软测量模型自适应校正与高温场软测量方法研究
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摘要
为了保证生产的安全进行,提高产品质量和产量,实现节能降耗,需要对工业生产过程中的关键参数进行测量。软测量技术利用能够测量或能够精确测量的过程参数作为辅助变量,基于辅助变量和主导变量的数学模型,实现传统仪表无法测量的过程参数的在线预测,因而成为检测领域研究的热点。
但是,目前软测量技术研究缺乏系统规范的理论框架；而在实际应用中,软测量模型性能随着过程特性的变化而逐渐恶化,因此需要通过在线校正来维护模型的测量性能,但传统的大多数软测量模型校正方法因存在“盲目校正”问题,其校正效果和实时性有待提高。
温度是高温生产中重要的过程参数,但传统的接触式测温方法存在响应时间长、无法在线连续测量、不能给出对象温度场等缺点。基于彩色CCD图像传感器的高温场非接触测温方法虽能实现高温场的在线测量,但作为其测温理论基础的大多是基于理想化假设而推导出的比色测温公式,实际应用中,因理想化假设不能满足,导致其测温误差较大,测量精度不足。
论文针对上述问题进行研究,构建了系统规范的软测量技术理论框架,该框架由软测量技术定义、实现步骤、实施方法(辅助变量选择、数据预处理、建模、模型维护)三部分构成；提出了一种目的性和针对性较强的软测量模型性能监测、评价及其自适应校正方法；开发了基于彩色CCD图像传感器的高温场软测量系统。主要研究内容及成果如下：
(1)提出了一种新型的软测量模型性能评价指标。与传统的基于预测均方误差或均方根误差的指标相比,该指标具有两个优点：①采用加权均值滤波减小离线测量噪声对模型性能评价的影响,避免由于离线噪声导致校正功能的误触发；②基于模型的设计性能对其当前性能进行客观地评价,使评价指标具有更为明确的物理意义。利用这一指标可对软测量模型性能进行有效的监测,从而提高了模型校正的目的性和针对性,解决了“盲目校正”问题。
(2)提出了一种基于频谱特征分析的过程特性变化类型判别方法。首先采集生产过程中稳态、渐变和突变工况下软测量模型性能评价指标数据,然后使用不同工况下评价指标曲线的频谱特征训练过程特性变化类型分类器,并利用该分类器判别过程特性变化类型。
(3)提出了一种基于离线测量数据和工况判别的软测量模型自适应校正方法。当在线监测的模型性能评价指标超过预定统计限时,才触发软测量模型校正功能；然后利用过程特性变化类型分类器判别工况变化类型并采取相应的模型校正措施：若过程特性渐变,则利用新的离线测量值对模型进行递推校正；若过程特性突变,则在历史数据中选取与当前测量点相似的样本构建局部建模样本集,进行模型重构。通过对不同类型的过程特性变化类型采用不同的模型校正方法,在保证测量实时性的同时,有效地解决了传统校正方法的“盲目校正”和校正缺乏针对性的问题,校正频率明显低于传统校正方法,而校正效果则优于传统方法,显著提高了软测量系统对过程特性变化的适应能力。
(4)提出了一种新型的适用范围广泛的比色测温公式,建立了带有烟雾干扰补偿和模型自校正功能的基于彩色CCD的高温场软测量模型。通过对比色测温原理和CCD工作原理进行深入的分析和严谨的数学推导,提出了不含任何理想化假设的改进的CCD比色测温公式,据此实现的比色测温方法的测量精度、可靠性及对不同被测对象的适应能力都优于传统的比色测温方法；针对工业现场普遍存在的烟雾干扰,构建了一种能对测温结果进行补偿校正的烟雾干扰补偿器,提高了测温结果的精度和可靠性；通过对高温场软测量模型进行在线校正,提高了其对不同测量对象的适应能力。
(5)提出了一种基于干扰源辐射体颜色信息的高温辐射体图像识别方法。能够有效克服现场各种光辐射干扰的影响,准确识别待测高温目标。
(6)提出了一种基于两级模板的CCD图像噪声滤波方法。利用一级模板识别图像边缘和脉冲噪声,利用二级模板滤除高斯噪声。该方法能够在尽可能保留图像细节、避免图像边缘模糊的同时滤除CCD图像噪声。
(7)开发了基于彩色CCD的高温场软测量系统。该系统能够实时在线测量高温辐射体的表面温度场,并提供详细的温度信息供操作者进行分析决策。实验结果表明,所开发的高温场软测量系统测温范围广,测温精度高,响应时间短,抗干扰性强,可靠性好,对不同被测对象的适应性强,能够满足工业生产的应用要求,性价比优势突出,具有较强的实用性和推广价值。
Industrial processing plants are generally instrumented with a large number of traditional hardware sensors with the primary purpose to collect and deliver data for process modeling, monitoring and control. Although these hardware sensors have been widely used in industry, they have many disadvantages such as time-consuming maintenance, aged deterioration, insufficient accuracy, slow dynamics, large noises and low reproducibility. Meanwhile, some critical variables, such as biomass concentration in bioprocess, cannot be measured online by hardware sensors. As a result, it is crucial to develop soft sensors to infer some critical variables of industrial process.
