基于多源信息融合与HMM的剩余寿命预测

针对设备剩余使用寿命预测问题, 提出一种基于多源信息融合与隐马尔可夫模型的预测方法。首先, 针对发动机结构复杂、监控数据参数多等问题, 提出一种基于传感器信噪比和主成分分析(principal component analysis, PCA)降维的多源传感器数据融合方法。在此基础上, 利用样本数据训练高斯混合隐马尔可夫模型, 同时为降低模型偏差并避免过拟合风险, 提出一种“定制”策略训练方法, 训练后的模型可用于系统健康状态识别和剩余使用寿命预测。最后, 通过美国国家航空航天局公开的航空发动机仿真数据集对所提方法进行了验证, 并与几种具有代表性且预测精度较高的文献方法进行了比较分析, 验证了方法的有效性。

Remaining useful life prediction based on multi-source information fusion and HMM

Aiming at the problem of equipment remaining useful life prediction, a prediction method based on multi-source information fusion and hidden Markov model is proposed. Firstly, a multi-source sensor data fusion method based on signal-to-noise ratio of sensor and principal component analysis (PCA) dimensionality reduction is proposed to solve the problems of complex engine structure and multiple monitoring data parameters.On this basis, the Gaussian mixture hidden Markov model is trained using the sample data. At the same time, in order to reduce the deviation of model and avoid the risk of over fitting, a "customized" strategy training method is proposed. The trained model can be used for system health status recognition and remaining useful life prediction. Finally, the effectiveness of the proposed method is verified by the aeroengine simulation data set published by National Aeronautics and Space Administration, and compared with several representative literature methods with high prediction accuracy.