空调系统故障检测及基于性能优化的在线容错控制
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摘要
空调系统的设备或传感器故障，可能导致系统能耗增大，热舒适性降低，室内空气品质恶化，控制系统失灵，甚至造成某些设备的损坏。及时准确地检测出系统所发生的故障，并获知故障对系统运行性能的劣化程度，从而做出故障修正决策，具有非常重要的现实意义。但是，实际过程中，面临不少亟待解决的问题：如何建立准确的运行或性能参数预测模型、可靠的故障检测方法；故障对系统运行性能的影响程度以及如何评价；故障修正决策方案等等。
针对上述问题，本文主要进行了以下四方面的工作。
第一，建立了基于支持向量回归（support vector regression, SVR）的运行参数预测模型，预测无故障运行时的参考数据和系统运行性能数据。通过预测多种运行工况的运行参数，对模型进行了仿真验证。预测模型的验证结果表明，SVR预测模型输入参数的选取，必须能够正确反映空调系统的运行情况。
其次，综合考虑统计残差和分形维数两种方法的优缺点，开发了两种方法相结合的故障检测法，并对送风温度固定偏差故障和漂移偏差故障进行了故障检测试验。仿真试验的结果表明，统计残差法对较小偏差故障的正确故障检测率较低。对于送风温度±0.2oC、+0.3oC固定偏差故障，以及±0.05oC/h、+0.1oC/h漂移偏差故障，正确故障检测率低于50%。分形维数法能够检测出这些故障，但需要一段时间收集测量信号。将两种方法结合的故障检测方法，采用统计残差法检测出相对大的偏差故障，采用分形维数法检测出相对小的故障；对于统计残差法，可采用较大的判别阈值来比较统计残差，这种组合式故障检测方法能够更加有效地对不同程度的故障情况进行检测。
第三，建立了改进的消去与选择转换法（elimination et choicetranslating reality，ELECTRE）的空调系统运行性能评价模型，引入了绝对阈值和分区间阈值概念来构建伪准则。评价准则中引入最小有效控制率来表征故障对控制系统性能的影响。针对送风温度故障和新风流量故障下的不同运行工况，采用评价模型对它们进行了评价分析。仿真结果表明，ELECTRE评价模型适用于空调系统不同运行工况或方案的优劣排序。
第四，开发了基于运行性能优化的在线容错控制策略，以提高系统运行性能为目标，对故障测量信号进行在线容错修正。针对送风温度、新风流量和新风温度传感器的不同故障情况，通过在线容错控制策略进行了验证分析。仿真结果表明，提出的在线容错控制策略能够在不同运行方案优劣排序的基础上，得到最优运行方案，相应优化系数的倒数，作为故障测量信号的修正系数，实现在线容错控制。对于送风温度+13%故障，从第6次修正开始，故障修正系数维持0.84不变；对于新风流量+39%故障，从第7次修正开始，故障修正系数维持0.66不变；对于新风温度–34%故障，在第一次计算优先满意得分时，就能得到故障修正指令。
总之，本文提出的统计残差法和分形维数相结合的组合式故障检测方法，能够有效地检测出不同程度的故障；开发的改进ELECTRE评价模型能够更加合理地对空调系统不同运行方案进行优劣排序；提出的在线容错控制策略，能够对空调系统传感器故障实现在线修正，并优化系统运行性能。更进一步的研究应考虑数据缺失或同时发生多个故障时，如何进行故障检测以及如何实现在线容错等。
The faults generated in air conditioning equipments or sensors, willresult in increasing the energy consumption, decreasing the thermal comfort,deteriorating the indoor air quality, removing the regulating role from controlsystems, or even damaging some certain equipment. It has very important andpractical significance to detect these faults accurately in time, to diagnose thedamage severity to system performance, and thus to make the correctingdecision. In real systems, however, some problems are inevitable to face andto be solved. The operating or performance parameters should be simulated orpredicted accurately. The fault detection methods should find out thegenerated faults reliably. The effect of faults on system operating performanceshould be evaluated objectively and fairly. And the decision-making schemesshould be established appropriately.
In view of the preceding discussions, this dissertation focuses on fourmain problems.
Firstly, the operating performance prediction model is established bysupport vector regression (SVR) to provide the fault-free operating referenceand system performance parameters. The prediction model is validated byseveral different operating conditions. The prediction results show that theinput parameters to SVR prediction model must be selected appropriately toreflect the operating performance of the air conditioning system.
