pneumatic accumulator. The upward movement of the boom is executed with the help of prime mover during the starting of the first duty cycle. In the latter duty cycles, the stored pressurized energy is utilized together with the prime mover energy capable to execute the upward movement of the boom. The position of the boom cylinder is controlled by using the conventional PID controller using proportional flow control valve (PFCV) and accumulator. The error between the actual position and demand position of the linear actuator is minimized along with attainment of superior controlled performance while utilizing Model Predictive Controller (MPC). The pressurized accumulator with PFCV has been utilized to cater the different position demands. This has been also justified both experimentally and analytically with the error in the permissible range of 2%. It has been observed that the proposed system is 10% more efficient in contrast to the conventional system.

Mobile hydraulic machines are used in various operations like construction, material handling and mining. High powers to weight ratio and manoeuvrability in rough terrains are striking features that help in edging out their electrical and mechanical counterparts. Internal leakage in hydraulic actuators is a faulty condition frequently observed in hydraulic machines. Internal leakage in the actuator affects the system’s dynamic performance and decreases its energy efficiency. Also, internal leakage is apparent only when the leakage is extreme and the actuator stops responding to command signals. Thus, detecting internal leakage in its early stages is a difficult task. Early detection and corrective action save energy, reduce component degradation, and reduce machine downtime. There are many existing techniques for internal leakage detection of hydraulic actuators, but they are intended for actuators working in a laboratory environment. The main focus of this paper is to present a practical method for early detection of internal leakage fault present in boom actuator of mobile hydraulic machines by analysing the machine work-cycle data with minimum hardware. The method trains and validates a Support Vector Machine (SVM) classifier using pressure and boom angle displacement signals. The time-series signals are processed using ’event-based’ feature extraction method. The binary version of Particle Swarm Optimisation is used for feature selection. The trained classifier can detect and classify internal leakage faults with more than 95% accuracy, which is sufficient for taking appropriate preventive maintenance steps on time.