Abstract Summary
Recently, with the advancement of computers and computational resources, the use of deep learning models is becoming a common practice in Structural Health Monitoring. This can facilitate the procedure by extracting proper features from data as well as detecting patterns in the data. However, there are several challenges in this effort among which two cases are very crucial: data shortage and long training time. Researchers address the former by different approaches, such as fusion at the data or decision level, incorporating the physics of the problem either by defining a hybrid cost function or by generating synthetic data from a finite element model. But these approaches may increase the overall training time. With the advancement of quantum computers and the associated algorithms, different fields have started to exploit inherent quantum mechanical features, e.g. superposition and entanglement, and investigate if these effects can be beneficial for them. Among different quantum applications, quantum machine learning (QML) methods are known for their faster convergence with smaller datasets than those of classic ML. This is one of the main reasons for their recent widespread applications. However, since limited numbers of qubits can remain stable at the same time, implementing QMLs in different fields is not feasible yet. To overcome this issue one could employ Hybrid Quantum-Classic Machine Learning approaches. In this study, a preliminary investigation on the application of Hybrid Quantum-Classical ML to structural health monitoring has been done. In this regard, a deep neural network (DNN) model with a quantum layer is selected as the hybrid model and the training dataset is obtained from ultrasonic inspection of a wind turbine blade. The effect of the quantum layer on the performance of the DNN for damage detection is then investigated by performing convergence and accuracy analyses of the model with and without the quantum layer. The promising results indicate the benefit of employing hybrid models in this field.
