A diagnostic logic for preventing structural failure in concrete displacing booms

During recent decades safety in the workplace has become more and more important. For this reason, systems for the diagnosis of possible failures represent a particularly attractive solution. In this paper a healthmonitoring algorithm for a specific kind of construction machinery (concrete displacing booms) is proposed. It is based on the knowledge of the geometrical and dynamic parameters of the boom, estimated through a stand-alone self-learning procedure. The proposed algorithm is able to signal alarms due to the most common failure conditions, such as overload or crack propagation. In detail, the crack propagation is detected through an Extended Kalman Filter (which identifies the variation of the boom natural frequencies during its lifetime), while the overload is estimated through force sensors mounted on the actuators. The results referred to this algorithm, numerically simulated and experimentally reproduced on a specific test rig, are presented and discussed.