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首页> 外文会议>Human System Interactions, 2009. HSI '09 >Unscented Kalman Filter (UKF) and frequency analysis (FA) techniques used for fault detection, diagnosis and isolation (FDDI) in Heating Ventilation Air Conditioning systems (HVAC)-comparison results
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Unscented Kalman Filter (UKF) and frequency analysis (FA) techniques used for fault detection, diagnosis and isolation (FDDI) in Heating Ventilation Air Conditioning systems (HVAC)-comparison results

机译：用于暖通空调系统（HVAC）的故障检测，诊断和隔离（FDDI）的无味卡尔曼滤波器（UKF）和频率分析（FA）技术-比较结果

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摘要

Monitoring and controlling the modern and sophisticated heating ventilation air conditioning (HVAC) building systems under a wide variety of occupancy and load related operating conditions represents a difficult and challenging task. Their complexity drastically increases and the control becomes more difficult task due to the several control loops that interact between them. The main objective of this study is to compare the performance of the automated strategies for fault detection, diagnosis and isolation (FDDI) based on frequency and spectral analysis (FA) of the system response, and an interactive multiple model (IMM), based on the unscented Kalman Filter (UKF) estimation technique to the problem of fault detection diagnosis and isolation (FDDI) of the valve actuator failures in discharge air temperature (DAT) loop of the HVAC systems. The both techniques are HVAC model-driven based and the simulations results reveal the superiority of the interactive multiple model based on unscented Kalman filter estimation algorithm (IMM_UKF) concerning its accuracy and robustness to the changes in the system structure parameters. These algorithms are implemented in a simulation environment, and the fault diagnosis results are presented for a several fault scenarios in terms of mode probabilities and active fault index. From the preliminaries simulations, for different scenarios we found that the IMM_UKF algorithm is robust to the choice of the matrix probability and to the small changes in process and measurement noise level, result that is confirmed in the literature.
机译：在各种各样的占用和负载相关的运行条件下，监视和控制现代复杂的采暖通风空调（HVAC）建筑系统是一项艰巨而艰巨的任务。由于它们之间相互作用的几个控制回路，它们的复杂性急剧增加，并且控制变得更加困难。这项研究的主要目的是比较基于系统响应的频率和频谱分析（FA）的故障检测，诊断和隔离自动化策略（FDDI）的性能，以及基于以下方面的交互式多重模型（IMM）的性能： Unscented Kalman Filter（UKF）估计技术解决了HVAC系统排气温度（DAT）回路中阀门执行器故障的故障检测诊断和隔离（FDDI）问题。两种技术都是基于HVAC模型驱动的，并且仿真结果揭示了基于无味卡尔曼滤波器估计算法（IMM_UKF）的交互式多重模型在其对系统结构参数变化的准确性和鲁棒性方面的优越性。这些算法是在仿真环境中实现的，并且根据模式概率和活动故障指数，针对几种故障场景提供了故障诊断结果。从初步模拟中，我们发现在不同情况下，IMM_UKF算法对于矩阵概率的选择以及过程和测量噪声水平的微小变化具有鲁棒性，这一结果在文献中得到了证实。

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