Quantitative Monitoring of Bolt Looseness Using Multichannel Piezoelectric Active Sensing and CBAM-Based Convolutional Neural Network

The bolted connection is widely utilized in engineering to practically and rigidly couple structural components. The integrity of the connection is paramount to the safety of the structure and has prompted the development of many monitoring methods, including the piezoelectricity-based active sensing method. However, the active sensing method cannot quantify bolt looseness due to the unclear relationship between bolt looseness and the single monitoring index typically used in the active sensing method. Thus, the authors propose the unique combination of a one-dimensional convolutional neural network (1DCNN) and multichannel active sensing for quantitative monitoring of bolted connections. In an experiment, piezoelectric ceramic transducer (PZT) patches are bonded on steel plates connected by a bolt. Each patch is wired to a multichannel active sensing monitoring system. After obtaining multichannel stress wave signals at different looseness levels, a looseness vector is calculated to generate training and validation datasets. A baseline 1DCNN model and a novel model improved using the convolutional block attention module (CBAM) are used to monitor the bolt looseness. Finally, the authors verify that the multichannel active sensing method combined with the 1DCNN model can accurately perform quantitative monitoring of bolt looseness, and the monitoring accuracy of the baseline 1DCNN model is above 91.07% in three different specimens. Compared with the baseline 1DCNN model, the monitoring accuracy of the CBAMCNN model improved by approximately 5%. Overall, the method proposed in this article offers a new and highly accurate approach for quantitative monitoring of bolted connections.

Mechanical Line Fit Model to Monitor the Position of KM3NeT Optical Modules from the Acoustic and Compass/Accelerometer Sensor System Data

The KM3NeT deep-sea neutrino telescope will use thousands of Digital Optical Modules (DOMs) forming a 3D array to detect the Cherenkov’s light produced by the particles generated after a neutrino interaction in the medium. The DOMs are arranged in Detection Units (DUs), structures anchored and maintained vertical by buoyancy each one containing 18 DOMs at different height. The DOMs are, thus, subject to movements due to sea currents. For a correct reconstruction of events detected by the telescope, it is necessary to monitor the position of each DOM with 10 cm accuracy. For this, an Acoustic Positioning System (APS) with a piezo-ceramic transducer installed in each DOM and a long baseline of acoustic transmitters and receivers on the seabed is used. Besides, there is a system of compass/accelerometers in the DOMs to determine their orientation. Then, a mechanical model is used to reconstruct the shape of the DU taking as input the information from the positioning sensors and using the sea current velocity as free parameter of the DU Line Fit method. The mechanical equations consider the buoyancy and the drag force of any item in the DU line. This work describes the data process of the different sensors and systems to obtain the fit shape of DUs, the situation for the first DUs installed as an example and to study the viability and define the full process to apply in KM3NeT.

Energy Harvesting Performance of Plate Wing from Discrete Gust Excitation

Energy harvesting from aeroelastic response tends to have a wide application prospect, especially for small-scale unmanned aerial vehicles. Gusts encountered in flight can be treated as a potential source for sustainable energy supply. The plate model is more likely to describe a low aspect ratio, thin plate wing structure. In this paper, the Von Kármán plate theory and 3D doublet lattice method, coupled with a piezoelectric equation, are used to build a linear state-space equation. Under the load of “one-minus-cosine” discrete gust, the effects of flow speed and gust amplitude, thickness of piezoelectric ceramic transducer (PZTs) layers, and mounted load resistance are investigated. Results reveal that the PZTs layers on the wing root of the leading edge can obtain the highest electrical parameters. The flow velocity, thickness of the PZTs layers and load resistance are used to optimize energy harvesting data.