MDF热压过程多传感器数据融合模型建立与仿真研究
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摘要
热压是中密度纤维板(MDF)生产过程中的关键工序之一,对产品质量和产量起着决定性作用。热压过程工艺状况复杂多变,受到各种go艺因素和干扰因素的影响,是一个包含多变量的非线性过程,使对于热压过程的板坯内部参数检测、过程控制的研究增加了很多不确定性。针对上述问题,本文以多层压机的MDF热压过程为研究对象,深入研究了多传感器数据融合分层体系、功能模型与融合方法,以提高数据检测精度和决策的可信度。
在深入分析MDF热压工艺过程机理,工艺参数特点及其相互关系的基础上,提出将多传感器数据融合理论应用于热压过程的检测与决策,构建了分层融合体系和功能模型,为热压过程的数据融合方法研究提供了框架结构。研究了数据层、特征层和决策层的融合算法,并对算法进行了仿真。热压试验和仿真结果证明了融合模型及算法的可行性和有效性。
针对观测数据的随机噪声和疏失误差,在数据层融合中,采用基于置信距离的数据.一致性检验方法,确定传感器最优融合数。提出了一种具有在线学习能力的白适应加权最小二乘算法,对板坯内部温度数据进行估计,各传感器的信息权重直接反映了传感器的动态误差情况,克服了由于某个传感器误差偏大对系统估值造成的不良影响,仿真结果表明,总体估值精度明显高于任一传感器的局部估值精度和算术平均算法的精度。
本文运用单因素对比试验和多因素正交试验,计算各因素对于性能指标的影响因子,得到各因素对于性能影响的定性关系。同时建立了工艺因素与性能参数有限样本集,针对小样本预测建模问题,提出了一种基于粒子群优化参数的支持向量机回归算法,建立了热压成品性能预测模型,相对于网络搜索和交叉验证选参方法,数值拟合精度和模型泛化性能得到了较大幅度的提高。
针对热压过程中的不确定性问题,为了定量表示相关模糊概念和数据,有针对性地构建了产品等级评定、热压状态评价和控制决策的模糊识别框架,运用海明距离法构造了证据的基本概率赋值,将模糊集理论和证据理论进行综合应用实现了决策,弥补了人工经验进行决策的不确定性,提高了决策的可信度。
课题将数据融合理论、智能控制理论与MDF热压工艺过程的检测与决策相结合,研究成果为板材成品力学性能指标的预测、工艺参数设定和优化、控制规则决策,提高人造板生产工艺理论的研究水平和实际热压控制系统的智能化程度,实现木材资源可持续利用提供有效的理论支撑和技术保障,具有重要的理论价值和现实意义。
Wood-based panel industry is an important branch of forestry industry which severely influences the effect of alleviating the contradiction between timber supply and demand, and achieving sustainable development strategy of forestry industry in China. Hot-pressing is one of the key procedures in wood-based panel production playing a decisive role in the quality and output of the products. Hot-pressing is a nonlinear, complex and changeable process involving multiple variables, and it is often affected by various technique factors and noises. These features make it difficult and uncertain to realize internal measuring and process monitoring during hot-pressing process of wood based panels. To solve the above problems, based on the medium density fiberboard (MDF) hot-pressing on multi-layer press, the multi-sensor data fusion architecture and methods are studied in this dissertation to improve the measurement precision and credibility of decision, and provide theoretical and technical basis for optimizing the fiberboard hot-pressing process, predicting the properties, and improving process control performance.
Based on the analysis of MDF hot-pressing mechanism, characteristics of technique parameters and their relationships, the dissertation put forward the views that multi-sensor data fusion theory should be applied lo the measurement and decision making of hot pressing process. A hierarchical fusion architecture and function model is established to provied a framework for the study of data fusion methods, data fusion algorithms of data, feature and decision layers are studied, and algorithms simulating are implemented. Hot pressing experiment and simulation results prove the feasibility and effectiveness of the fusion model and algorithm.
Because the random noise and gross error may easily exist in the measurement data, in the data layer fusion, the confidence distance is calculated to check the consistency of data from multiple sensors which is embedded in hot-pressing system, and then the optimized number of sensors can be determined. An adaptive weighted least square algorithm with online learning ablility is presented to estimate parameter of internal temperature of the mat. The weight of each sensor is computed based on its deviation to overcome the influence of large error from one sensor and improve the overall estimation precision. The simulation results demonstrate the high accuracy of the overall estimation compared with local one of any sensor in the system and arithmetic average algorithm.
Single factor comparison experiments and multi-factor orthogonal experiments are carried out in order to compute the influence factor of each technique factor on the property index and thus the qualitative relationship between technique factor and property index is acquired. The finite sample set with mechanical properties as output variables and technique factors as input are acquired by the experiments. Aiming at the prediction modeling with small sample set, SVM regression algorithm with PSO optimized parameters is presented in this dissertation, and a model for properties prediction of hot pressing product is established. The algorithm has higher precision and better generalization performance of the numerical fitting than traditional methods.
To solve the uncertainty problem of hot-pressing process and to quantitatively represent fuzzy concepts and data, the fuzzy recognition framework of product rating, hot-pressing condition evaluation and control decision are set. The basic probability assignment is obtained by typical samples and Hamming distance method. The decision making is realized by integrated application of fuzzy sets theory and evidence theory to eliminate the uncertainty of decision made by artificial experience and achieve higher reliability and credibility of the decision.
