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Comprehensive Analysis for Air Supply Fan Faults Based on HVAC Mathematical Model

机译：基于HVAC数学模型的空气供应风扇故障综合分析

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摘要

Due to the growing demand on high efficient heat ventilation and air conditioning (HVAC) systems, how to improve the efficiency of HVAC system regarding reduces energy consumption of system has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependent upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the fault detection and isolation (FDI) system plays a crucial role for identifying failures. Finding healthy HVAC source as the reference for health monitoring is the main aim in this area. To dispel this concern a comprehensive transient model of heat ventilation and air conditioning (HVAC) systems is developed in this study. The transient model equations can be solved efficiently using MATLAB coding and simulation technique. Our proposed model is validated against real HVAC system regarding different parts of HVAC. The developed model in this study can be used for a pre tuning of control system and put to good use for fault detection and isolation in order to accomplish high-quality health monitoring and result in energy saving. Fan supply consider as faulty device of HVAC system with six fault type. A sensitivity analysis based on evaluated model shows us three features are sensitive to all faults type and three auxiliary features are sensitive to some faults. The magnitude and trait of features are a good potential for automatic fault tolerant system based on machine learning systems.
机译：由于对高效热通风和空调（HVAC）系统的需求不断增长，如何提高HVAC系统的效率，减少系统的能源消耗已成为关键问题之一。报告表明，效率和可用性严重依赖于高可靠性和可维护性。最近，已经引入了电子维护的概念以降低维护成本。在电子维护系统中，故障检测和隔离（FDI）系统对识别故障起到至关重要的作用。寻找健康的HVAC来源作为健康监测的参考是该领域的主要目标。为了消除这一局面，本研究开发了综合瞬态瞬态模型和空调（HVAC）系统。可以使用MATLAB编码和仿真技术有效地解决瞬态模型方程。我们提出的模型对关于HVAC的不同部分的真实HVAC系统验证。该研究中的开发模型可用于预调整控制系统，充分利用故障检测和隔离，以实现高质量的健康监测并导致节能。风扇供应认为具有六种故障类型的HVAC系统故障设备。基于评估模型的灵敏度分析表明，US三个特征对所有故障类型敏感，三个辅助功能对某些故障敏感。特征的幅度和特征是基于机器学习系统的自动容错系统的良好潜力。

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