Unfortunately, development of soft sensors is lack of holistic direction by the framework of soft sensor techniques. Furthermore, the estimation performance of soft sensors is deteriorated with the changes of the process characteristics, especially for data-driven modeling approaches. For the time-varying processes, soft sensors need to be updated online to maintain their performance. However, the traditional correction methods update soft sensors whenever new measurements arrive. This blind correction would lead to drastic fluctuations of estimations and large computational load.
Temperature is one of the important process parameters which need to be measured and controlled in high-temperature industry. The widely applied technique for temperature measurements is contact thermography technology. But it has slow response and can't online real time measure the temperature distribution for measurement targets. The temperature contactless measurement technology based on colored CCD enables the online real time measurement of temperature fields with a good accuracy. However, the traditional measurement technologies are based on the formulas deduced in terms of idealization and hypothesis which have low accuracy and precision, and are very sensitive to the influence of the locale interference.
In the paper, based on the discussion and review of algorithms for soft sensing, the framework of soft sensor techniques has been summarized. The framework proposes the definition, implementation procedure of the soft sensors and the popular algorithms for soft sensing. Then, an approach for accessing, monitoring and maintaining the performance of soft sensors is proposed. Finally, the high-temperature field soft sensing technique based on colored CCD has been developed. The major research contents and results are as follows:
(1) A novel performance index of soft sensors is designed to evaluate the performance of soft sensors. Comparing with the traditional performance index based on the prediction errors, the proposed performance index has two main advantages:(i) the influence of offline measurement noises is decreased by weighted mean filter, which could avoid the offline measurement noises to trigger the adaptation mechanism.(ii) the index objectively evaluates the current performance of soft sensors based on their design performance with more clear physical significance. The statistic confidence limit of the performance index is determined by the index series under the normal operation condition. If the index is outside the predefined limit, the soft sensor adaptation mechanism would be triggered. The method can avoid the soft sensor being updated blindly by the traditional soft sensor adaptation method.
(2) For diagnosing the types of the process characteristics changes which cause the deterioration of soft sensors, a detection method of the type of process characteristics change is proposed. At first, a series of the performance indexes under the normal operation condition, the gradual change condition and the abrupt change condition are collected, respectively. Then, the amplitude of the Discrete Fourier transforms (DFT) of the series are used as feature vectors to train an operation condition classifier. The state classifier is used to judge the type of process characteristics changes according to the amplitude of the DFT of the indexes series.
(3) An adaptive correction of soft sensors using offline measurements and based on the operation condition classifier is proposed. When the adaptation mechanism is triggered, the current operation condition is diagnosed by the classifier. When the process characteristics gradually change, the soft-sensor would be updated recursively. When process characteristics have an abrupt change, the soft sensor would be reconstructed from past data in a neighbor around the query point. The adaptive correction uses the different adaptation methods to the different types of operation condition, which can cope with the changes in process characteristics and achieve high estimation performance with high instantaneity.
(4) A new type of two-color thermometry formula is proposed, which contains the coefficients of emissivity change and CCD response bandwidth. Based on the formula, a high-temperature field soft sensor using colored CCD with smog disturbance compensation and adaptive correction mechanism is proposed. The proposed two-color thermometry formula has not any idealization hypothesis, which is deduced by the mechanism of the CCD imaging and radiation temperature measurement theory. The two-color thermometry based on the formula has advantages such as higher precision, reliability and strong adaptability compared to the traditional methods. Furthermore, the measured results interfered by the smog are corrected by a radiant energy attenuation compensator. Finally, the soft sensor would be online updated using the measurement by other thermodetector, which improves the adaptability to different measured targets.
(5) In order to decrease the influence of industrial interferences, a high-temperature radiator image recognition method based on the color information of the interference radiator is developed. By segmenting the red and green color images and using the mathematics morphology method, the measured targets can be recognized accurately.
(6) An improved mean filter algorithm based on two templates is developed. The edge of the target image and the impulse noises are recognized in the first level of template. Then, the Gaussian noises are filtered in the second level of template. The method could reduce Gaussian noises and Salt and Pepper noises and keep the major details of the original images and clear target image border.
(7) Based on the research above, a high-temperature-field soft sensing system is developed. The soft sensing system could online detect the surface high-temperature fields of high-temperature radiators and provide temperature information to operators for analysis and decision. The experimental results show that the measurement range of the soft sensing system could cover the dynamic range of temperature in common high-temperature production. It also has advantages such as high precision, short response time and strong anti-interference ability, which meets the need of the actual industry and has high practicability and promotional value.
