时频数据驱动的典型复杂供输机构健康状态软测量方法

复杂供输机构的运行环境恶劣，传感器采集到的振动信号中含有猛烈冲击、噪声等成分，属于典型的非平稳特征信号，难以直接评估供输机构健康状态。针对上述问题，提出一种基于时频数据驱动的供输机构健康状态软测量方法。利用Morlet小波变换得到振动加速度信号时频图作为输入特征，构建基于深度卷积网络的软测量模型。在深度卷积网络中引入Dropout正则化项，减缓过拟合现象，同时定量分析软测量结果不确定性。复杂供输机构台架试验表明：该方法能够有效区分供输机构健康状态，准确率达到90%，在性能退化阶段，能够定量分析机构退化程度，测量误差在7%左右；与基于均方根值、信息熵及时频图的软测量方法对比分析可知，该方法能够有效提高供输机构健康状态识别精度和性能退化程度测量精度，降低测量结果的不确定性。

Soft Measurement Method for Health State of Typical Complex Feeding and Ramming Mechanism Based on Time-frequency Data Drive

The operating environment of complex feeding and ramming mechanisms is harsh, and the vibration signals collected by sensors contain violent impact, noise and other components, which are typical non-stationary characteristic signals, and the health state of feeding and ramming mechanisms is difficultly evaluated. To solve these problems, a soft measurement method based on time-frequency data driving is proposed for measuring the health state of feeding and ramming mechanism. The time-frequency graph of vibration acceleration signal, as an input feature, is obtained by Morlet wavelet transform, and a soft measurement model based on deep convolutional network is established. The Dropout regularization term is introduced into the deep convolutional network to relieve overfitting phenomenon, and the uncertainty of soft measurement results is analyzed quantitatively. The bench test of complex feeding and ramming mechanism shows that the proposed soft measurement method can effectively distinguish the health state of feeding and ramming mechanism with the accuracy of 90%. In the stage of performance degradation, the degradation degree of the mechanism performance can be quantitatively analyzed, and the measured error is about 7%. Compared with other data-driven soft measurement methods for health state, the proposed method can effectively improve the identification accuracy of health state and the measuring accuracy of performance degradation of feeding and ramming mechanisms, and reduce the uncertainty of measureed results.