基于连续型HMM和PSO-SVM的滚动轴承剩余寿命预测

Residual life prediction of rolling bearings based on continuous hidden Markov model and PSO-SVM

为了准确识别轴承当前所处的退化状态,并进一步精确有效地预测其剩余寿命,提出一种基于连续型隐马尔可夫模型(CHMM)与PSO-SVM相结合的预测方法。首先,提取轴承振动全寿命周期信号的时域、频域、时频域的特征,并构建特征空间;然后,利用CHMM将轴承全寿命周期划分若干个退化阶段,并通过选取不同阶段的特征训练样本,采用PSO-SVM进行预测模型的训练,分别得到不同阶段的剩余寿命预测模型;最后,运用滚动轴承全寿命数据对所提方法进行测试,并与全寿命周期数据CHMM分区段后的SVM模型和未分区段的PSO-SVM模型的预测方法作对比。实验结果表明该方法能有效提高预测精度,具有一定的实用性。

In order to accurately identify the degradation state of the bearing and predict its residual life accurately and effectively, a prediction method based on Continuous Hidden Markov Model (CHMM) and Particle Swarm Optimization and Support Vecotr Machine (PSO-SVM) was proposed. Firstly, characteristics of the time domain, frequency domain and time-frequency domain of the bearing life cycle signal were extracted, and the feature space was constructed. Then, CHMM was used to divide the bearing life cycle into several degradation stages, and with the training samples of different stages selected, PSO-SVM was used to train and obtain the residual life prediction models of different stages. Finally, experiments were conducted on the full-life cycle data of rolling bearings with the comparison with pretection methods of segmented SVM model with full-life cycle data CHMM and un-segmented PSO-SVM model. The experimental results show that the proposed method can effectively improve the prediction accuracy and has certain practicability.