Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea.
School of Mechanical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Sensors (Basel). 2023 Mar 15;23(6):3153. doi: 10.3390/s23063153.
Fault diagnosis is important in rotor systems because severe damage can occur during the operation of systems under harsh conditions. The advancements in machine learning and deep learning have led to enhanced performance of classification. Two important elements of fault diagnosis using machine learning are data preprocessing and model structure. Multi-class classification is used to classify faults into different single types, whereas multi-label classification classifies faults into compound types. It is valuable to focus on the capability of detecting compound faults because multiple faults can exist simultaneously. Diagnosis of untrained compound faults is also a merit. In this study, input data were first preprocessed with short-time Fourier transform. Then, a model was built for classification of the state of the system based on multi-output classification. Finally, the proposed model was evaluated based on its performance and robustness for classification of compound faults. This study proposes an effective model based on multi-output classification, which can be trained using only single fault data for the classification of compound faults and confirms the robustness of the model to changes in unbalance.
故障诊断在转子系统中很重要，因为在恶劣条件下运行的系统可能会发生严重损坏。机器学习和深度学习的进步提高了分类的性能。使用机器学习进行故障诊断的两个重要元素是数据预处理和模型结构。多类分类用于将故障分为不同的单一类型，而多标签分类则将故障分为复合类型。关注检测复合故障的能力是很有价值的，因为可能同时存在多个故障。诊断未经训练的复合故障也是一个优点。在这项研究中，输入数据首先经过短时傅里叶变换进行预处理。然后，基于多输出分类建立了一个系统状态分类模型。最后，根据分类复合故障的性能和鲁棒性对所提出的模型进行了评估。本研究提出了一种基于多输出分类的有效模型，该模型仅使用单一故障数据进行复合故障分类，并验证了模型对不平衡变化的鲁棒性。
