Multimodal Temperature Sensing Using Electromechanical Impedance (EMI) for Structural Health Monitoring

In this article, we propose a new multimodal sensing approach in which electromechanical impedance (EMI) signatures are used to sense temperature in addition to structural health monitoring (SHM) for which the technique is commonly used. Here, we use machine learning to estimate temperature differences between signatures, enabling the EMI sensor to serve not only as a corrosion sensor but also as a temperature sensor. In this study, we collected two comprehensive datasets, each consisting of nine EMI signatures from a steel rod in a healthy and damaged state. The datasets span temperatures ranging from −10 °C to +30 °C with the steps of 5 °C. A peak finding algorithm was used to preprocess the datasets, and several machine learning models were implemented via scikit-learn, trained, and evaluated. The overall best model was the support vector regressor using radial basis function (RBF) kernel, which achieved a mean-squared error (MSE) of 0.89 on the healthy dataset and 1.24 on the damaged dataset.