基于主元分析的冷水机组传感器故障检测效率研究
发布时间：2018-11-25 07:53
【摘要】：传感器故障不仅会影响制冷空调系统的运行状况，也会导致运行能耗的增加。传感器的故障检测、诊断及重构研究是制冷空调领域与自动控制领域的一个交叉研究方向，近年来逐渐受到关注和重视。冷水机组是制冷空调系统的主要供能设备，，也是制冷空调系统运行与耗能的核心设备，冷水机组传感器的故障检测、诊断及重构研究，具有非常重要的理论研究意义和工程应用价值。 主元分析是传感器故障检测、诊断及重构研究中常用的数据分析方法。首先分析整理了以Q统计量为检验标准的基于主元分析的传感器故障检测、诊断及重构策略。结合热平衡原理以及冷水机组运行的控制逻辑，分析和筛选了冷水机组中的常用8个传感器——冷冻水侧供水温度、回水温度、流量，冷却水侧供水温度、回水温度、流量，机组功率以及制冷剂流量调节装置反馈信号组成了主元分析的耦合模型，并分析了不同传感器不同故障程度下的故障检测效率特点。然后采用了实测数据和模拟数据数据进行分析和验证工作。结果表明，不同传感器在不同故障条件下，检测效率差异很大。基于主元分析的传感器故障检测方法在小偏差故障条件下的故障检测效率较低，而且部分传感器的整体故障检测效率偏低。 针对传感器故障检测效率有待进一步提高的问题，从训练矩阵优化、测量数据优化、检验标准优化三方面分析了一系列改善和提高基于主元分析的水冷冷水机组传感器故障检测效率的方法。 在训练矩阵优化方面，依据距离度量的概念，建立了两种训练矩阵优化的方法。一种是以标准化原始数据欧氏距离作为异常数据的判断标准，剔除欧氏距离z得分大于2的异常数据，建立了结合基于距离度量异常值剔除的主元分析故障检测策略；另一种以Q统计量阈值Qα作为异常数据的判断标准，采用严格的自适应异常数据循环剔除方法，建立了自适应主元分析故障检测策略。两种方法的主要目的均是通过剔除原始数据中偏离聚集中心的数据，减少异常数据对主元分析正交投影空间的影响。 在测量数据优化方面，采用小波变换方法优化原始训练数据和后续被测数据，去除数据中的噪声。由于小波变换具有可变的层次性，因此进一步对比分析了不同小波分解层次对检测效率的影响。当分解层次越多时，检测效率提高越明显。 在检验标准优化方面，通过多统计量的交叉检验提高故障检测效率。对比分析了Q统计量、T~2统计量和HawkinsT~2_H统计量对于不同传感器不同故障的检测效率。通过主元空间统计量——T~2统计量和残差空间统计量——Q统计量及Hawkins T~2_H统计量的交叉检验，能明显提高在小偏差条件下的整体故障检测效率。为了进一步提高对故障的及时检测，以训练矩阵Q统计量的均值作为预期均值，采用Q统计量的累积和控制图进行在线检测及其效率分析，利用误差的时间累积性提高对微小偏差故障的检测效率。 结果表明，上述方法均能改善和提高冷水机组传感器的故障检测效率，从而促进传感器故障诊断及数据重构研究的敏感性。
[Abstract]:The sensor failure will not only affect the operating conditions of the refrigeration and air conditioning system, but also result in an increase in operating energy consumption. The fault detection, diagnosis and reconstruction of the sensor is a cross-research direction in the field of refrigeration and air-conditioning and automatic control, and has been paid more attention and attention in recent years. Chiller is the main energy-supply equipment of the refrigeration and air-conditioning system. It is also the core equipment for the operation and energy consumption of the refrigeration and air-conditioning system. The fault detection, diagnosis and reconstruction of the water chilling unit sensor has very important theoretical research significance and engineering application value. The main element analysis is the data analysis commonly used in the research of sensor fault detection, diagnosis and reconstruction Methods: First, the fault detection, diagnosis and reconstruction of the sensor based on the primary element analysis of Q statistics is analyzed. the water supply temperature, the water return temperature, the flow rate, the water supply temperature of the cooling water side and the water return temperature are analyzed and screened in combination with the heat balance principle and the control logic of the operation of the water chilling unit, The coupling model of the main element analysis is composed of the flow rate, the unit power and the feedback signal of the refrigerant flow regulating device, and the failure detection efficiency of different sensors under different fault conditions is analyzed. Characteristics. The measured data and the analog data data are then used for analysis and verification. The results show that the detection efficiency of different sensors is different under different fault conditions. The sensor fault detection method based on the primary element analysis is low in fault detection efficiency under the condition of small deviation fault, and the whole fault detection efficiency of the partial sensor In view of the problem that the sensor fault detection efficiency is to be further improved, the fault detection of the water-cooled water chilling unit based on the main element analysis is analyzed from the aspects of the training matrix optimization, the measurement data optimization and the test standard optimization. The method of efficiency. In the optimization of training matrix, two kinds of training are set up according to the concept of distance measure. The invention relates to a method for optimizing the training matrix. The method comprises the following steps of: taking the Euclidean distance of the standardized raw data as the judgment standard of the abnormal data, and removing the abnormal data with the Euclidean distance z score of more than 2, and establishing a main element combined with the elimination of the abnormal value based on the distance metric; The fault detection strategy is analyzed, and the self-adaptive main element is established by using the strict self-adaptive abnormal data cyclic elimination method based on the Q statistic threshold Q value as the judgment standard of the abnormal data. The main purpose of the two methods is to reduce the abnormal data to the main element by eliminating the data from the collection center in the original data. The influence of the cross-projection space. In the aspect of data optimization, the method of wavelet transform is used to optimize the original training data and the follow-up. The data is used to remove the noise in the data. The wavelet transform has the variable hierarchy, so it is further compared and analyzed the different wavelength division. The effect of the solution level on the detection efficiency. The more the decomposition level The more the detection efficiency is, the more statistical it is to test the standard optimization. The efficiency of fault detection is improved by cross-checking of quantity. The statistics of Q statistics, T ~ 2 statistic and HawksT ~ 2 _ H statistic are compared and analyzed. The detection efficiency of different sensors of different sensors can be obviously improved by cross-checking the statistics of the statistics of the main element space _ T-2 and the statistic quantity of the residual space _ Q and the statistic quantity of Hawkins T-2 _ H. in order to further improve the timely detection of the fault, the average value of the statistical quantity of the training matrix Q is used as the expected mean value, the accumulation and control charts of the Q statistics are adopted to carry out on-line detection and the efficiency analysis, and the time accumulative property of the error is utilized. The results show that the method can improve and improve the fault detection efficiency of the water chilling unit sensor, so as to promote the transmission
