多传感器融合和 MHA-LSTM 的电机轴承剩余寿命预测

轴承作为电机的核心部件, 主要起到支撑引导轴、 减小设备摩擦、 连接不同设备等作用, 其剩余寿命预测对系统健康 管理起着十分重要的作用。 针对单一传感器信号通常难以全面描述系统的潜在退化机制, 论文提出一种基于多头注意力机制 和长短时记忆神经网络的电机轴承剩余寿命预测模型。 首先, 基于马氏距离确定轴承性能退化起始点, 将滚动轴承全寿命周 期分为正常阶段与退化阶段; 其次, 使用自编码器自动提取振动信号特征, 并将其与电机电流、 轴承温度融合, 构成多源信息 特征矩阵; 然后基于多头注意力机制和长短时记忆网络模型动态选择相关度较高的特征, 提高寿命预测的准确性。 最后, 采 用实验数据进行验证, 结果表明所提出的模型具有更高的准确性。

Prediction of remaining life of motor bearings using multi-sensor fusion and MHA-LSTM

As a core component of motors, bearings primarily serve functions such as supporting and guiding shafts, reducing friction in equipment, and connecting different components. Predicting the remaining life of bearings is crucial for system health management. However, single sensor signals often fail to comprehensively describe the potential degradation mechanisms of the system. This paper proposes a novel approach for predicting the remaining life of motor bearings based on the multi-head attention mechanism and long short-term memory neural network. Firstly, Mahalanobis distance is used to determine the starting point of bearing performance degradation by dividing the entire life cycle of rolling bearings into normal and degradation phases. Secondly, an Autoencoder is employed to automatically extract vibration signal features, which are subsequently fused with motor current and bearing temperature signal to construct a multi-source information feature matrix. Subsequently, the multi-head attention mechanism and long short-term memory network dynamically select features with high relevance, thereby improving the accuracy of the remaining life prediction. Finally, the model is validated using experimental data, and the results show that the proposed model has higher accuracy.