Abstract：A new approach for state recognition and remaining useful life (RUL) prediction based on Mixture of Gaussians Hidden Markov Model (MoG-HMM) was presented. State number optimization method was established based on cluster validity measures. One can recognize the state through identifying the MoG-HMM that best fits the observations. Then, the RUL prediction method was presented at the recognition base. Finally, the data of gearbox’s full life cycle test was used to demonstrate the proposed methods. The results showed that the mean accuracy performance was 90.94%.
张星辉 康建设 高存明 曹端超 滕红智. 基于MoG-HMM的齿轮箱状态识别与剩余使用寿命预测研究[J]. , 2013, 32(15): 20-25.
Xinghui Zhang Jianshe Kang Cunming Gao Duanchao Cao Hongzhi Teng. Gearbox state identification and remaining useful life prediction based on MoG-HMM. , 2013, 32(15): 20-25.
2013, Vol. 32 