但是,目前软测量技术研究缺乏系统规范的理论框架；而在实际应用中,软测量模型性能随着过程特性的变化而逐渐恶化,因此需要通过在线校正来维护模型的测量性能,但传统的大多数软测量模型校正方法因存在“盲目校正”问题,其校正效果和实时性有待提高。
温度是高温生产中重要的过程参数,但传统的接触式测温方法存在响应时间长、无法在线连续测量、不能给出对象温度场等缺点。基于彩色CCD图像传感器的高温场非接触测温方法虽能实现高温场的在线测量,但作为其测温理论基础的大多是基于理想化假设而推导出的比色测温公式,实际应用中,因理想化假设不能满足,导致其测温误差较大,测量精度不足。
论文针对上述问题进行研究,构建了系统规范的软测量技术理论框架,该框架由软测量技术定义、实现步骤、实施方法(辅助变量选择、数据预处理、建模、模型维护)三部分构成；提出了一种目的性和针对性较强的软测量模型性能监测、评价及其自适应校正方法；开发了基于彩色CCD图像传感器的高温场软测量系统。主要研究内容及成果如下：
(1)提出了一种新型的软测量模型性能评价指标。与传统的基于预测均方误差或均方根误差的指标相比,该指标具有两个优点：①采用加权均值滤波减小离线测量噪声对模型性能评价的影响,避免由于离线噪声导致校正功能的误触发；②基于模型的设计性能对其当前性能进行客观地评价,使评价指标具有更为明确的物理意义。利用这一指标可对软测量模型性能进行有效的监测,从而提高了模型校正的目的性和针对性,解决了“盲目校正”问题。
(2)提出了一种基于频谱特征分析的过程特性变化类型判别方法。首先采集生产过程中稳态、渐变和突变工况下软测量模型性能评价指标数据,然后使用不同工况下评价指标曲线的频谱特征训练过程特性变化类型分类器,并利用该分类器判别过程特性变化类型。
(3)提出了一种基于离线测量数据和工况判别的软测量模型自适应校正方法。当在线监测的模型性能评价指标超过预定统计限时,才触发软测量模型校正功能；然后利用过程特性变化类型分类器判别工况变化类型并采取相应的模型校正措施：若过程特性渐变,则利用新的离线测量值对模型进行递推校正；若过程特性突变,则在历史数据中选取与当前测量点相似的样本构建局部建模样本集,进行模型重构。通过对不同类型的过程特性变化类型采用不同的模型校正方法,在保证测量实时性的同时,有效地解决了传统校正方法的“盲目校正”和校正缺乏针对性的问题,校正频率明显低于传统校正方法,而校正效果则优于传统方法,显著提高了软测量系统对过程特性变化的适应能力。
(4)提出了一种新型的适用范围广泛的比色测温公式,建立了带有烟雾干扰补偿和模型自校正功能的基于彩色CCD的高温场软测量模型。通过对比色测温原理和CCD工作原理进行深入的分析和严谨的数学推导,提出了不含任何理想化假设的改进的CCD比色测温公式,据此实现的比色测温方法的测量精度、可靠性及对不同被测对象的适应能力都优于传统的比色测温方法；针对工业现场普遍存在的烟雾干扰,构建了一种能对测温结果进行补偿校正的烟雾干扰补偿器,提高了测温结果的精度和可靠性；通过对高温场软测量模型进行在线校正,提高了其对不同测量对象的适应能力。
(5)提出了一种基于干扰源辐射体颜色信息的高温辐射体图像识别方法。能够有效克服现场各种光辐射干扰的影响,准确识别待测高温目标。
(6)提出了一种基于两级模板的CCD图像噪声滤波方法。利用一级模板识别图像边缘和脉冲噪声,利用二级模板滤除高斯噪声。该方法能够在尽可能保留图像细节、避免图像边缘模糊的同时滤除CCD图像噪声。
(7)开发了基于彩色CCD的高温场软测量系统。该系统能够实时在线测量高温辐射体的表面温度场,并提供详细的温度信息供操作者进行分析决策。实验结果表明,所开发的高温场软测量系统测温范围广,测温精度高,响应时间短,抗干扰性强,可靠性好,对不同被测对象的适应性强,能够满足工业生产的应用要求,性价比优势突出,具有较强的实用性和推广价值。
Industrial processing plants are generally instrumented with a large number of traditional hardware sensors with the primary purpose to collect and deliver data for process modeling, monitoring and control. Although these hardware sensors have been widely used in industry, they have many disadvantages such as time-consuming maintenance, aged deterioration, insufficient accuracy, slow dynamics, large noises and low reproducibility. Meanwhile, some critical variables, such as biomass concentration in bioprocess, cannot be measured online by hardware sensors. As a result, it is crucial to develop soft sensors to infer some critical variables of industrial process.