Then, to balance the strengths and weakness of the statistics residualsand fractal dimension, a hybrid method has been proposed to detect fault. Themethod is validated by fixed bias and drifting bias faults of supply airtemperature. The fault detection results show that, for the lower bias faults,the statistics residual method presents smaller correct fault detection rates. Forthe supply air temperature fixed bias faults±0.2oC,+0.3oC, and drifting biasfaults±0.05oC/h,+0.1oC/h, the correct fault detection rates are lower than50%. The fractal dimension method can detect these relatively small faults, but needs a long period to collect the measured signals. In view of theirstrengths and drawbacks, two methods can be combined to a novel faultdetection tool. The statistics residual method can detect the relatively largebias faults, and the fractal dimension method can detect the small ones. Thestatistics residual method uses larger residual threshold, while the fractaldimension method must employ the suitable dimension threshold. Thecombined method can effectively detect the various degrees of faults.
Furthermore, an improved ELECTRE (Elimination Et ChoiceTranslating Reality) model has been developed to evaluate air conditioningsystem performance. The absolute thresholds and dividing thresholds hasbeen introduced to construct the pseudo-criteria in the improved evaluationmodel. The minimum valid control efficiency has been defined as anevaluation criterion to represent the influence of fault on the performance ofthe control system. The evaluation model is validated by dozens of operatingstates under supply air temperature faults and fresh air flow rate faults,respectively. The ELECTRE evaluation model can be completely suitable tooutrank the system performance under different fault and different operatingconditions.
Fourthly, an online fault-tolerant control strategy based on operatingperformance optimization is developed to correct the faulty measurementsonline. The strategy is validated by supply air temperature faults, fresh airflow rate faults, and fresh air temperature faults. By outranking the differentoperating alternatives, the online fault-tolerant control strategy can achievethe fault correction factor according to the optimal operating scheme. Theonline fault tolerant can thus be implemented to correct the faultymeasurements. For the supply air temperature fault+13%, the total faultcorrection factor keeps at0.84from the sixth correction. For the fresh air flowrate fault+39%, the total fault correction factor maintains at0.66from theseventh correction. For the fresh air temperature fault–34%, the faultcorrection responses are output by the first calculation of the outrankingsatisfaction scores.
Generally, the proposed fault detection method combined the statisticalresiduals with the fractal dimension, can detect the various degrees of faults with more effectiveness. The improved ELECTRE evaluation model canmore outrank dozens of operating alternatives with more reasonability. Thedeveloped online fault tolerant control strategy can correct the sensor faultsgenerated in air conditioning systems, and can optimize the system operatingperformance. Further investigation should focus on how to identify severalfaults and how to complete the online fault tolerant control under the data lossor several faults conditions.
针对上述问题，本文主要进行了以下四方面的工作。
第一，建立了基于支持向量回归（support vector regression, SVR）的运行参数预测模型，预测无故障运行时的参考数据和系统运行性能数据。通过预测多种运行工况的运行参数，对模型进行了仿真验证。预测模型的验证结果表明，SVR预测模型输入参数的选取，必须能够正确反映空调系统的运行情况。
其次，综合考虑统计残差和分形维数两种方法的优缺点，开发了两种方法相结合的故障检测法，并对送风温度固定偏差故障和漂移偏差故障进行了故障检测试验。仿真试验的结果表明，统计残差法对较小偏差故障的正确故障检测率较低。对于送风温度±0.2oC、+0.3oC固定偏差故障，以及±0.05oC/h、+0.1oC/h漂移偏差故障，正确故障检测率低于50%。分形维数法能够检测出这些故障，但需要一段时间收集测量信号。将两种方法结合的故障检测方法，采用统计残差法检测出相对大的偏差故障，采用分形维数法检测出相对小的故障；对于统计残差法，可采用较大的判别阈值来比较统计残差，这种组合式故障检测方法能够更加有效地对不同程度的故障情况进行检测。
第三，建立了改进的消去与选择转换法（elimination et choicetranslating reality，ELECTRE）的空调系统运行性能评价模型，引入了绝对阈值和分区间阈值概念来构建伪准则。评价准则中引入最小有效控制率来表征故障对控制系统性能的影响。针对送风温度故障和新风流量故障下的不同运行工况，采用评价模型对它们进行了评价分析。仿真结果表明，ELECTRE评价模型适用于空调系统不同运行工况或方案的优劣排序。
第四，开发了基于运行性能优化的在线容错控制策略，以提高系统运行性能为目标，对故障测量信号进行在线容错修正。针对送风温度、新风流量和新风温度传感器的不同故障情况，通过在线容错控制策略进行了验证分析。仿真结果表明，提出的在线容错控制策略能够在不同运行方案优劣排序的基础上，得到最优运行方案，相应优化系数的倒数，作为故障测量信号的修正系数，实现在线容错控制。对于送风温度+13%故障，从第6次修正开始，故障修正系数维持0.84不变；对于新风流量+39%故障，从第7次修正开始，故障修正系数维持0.66不变；对于新风温度–34%故障，在第一次计算优先满意得分时，就能得到故障修正指令。
总之，本文提出的统计残差法和分形维数相结合的组合式故障检测方法，能够有效地检测出不同程度的故障；开发的改进ELECTRE评价模型能够更加合理地对空调系统不同运行方案进行优劣排序；提出的在线容错控制策略，能够对空调系统传感器故障实现在线修正，并优化系统运行性能。更进一步的研究应考虑数据缺失或同时发生多个故障时，如何进行故障检测以及如何实现在线容错等。
