This article describes the design of a new test rig for measuring the friction force in pneumatic components and seals. The test rig was designed in order to measure both the overall friction force in a pneumatic cylinder or a valve as a whole, and the single contribution to friction force caused by the sliding seals. To this end, special fixtures and devices were designed and manufactured in order to measure the friction force in piston seals, rod seals and cartridge valve seals individually. Results of friction measurements carried out on pneumatic cylinders with similar characteristics but produced by different manufacturers are presented and compared. A test procedure and a methodology, in order to separate the contributions of the individual seals from the overall friction of the cylinders, is presented.

This paper presents an analytical model and its experimental validation for assessing friction forces in reciprocating seals for single and double rod pneumatic cylinders. The contributions of individual piston and rod seals are analyzed using a combination of analytical and experimental approaches. The former entails a friction force formulation based on a lumped parameter seal model, whereas the latter involves carrying out measurements on complete actuators. Selecting appropriate test conditions made it possible to analyze separately the contributions that individual seals make to the overall friction measured on complete actuators. Analysis results were validated by means of friction measurements on the actuators seals, which were tested individually in specific devices. The experimental results were interpreted on the basis of the proposed analytical formulation and confirmed its validity.

One of the most significant goals of earthmoving equipment is to maximize productivity during loading cycles. A real-time knowledge of the forces exchanged between the machine implement and the surrounding, that is, while digging, can be used in different ways to increase productivity. It can be used to determine the amount of material moved by the machine; or to find the optimal bucket trajectory; moreover, as input to traction control systems. This article presents an online force estimation algorithm able to predict vertical and horizontal forces exchanged between the front-loader and the surrounding environment, as well as the reaction forces through the implement itself. Taking the case of a 14-ton wheel loader as reference, this article illustrates the development of a simulation model for the analysis of the machine digging system, along with the instrumentation and testing of the proposed estimation algorithm. The model is divided into two sections describing, respectively, system kinematic and system dynamics. The kinematic model of the front-loader is compared against measurements, and results show an average error lower than 1%. The dynamic model predicts both hydraulic and dynamic features of the machine implement, achieving an accuracy on the payload mass within 2%–3%, even during dynamic conditions. The estimated pushing force reflects the expected behavior when tested for various pushing efforts and in different ground conditions. Eventually, the algorithm was tested on a complete loading cycle and the results show good consistency considering the measured front-loader trajectory and vehicle speed. The proposed model overcomes some drawbacks of the currently used technologies. For example, it allows for an online estimation of the bucket forces for any position of the implement. Although the results discussed in this article pertain to a specific reference machine, the proposed method can be extended to most wheel loaders equipped with standard digging equipment.

This study investigates the potential of deploying a neural network model on an advanced programmable logic controller (PLC), specifically the Finder Opta™, for real-time inference within the predictive maintenance framework. In the context of Industry 4.0, edge computing aims to process data directly on local devices rather than relying on a cloud infrastructure. This approach minimizes latency, enhances data security, and reduces the bandwidth required for data transmission, making it ideal for industrial applications that demand immediate response times. Despite the limited memory and processing power inherent to many edge devices, this proof-of-concept demonstrates the suitability of the Finder Opta™ for such applications. Using acoustic data, a convolutional neural network (CNN) is deployed to infer the rotational speed of a mechanical test bench. The findings underscore the potential of the Finder Opta™ to support scalable and efficient predictive maintenance solutions, laying the groundwork for future research in real-time anomaly detection. By enabling machine learning capabilities on compact, resource-constrained hardware, this approach promises a cost-effective, adaptable solution for diverse industrial environments.