Unfortunately, development of soft sensors is lack of holistic direction by the framework of soft sensor techniques. Furthermore, the estimation performance of soft sensors is deteriorated with the changes of the process characteristics, especially for data-driven modeling approaches. For the time-varying processes, soft sensors need to be updated online to maintain their performance. However, the traditional correction methods update soft sensors whenever new measurements arrive. This blind correction would lead to drastic fluctuations of estimations and large computational load.
Temperature is one of the important process parameters which need to be measured and controlled in high-temperature industry. The widely applied technique for temperature measurements is contact thermography technology. But it has slow response and can't online real time measure the temperature distribution for measurement targets. The temperature contactless measurement technology based on colored CCD enables the online real time measurement of temperature fields with a good accuracy. However, the traditional measurement technologies are based on the formulas deduced in terms of idealization and hypothesis which have low accuracy and precision, and are very sensitive to the influence of the locale interference.
In the paper, based on the discussion and review of algorithms for soft sensing, the framework of soft sensor techniques has been summarized. The framework proposes the definition, implementation procedure of the soft sensors and the popular algorithms for soft sensing. Then, an approach for accessing, monitoring and maintaining the performance of soft sensors is proposed. Finally, the high-temperature field soft sensing technique based on colored CCD has been developed. The major research contents and results are as follows:
(1) A novel performance index of soft sensors is designed to evaluate the performance of soft sensors. Comparing with the traditional performance index based on the prediction errors, the proposed performance index has two main advantages:(i) the influence of offline measurement noises is decreased by weighted mean filter, which could avoid the offline measurement noises to trigger the adaptation mechanism.(ii) the index objectively evaluates the current performance of soft sensors based on their design performance with more clear physical significance. The statistic confidence limit of the performance index is determined by the index series under the normal operation condition. If the index is outside the predefined limit, the soft sensor adaptation mechanism would be triggered. The method can avoid the soft sensor being updated blindly by the traditional soft sensor adaptation method.
(2) For diagnosing the types of the process characteristics changes which cause the deterioration of soft sensors, a detection method of the type of process characteristics change is proposed. At first, a series of the performance indexes under the normal operation condition, the gradual change condition and the abrupt change condition are collected, respectively. Then, the amplitude of the Discrete Fourier transforms (DFT) of the series are used as feature vectors to train an operation condition classifier. The state classifier is used to judge the type of process characteristics changes according to the amplitude of the DFT of the indexes series.
(3) An adaptive correction of soft sensors using offline measurements and based on the operation condition classifier is proposed. When the adaptation mechanism is triggered, the current operation condition is diagnosed by the classifier. When the process characteristics gradually change, the soft-sensor would be updated recursively. When process characteristics have an abrupt change, the soft sensor would be reconstructed from past data in a neighbor around the query point. The adaptive correction uses the different adaptation methods to the different types of operation condition, which can cope with the changes in process characteristics and achieve high estimation performance with high instantaneity.
(4) A new type of two-color thermometry formula is proposed, which contains the coefficients of emissivity change and CCD response bandwidth. Based on the formula, a high-temperature field soft sensor using colored CCD with smog disturbance compensation and adaptive correction mechanism is proposed. The proposed two-color thermometry formula has not any idealization hypothesis, which is deduced by the mechanism of the CCD imaging and radiation temperature measurement theory. The two-color thermometry based on the formula has advantages such as higher precision, reliability and strong adaptability compared to the traditional methods. Furthermore, the measured results interfered by the smog are corrected by a radiant energy attenuation compensator. Finally, the soft sensor would be online updated using the measurement by other thermodetector, which improves the adaptability to different measured targets.
(5) In order to decrease the influence of industrial interferences, a high-temperature radiator image recognition method based on the color information of the interference radiator is developed. By segmenting the red and green color images and using the mathematics morphology method, the measured targets can be recognized accurately.
(6) An improved mean filter algorithm based on two templates is developed. The edge of the target image and the impulse noises are recognized in the first level of template. Then, the Gaussian noises are filtered in the second level of template. The method could reduce Gaussian noises and Salt and Pepper noises and keep the major details of the original images and clear target image border.
(7) Based on the research above, a high-temperature-field soft sensing system is developed. The soft sensing system could online detect the surface high-temperature fields of high-temperature radiators and provide temperature information to operators for analysis and decision. The experimental results show that the measurement range of the soft sensing system could cover the dynamic range of temperature in common high-temperature production. It also has advantages such as high precision, short response time and strong anti-interference ability, which meets the need of the actual industry and has high practicability and promotional value.
引文
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