The faults generated in air conditioning equipments or sensors, willresult in increasing the energy consumption, decreasing the thermal comfort,deteriorating the indoor air quality, removing the regulating role from controlsystems, or even damaging some certain equipment. It has very important andpractical significance to detect these faults accurately in time, to diagnose thedamage severity to system performance, and thus to make the correctingdecision. In real systems, however, some problems are inevitable to face andto be solved. The operating or performance parameters should be simulated orpredicted accurately. The fault detection methods should find out thegenerated faults reliably. The effect of faults on system operating performanceshould be evaluated objectively and fairly. And the decision-making schemesshould be established appropriately.
In view of the preceding discussions, this dissertation focuses on fourmain problems.
Firstly, the operating performance prediction model is established bysupport vector regression (SVR) to provide the fault-free operating referenceand system performance parameters. The prediction model is validated byseveral different operating conditions. The prediction results show that theinput parameters to SVR prediction model must be selected appropriately toreflect the operating performance of the air conditioning system.
Then, to balance the strengths and weakness of the statistics residualsand fractal dimension, a hybrid method has been proposed to detect fault. Themethod is validated by fixed bias and drifting bias faults of supply airtemperature. The fault detection results show that, for the lower bias faults,the statistics residual method presents smaller correct fault detection rates. Forthe supply air temperature fixed bias faults±0.2oC,+0.3oC, and drifting biasfaults±0.05oC/h,+0.1oC/h, the correct fault detection rates are lower than50%. The fractal dimension method can detect these relatively small faults, but needs a long period to collect the measured signals. In view of theirstrengths and drawbacks, two methods can be combined to a novel faultdetection tool. The statistics residual method can detect the relatively largebias faults, and the fractal dimension method can detect the small ones. Thestatistics residual method uses larger residual threshold, while the fractaldimension method must employ the suitable dimension threshold. Thecombined method can effectively detect the various degrees of faults.
Furthermore, an improved ELECTRE (Elimination Et ChoiceTranslating Reality) model has been developed to evaluate air conditioningsystem performance. The absolute thresholds and dividing thresholds hasbeen introduced to construct the pseudo-criteria in the improved evaluationmodel. The minimum valid control efficiency has been defined as anevaluation criterion to represent the influence of fault on the performance ofthe control system. The evaluation model is validated by dozens of operatingstates under supply air temperature faults and fresh air flow rate faults,respectively. The ELECTRE evaluation model can be completely suitable tooutrank the system performance under different fault and different operatingconditions.
Fourthly, an online fault-tolerant control strategy based on operatingperformance optimization is developed to correct the faulty measurementsonline. The strategy is validated by supply air temperature faults, fresh airflow rate faults, and fresh air temperature faults. By outranking the differentoperating alternatives, the online fault-tolerant control strategy can achievethe fault correction factor according to the optimal operating scheme. Theonline fault tolerant can thus be implemented to correct the faultymeasurements. For the supply air temperature fault+13%, the total faultcorrection factor keeps at0.84from the sixth correction. For the fresh air flowrate fault+39%, the total fault correction factor maintains at0.66from theseventh correction. For the fresh air temperature fault–34%, the faultcorrection responses are output by the first calculation of the outrankingsatisfaction scores.
Generally, the proposed fault detection method combined the statisticalresiduals with the fractal dimension, can detect the various degrees of faults with more effectiveness. The improved ELECTRE evaluation model canmore outrank dozens of operating alternatives with more reasonability. Thedeveloped online fault tolerant control strategy can correct the sensor faultsgenerated in air conditioning systems, and can optimize the system operatingperformance. Further investigation should focus on how to identify severalfaults and how to complete the online fault tolerant control under the data lossor several faults conditions.
引文
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