Operational Load Monitoring of a Composite Panel Using Artificial Neural Networks

Abstract

Operational Load Monitoring consists of the real-time reading and recording of the number and level of strains and stresses during load cycles withstood by a structure in its normal operating environment, in order to make more reliable predictions about its remaining lifetime in service. This is particularly important in aeronautical and aerospace industries, where it is very relevant to extend the components useful life without compromising flight safety. Sensors, like strain gauges, should be mounted on points of the structure where highest strains or stresses are expected. However, if the structure in its normal operating environment is subjected to variable exciting forces acting in different points over time, the number of places where data will have be acquired largely increases. The main idea presented in this paper is that instead of mounting a high number of sensors, an artificial neural network can be trained on the base of finite element simulations in order to estimate the state of the structure in its most stressed points based on data acquired just by a few sensors. The model should also be validated using experimental data to confirm proper predictions of the artificial neural network. An example with an omega-stiffened composite structural panel (a typical part used in aerospace applications) is provided. Artificial neural network was trained using a high-accuracy finite element model of the structure to process data from six strain gauges and return information about the state of the panel during different load cases. The trained neural network was tested in an experimental stand and the measurements confirmed the usefulness of presented approach.

Comparison of Machine Learning Algorithms for Structure State Prediction in Operational Load Monitoring

“…A typical structure for aerospace applications is considered—hat-stiffened composite panel made of carbon/epoxy woven laminate, presented in Figure 1 a. In the author’s earlier work [ 15 ], a detailed description of the geometry, experimental measurements of the mechanical properties, as well as building and validating a finite element model, can be found.…”

“…From the finite element (FE) model, 1241 data samples of relationship between strains in positions SG1-SG6 and the value of ISF ( Supplementary S7 to [ 15 ]) were generated. When generating the data, forces of different values were applied to 31 spots at line BC3 ( Figure 1 d).…”

“…The authors of [ 15 ] present a neural network, trained with 70% of the abovementioned data generated from FE simulations, that predicts the maximal value of ISF for the whole structure based on the data from strain gauges ( Figure 4 ). Efficiency of the networks, when dealing with strain values acquired in series of experiments, was measured.…”

“…As the accuracy of OLM depends on the number of sensors, and the number is limited, monitoring of flying parameters (like speed, acceleration and mass) is often performed to estimate the state of the structure in other locations [ 14 ]. In the author’s earlier work [ 15 ] it has been proven that by implementing artificial neural networks (ANNs), the temporary state of the whole structure could be quite accurately estimated using only a relatively small number of sensors. The neural network was trained with data obtained from finite element simulations of the structure under many different load cases, and then tested with data acquired from strain gauges in series of experiments.…”

Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images

“…One of the most effective data driven modeling techniques has been proven to be artificial neural networks (ANN)s [14]. In this regard, many recent applications of neural networks have emerged in literature for monitoring of strains and stresses during load cycles using strain gauges [15], pavement defect segmentation using a deep autoencoder [16], and machine learning based continuous deflection detection for bridges using fiber optic gyroscope [17].…”