【学位授予单位】：华中科技大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：TU831.4
本文编号：2355363
[Abstract]:The sensor failure will not only affect the operating conditions of the refrigeration and air conditioning system, but also result in an increase in operating energy consumption. The fault detection, diagnosis and reconstruction of the sensor is a cross-research direction in the field of refrigeration and air-conditioning and automatic control, and has been paid more attention and attention in recent years. Chiller is the main energy-supply equipment of the refrigeration and air-conditioning system. It is also the core equipment for the operation and energy consumption of the refrigeration and air-conditioning system. The fault detection, diagnosis and reconstruction of the water chilling unit sensor has very important theoretical research significance and engineering application value. The main element analysis is the data analysis commonly used in the research of sensor fault detection, diagnosis and reconstruction Methods: First, the fault detection, diagnosis and reconstruction of the sensor based on the primary element analysis of Q statistics is analyzed. the water supply temperature, the water return temperature, the flow rate, the water supply temperature of the cooling water side and the water return temperature are analyzed and screened in combination with the heat balance principle and the control logic of the operation of the water chilling unit, The coupling model of the main element analysis is composed of the flow rate, the unit power and the feedback signal of the refrigerant flow regulating device, and the failure detection efficiency of different sensors under different fault conditions is analyzed. Characteristics. The measured data and the analog data data are then used for analysis and verification. The results show that the detection efficiency of different sensors is different under different fault conditions. The sensor fault detection method based on the primary element analysis is low in fault detection efficiency under the condition of small deviation fault, and the whole fault detection efficiency of the partial sensor In view of the problem that the sensor fault detection efficiency is to be further improved, the fault detection of the water-cooled water chilling unit based on the main element analysis is analyzed from the aspects of the training matrix optimization, the measurement data optimization and the test standard optimization. The method of efficiency. In the optimization of training matrix, two kinds of training are set up according to the concept of distance measure. The invention relates to a method for optimizing the training matrix. The method comprises the following steps of: taking the Euclidean distance of the standardized raw data as the judgment standard of the abnormal data, and removing the abnormal data with the Euclidean distance z score of more than 2, and establishing a main element combined with the elimination of the abnormal value based on the distance metric; The fault detection strategy is analyzed, and the self-adaptive main element is established by using the strict self-adaptive abnormal data cyclic elimination method based on the Q statistic threshold Q value as the judgment standard of the abnormal data. The main purpose of the two methods is to reduce the abnormal data to the main element by eliminating the data from the collection center in the original data. The influence of the cross-projection space. In the aspect of data optimization, the method of wavelet transform is used to optimize the original training data and the follow-up. The data is used to remove the noise in the data. The wavelet transform has the variable hierarchy, so it is further compared and analyzed the different wavelength division. The effect of the solution level on the detection efficiency. The more the decomposition level The more the detection efficiency is, the more statistical it is to test the standard optimization. The efficiency of fault detection is improved by cross-checking of quantity. The statistics of Q statistics, T ~ 2 statistic and HawksT ~ 2 _ H statistic are compared and analyzed. The detection efficiency of different sensors of different sensors can be obviously improved by cross-checking the statistics of the statistics of the main element space _ T-2 and the statistic quantity of the residual space _ Q and the statistic quantity of Hawkins T-2 _ H. in order to further improve the timely detection of the fault, the average value of the statistical quantity of the training matrix Q is used as the expected mean value, the accumulation and control charts of the Q statistics are adopted to carry out on-line detection and the efficiency analysis, and the time accumulative property of the error is utilized. The results show that the method can improve and improve the fault detection efficiency of the water chilling unit sensor, so as to promote the transmission
【学位授予单位】：华中科技大学
【学位级别】：博士
【学位授予年份】：2013
【分类号】：TU831.4
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