基于CEEMDAN-GPR的球磨机负荷软测量

为对球磨机软测量方法中的测量结果精度实时估计，同时改善软测量信号分解中的模态混叠问题，本文提出了一种新的基于自适应噪声完备集合经验模态分解、高斯混合模型与高斯过程的球磨机负荷软测量方法，核心思想是使用改进CEEMDAN-GMM方法将球磨机振声和振动时域信号分解为一系列的本征模态函数并分类，由高斯过程回归给出预测值。相较于其他软测量方法，完全集合经验模态分解可以很大程度上避免经验模态分解带来的模态混叠影响，高斯混合模型可以通过设定概率阈值的方法在特征聚类的同时识别异常信号，高斯过程回归不但可以给出基于数据驱动的预测值，还能给出相应的置信区间，并据此向操作人员发出异常预警。实验证明，相较于其他软测量方法，本方法在球磨机负荷参数软测量精度、异常检测等方面均有一定的改进。

A CEEMDAN-GPR Based Ball Mill Load Parameters Soft Sensor Method

To give the real-time prediction of accuracy of the ball mill soft sensor, and to solve the model mixing problem in data decomposition of soft sensor. this paper proposed a new Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Gaussian Mixture Model (GMM) and Gaussian Process Regression (GPR) based ball mill load parameters soft sensor method. The key features of this method are using CEEMDAN-GPR to decompose and classify vibration and acoustical signals time domain signals to a series of intrinsic mode functions (IMFs). GPR is used to provide the predicted values. Comparing to the other soft sensor method, the CEEMDAN-based method is largely avoiding the mode mixing issue coming with the original EMD method. Anomalous signals can be classified while feature clustering by giving a probability threshold to the GMM. The GPR-based predicting method will not only provide the data-driven predict values, but also provide their confidence intervals, and warn the operator if necessary. The experiment result shows that comparing to the other soft sensor method, the proposed method has improvements on mill load predicting accuracy and ability of abnormal warning.