Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, 01187 Dresden, Germany.
Institute of Lightweight Engineering and Polymer Technology (ILK), Technische Universität Dresden, 01307 Dresden, Germany.
Sensors (Basel). 2021 Mar 12;21(6):2005. doi: 10.3390/s21062005.
Damage identification of composite structures is a major ongoing challenge for a operational life-cycle due to the complex, gradual damage behaviour of composite materials. Especially for composite rotors in aero-engines and wind-turbines, a cost-intensive maintenance service has to be performed in order to avoid critical failure. A major advantage of composite structures is that they are able to safely operate after damage initiation and under ongoing damage propagation. Therefore, a robust, efficient diagnostic damage identification method would allow monitoring the damage process with intervention occurring only when necessary. This study investigates the structural vibration response of composite rotors by applying machine learning methods and the ability to identify, localise and quantify the present damage. To this end, multiple fully connected neural networks and convolutional neural networks were trained on vibration response spectra from damaged composite rotors with barely visible damage, mostly matrix cracks and local delaminations using dimensionality reduction and data augmentation. A databank containing 720 simulated test cases with different damage states is used as a basis for the generation of multiple data sets. The trained models are tested using k-fold cross validation and they are evaluated based on the sensitivity, specificity and accuracy. Convolutional neural networks perform slightly better providing a performance accuracy of up to 99.3% for the damage localisation and quantification.
复合材料结构的损伤识别是一个持续存在的挑战，因为复合材料具有复杂的、渐进的损伤行为。特别是对于航空发动机和风力涡轮机中的复合材料转子，为了避免临界失效，必须进行昂贵的维护服务。复合材料结构的一个主要优势是，它们在损伤起始后和持续的损伤扩展下能够安全地运行。因此，一种稳健、高效的诊断损伤识别方法将允许在必要时进行监测损伤过程，仅在必要时进行干预。本研究通过应用机器学习方法和识别、定位和量化现有损伤的能力，研究了复合材料转子的结构振动响应。为此，使用降维和数据扩充，对具有几乎不可见的损伤、主要是基体裂纹和局部分层的损伤复合材料转子的振动响应谱，训练了多个全连接神经网络和卷积神经网络。一个包含 720 个具有不同损伤状态的模拟测试案例的数据库被用作生成多个数据集的基础。使用 k 折交叉验证测试训练后的模型，并根据灵敏度、特异性和准确性进行评估。卷积神经网络的性能略好，提供了高达 99.3%的损伤定位和量化的性能准确性。
