Purdue University Graduate School

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Innovation through energy saving and condition monitoring of material handling machines

posted on 2024-05-17, 14:31 authored by Annalisa SciancaleporeAnnalisa Sciancalepore

One of the most often utilized machinery in fluid power applications is the material-handling machines, which includes telehandlers, forklifts, cranes, and scissor lifts that are used from constructions to mining.
Counterbalance valves (CBVs), hydraulic components that protect the system from failures and manage the load under overrunning load conditions due to their distinctive design, are used in material-handling devices to ensure both the operators' and most off-road vehicles' safety. However, they present a significant shortcoming: the over-pressurization of the supply line, which leads to constringent energy consumption. The primary motivation for this work is this drawback. In this work, a CBV-based system with an adjustable pilot has been investigated using a truck-mounted hydraulic crane as a reference machine.

By analyzing theoretically and experimentally the behavior of this novel hydraulic system, it is possible to achieve up to 90% of energy-saving than a baseline configuration of a load-holding machine by controlling the opening of the CBV by adjusting the pressure at the pilot stage. After exploring the capabilities of the studied system and the possible control strategies to control opening of the CBV, this work suggests two different solutions to control the system: “Smart CBV” and “Smart System” modes. By properly controlling the signal on the pilot stage of the CBV, "Smart CBV" enables energy savings of up to 80%. On the other hand, the "Smart System" mode can save up to 95% of energy by using the CBV as a meter-out element that successfully regulates the flow to the actuator and, consequently, its velocity. To attain these outstanding results, it is essential to maintain proper system control.

Moreover, since safety is one of the priorities of this type of machine, a Condition Monitoring (CM) model is developed to ensure the actual functionalities of the novel proposed system. By identifying faulty conditions and preventing breakdowns before they occur, CM can be utilized to improve the safety of these type of machines. However, training a CM model using experimental data is time-consuming and expensive since it requires abundant data with different extent of machine failures from the field test. The solution suggested in this work is to generate faulty and healthy data for the reference machine using a high-fidelity simulation tool to train a CM model.

Particular focus is given to the counterbalance valve (CBV), a crucial element for the hydraulic system of material handling machines, and the linear actuator (hydraulic cylinder). The different types of faults on two elements are modeled with an approach validated using experimental tests. Considering that the simulation model provides comparable outcomes to training on empirical data, the CM model is trained in a single fault condition and multi faults conditions using simulated data. Instead, the CM model is tested using the experimental tests in multiple faulty conditions on the chosen components.

Moreover, finding the best CM model for this case study is another goal of this work. As a result, several CM models are investigated: Random Forest (RF), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). In terms of precision and recall, metrics frequently employed in the CM field to assess the performances of the designed CM model, the results generally indicate more than 90% accuracy.


Degree Type

  • Doctor of Philosophy


  • Agricultural and Biological Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Dr. Andrea Vacca

Advisor/Supervisor/Committee co-chair

Dr. Jose M Garcia Bravo

Additional Committee Member 2

Dr. Lizhi Shang

Additional Committee Member 3

Dr. Gregory M. Shaver