The dissertation combined multi-sensor data fusion theory and intelligent control theory with measurement and decision making process of MDF hot-pressing, and provided effective theoretical and technical support for the board mechanical properties prediction, process parameters setting and optimization. and decision making of control rules. The research has the important reality significance and theoritical value to improve the study of hot-pressing process, enhance the control system intellectual degree and realize the sustainable utilization of wood resources.
在深入分析MDF热压工艺过程机理,工艺参数特点及其相互关系的基础上,提出将多传感器数据融合理论应用于热压过程的检测与决策,构建了分层融合体系和功能模型,为热压过程的数据融合方法研究提供了框架结构。研究了数据层、特征层和决策层的融合算法,并对算法进行了仿真。热压试验和仿真结果证明了融合模型及算法的可行性和有效性。
针对观测数据的随机噪声和疏失误差,在数据层融合中,采用基于置信距离的数据.一致性检验方法,确定传感器最优融合数。提出了一种具有在线学习能力的白适应加权最小二乘算法,对板坯内部温度数据进行估计,各传感器的信息权重直接反映了传感器的动态误差情况,克服了由于某个传感器误差偏大对系统估值造成的不良影响,仿真结果表明,总体估值精度明显高于任一传感器的局部估值精度和算术平均算法的精度。
本文运用单因素对比试验和多因素正交试验,计算各因素对于性能指标的影响因子,得到各因素对于性能影响的定性关系。同时建立了工艺因素与性能参数有限样本集,针对小样本预测建模问题,提出了一种基于粒子群优化参数的支持向量机回归算法,建立了热压成品性能预测模型,相对于网络搜索和交叉验证选参方法,数值拟合精度和模型泛化性能得到了较大幅度的提高。
针对热压过程中的不确定性问题,为了定量表示相关模糊概念和数据,有针对性地构建了产品等级评定、热压状态评价和控制决策的模糊识别框架,运用海明距离法构造了证据的基本概率赋值,将模糊集理论和证据理论进行综合应用实现了决策,弥补了人工经验进行决策的不确定性,提高了决策的可信度。
课题将数据融合理论、智能控制理论与MDF热压工艺过程的检测与决策相结合,研究成果为板材成品力学性能指标的预测、工艺参数设定和优化、控制规则决策,提高人造板生产工艺理论的研究水平和实际热压控制系统的智能化程度,实现木材资源可持续利用提供有效的理论支撑和技术保障,具有重要的理论价值和现实意义。
Wood-based panel industry is an important branch of forestry industry which severely influences the effect of alleviating the contradiction between timber supply and demand, and achieving sustainable development strategy of forestry industry in China. Hot-pressing is one of the key procedures in wood-based panel production playing a decisive role in the quality and output of the products. Hot-pressing is a nonlinear, complex and changeable process involving multiple variables, and it is often affected by various technique factors and noises. These features make it difficult and uncertain to realize internal measuring and process monitoring during hot-pressing process of wood based panels. To solve the above problems, based on the medium density fiberboard (MDF) hot-pressing on multi-layer press, the multi-sensor data fusion architecture and methods are studied in this dissertation to improve the measurement precision and credibility of decision, and provide theoretical and technical basis for optimizing the fiberboard hot-pressing process, predicting the properties, and improving process control performance.
Based on the analysis of MDF hot-pressing mechanism, characteristics of technique parameters and their relationships, the dissertation put forward the views that multi-sensor data fusion theory should be applied lo the measurement and decision making of hot pressing process. A hierarchical fusion architecture and function model is established to provied a framework for the study of data fusion methods, data fusion algorithms of data, feature and decision layers are studied, and algorithms simulating are implemented. Hot pressing experiment and simulation results prove the feasibility and effectiveness of the fusion model and algorithm.
Because the random noise and gross error may easily exist in the measurement data, in the data layer fusion, the confidence distance is calculated to check the consistency of data from multiple sensors which is embedded in hot-pressing system, and then the optimized number of sensors can be determined. An adaptive weighted least square algorithm with online learning ablility is presented to estimate parameter of internal temperature of the mat. The weight of each sensor is computed based on its deviation to overcome the influence of large error from one sensor and improve the overall estimation precision. The simulation results demonstrate the high accuracy of the overall estimation compared with local one of any sensor in the system and arithmetic average algorithm.
Single factor comparison experiments and multi-factor orthogonal experiments are carried out in order to compute the influence factor of each technique factor on the property index and thus the qualitative relationship between technique factor and property index is acquired. The finite sample set with mechanical properties as output variables and technique factors as input are acquired by the experiments. Aiming at the prediction modeling with small sample set, SVM regression algorithm with PSO optimized parameters is presented in this dissertation, and a model for properties prediction of hot pressing product is established. The algorithm has higher precision and better generalization performance of the numerical fitting than traditional methods.
To solve the uncertainty problem of hot-pressing process and to quantitatively represent fuzzy concepts and data, the fuzzy recognition framework of product rating, hot-pressing condition evaluation and control decision are set. The basic probability assignment is obtained by typical samples and Hamming distance method. The decision making is realized by integrated application of fuzzy sets theory and evidence theory to eliminate the uncertainty of decision made by artificial experience and achieve higher reliability and credibility of the decision.
The dissertation combined multi-sensor data fusion theory and intelligent control theory with measurement and decision making process of MDF hot-pressing, and provided effective theoretical and technical support for the board mechanical properties prediction, process parameters setting and optimization. and decision making of control rules. The research has the important reality significance and theoritical value to improve the study of hot-pressing process, enhance the control system intellectual degree and realize the sustainable utilization of wood resources.
引文
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