Peng Huaqing, Li Heng, Zhang Yu, Wang Siyuan, Gu Kai, Ren Mifeng
State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Co., Ltd., Shenzhen 518172, China.
College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China.
Entropy (Basel). 2022 Jan 21;24(2):164. doi: 10.3390/e24020164.
In order to reduce maintenance costs and avoid safety accidents, it is of great significance to carry out fault prediction to reasonably arrange maintenance plans for rotating mechanical equipment. At present, the relevant research mainly focuses on fault diagnosis and remaining useful life (RUL) predictions, which cannot provide information on the specific health condition and fault types of rotating mechanical equipment in advance. In this paper, a novel three-stage fault prediction method is presented to realize the identification of the degradation period and the type of failure simultaneously. Firstly, based on the vibration signals from multiple sensors, a convolutional neural network (CNN) and long short-term memory (LSTM) network are combined to extract the spatiotemporal features of the degradation period and fault type by means of the cross-entropy loss function. Then, to predict the degradation trend and the type of failure, the attention-bidirectional (Bi)-LSTM network is used as the regression model to predict the future trend of features. Furthermore, the predicted features are given to the support vector classification (SVC) model to identify the specific degradation period and fault type, which can eventually realize a comprehensive fault prediction. Finally, the NSF I/UCR Center for Intelligent Maintenance Systems (IMS) dataset is used to verify the feasibility and efficiency of the proposed fault prediction method.
为了降低维护成本并避免安全事故，对旋转机械设备进行故障预测以合理安排维护计划具有重要意义。目前，相关研究主要集中在故障诊断和剩余使用寿命（RUL）预测上，无法提前提供旋转机械设备具体健康状况和故障类型的信息。本文提出了一种新颖的三阶段故障预测方法，以同时实现退化期和故障类型的识别。首先，基于多个传感器的振动信号，将卷积神经网络（CNN）和长短期记忆（LSTM）网络相结合，通过交叉熵损失函数提取退化期和故障类型的时空特征。然后，为了预测退化趋势和故障类型，使用注意力双向（Bi）-LSTM网络作为回归模型来预测特征的未来趋势。此外，将预测特征输入支持向量分类（SVC）模型以识别具体的退化期和故障类型，最终实现全面的故障预测。最后，使用美国国家科学基金会（NSF）I/UCR智能维护系统中心（IMS）数据集验证了所提出故障预测方法的可行性和有效性。
