基于虚拟样本生成技术的多组分机械信号建模

采用具有多组分、非平稳、非线性等特性的机械振动/振声信号构建数据驱动软测量模型，是目前工业界测量高能耗旋转机械设备内部难以检测过程参数的常用手段.针对机械信号产生机理的复杂性导致模型解释性弱，以及工业过程连续不间断运行和机械设备旋转封闭的特殊性导致获取完备训练样本的经济性差和周期性长等问题，本文提出一种基于虚拟样本生成（Virtual sample generation，VSG）技术的多组分机械信号建模方法.首先，将机械信号自适应分解为具有不同时间尺度的平稳子信号并变换为多尺度谱数据；接着，采用适合于小样本高维数据建模的改进选择性集成核偏最小二乘（Selective ensemble kernel partial least squares，SENKPLS）算法构建面向真实训练样本的基于可行性的规划（Feasibility-based programming，FBP）模型，提出一种综合先验知识和FBP模型等手段面向高维谱数据的VSG技术，用以弥补真实训练样本的短缺问题；然后，基于互信息（Mutual information，MI）对由真实和虚拟训练样本组成的混合建模数据进行自适应特征选择；最后，基于约简的混合训练样本采用SENKPLS构建软测量模型.以近红外谱数据和磨矿过程实验球磨机的筒体振动/振声信号验证所提VSG技术和面向多组分机械信号建模方法的合理性和有效性.

Modeling Multiple Components Mechanical Signals by Means of Virtual Sample Generation Technique

Mechanical vibration & acoustic signals with characteristics of multiple components, nonstationarity and nonlinearity are always used to construct the data-driven soft sensor model of industrial processes. It is one of the main approaches to measure the difficulty-to-measure process parameters inside those high energy consumption mechanical devices. Duo to the complexity of the production mechanism of these mechanical signals, most of these soft sensor models are difficult to be explained. Moreover, the characteristics of the industrial process' continuous running and the mechanical equipment' operation modes lead to the difficulty of high economic cost and long period waiting to obtain sufficient training samples. To solve these problems, a new multi-component mechanical signal modeling method based on virtual sample generation (VSG) technology is proposed. Firstly, the mechanical signals are processed into a set of sub-signals with different time scales by using adaptive multi-component signal decomposition technique; then these sub-signals are transferred to high dimensional multi-scale spectral data. Secondly, an improved selective ensemble kernel partial least squares (SENKPLS) algorithm that suits to model small sample high dimensional data is used to construct a feasibility-based programming (FBP) model with the true training samples; then prior knowledge, FBP models and information entropy are integrated to produce virtual training samples. Thirdly, mutual information (MI) method is used to select the spectral features of the new mixing training samples based on the true and virtual ones. Finally, a soft sensor model is built by using these reduced mixing spectral data. Near-infra spectra data and mechanical vibration and acoustic singals of a laboratory-scale ball mill in grinding process validate the reasonability and effectiveness of the proposed VSG techniques and multi-component mechanical signals-based modeling approach.