基于多尺度振动和振声频谱特征自适应提取与选择的磨机负荷参数软测量

Soft sensing modeling of mill load parameter based on adaptive feature selection and extraction of multi-scale vibration and acoustic frequency spectrum

针对磨机筒体振动/振声多尺度频谱与磨机负荷参数间的模糊特性、多源多尺度频谱间的冗余性与互补性,以及现有文献中潜结构选择性集成模型难以模拟运行专家"听音"推理识别磨机负荷参数等问题,提出一种基于多尺度振动/振声频谱特征自适应提取与选择的磨机负荷参数软测量模型.该方法首先进行多尺度频谱获取,然后基于核潜结构映射和互信息进行多尺度振动和振声频谱特征的自适应提取和选择,最后采用同步聚类算法、Madani模糊模型以及基于分支定界和自适应加权融合算法的选择性集成学习策略构建磨机负荷参数软测量模型.通过实验球磨机验证了所构建的软测量模型能够模拟运行专家的模糊推理机制,具有较好的建模精度.

Aiming at the fuzzy type characteristics between multi-frequency spectrums of vibration/acoustic signal and mill load parameters, the redundancy and complementarity in the multi-frequency spectrums, and the difficulty of using latent structural selected model in existing literatures to simulate the operation expert "listen"inference to identify the mill load parameters, a soft sensing model of mill load parameters based on adaptive extraction and selection of multi-scale vibration/acoustic spectrum characteristics is proposed. The method firstly acquires multi-scale spectrum,then extracts and selects the features of multi-scale vibration and acoustic spectrum adaptively using kernel partial least squares(KPLS) and mutual information(MI). Finally, the soft sensing model of mill load parameters is constructed by using online clustering, Madani fuzzy model, banch and bound(BB), adaptation weighted fusion(AWF) and modeling of selective sets. The experiments are carried out in the experimental wet mill. The experimental results show that the proposed model can simulate the expert’s reasoning mechanism and has better modeling accuracy.