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    • 5. 发明申请
    • FAULT DIAGNOSTICS AND PROGNOSTICS BASED ON DISTANCE FAULT CLASSIFIERS
    • 基于距离故障分类器的故障诊断与预测
    • WO2006026267A2
    • 2006-03-09
    • PCT/US2005/029964
    • 2005-08-19
    • CARRIER CORPORATIONFARZAD, MohsenSADEGH, Payman
    • FARZAD, MohsenSADEGH, Payman
    • G01K13/00
    • G01K15/00F24F11/30F24F11/32F24F2120/10F25B13/00F25B49/005
    • The present invention is directed to a mathematical approach to detect faults by reconciling known data driven techniques with a physical understanding of the HVAC system and providing a direct linkage between model parameters and physical system quantities to arrive at classification rules that are easy to interpret, calibrate and implement. The fault modes of interest are low system refrigerant charge and air filter plugging. System data from standard sensors is analyzed under no-fault and full-fault conditions. The data is screened to uncover patterns though which the faults of interest manifest in sensor data and the patterns are analyzed and combined with available physical system information to develop an underlying principle that links failures to measured sensor responses. These principles are then translated into online algorithms for failure detection.
    • 本发明涉及一种通过协调已知的数据驱动技术与对HVAC系统的物理理解并提供模型参数和物理系统量之间的直接联系以检测故障来进行分类的数学方法 易于解释,校准和实施的规则。 感兴趣的故障模式是低系统制冷剂充注和空气过滤器堵塞。 在无故障和全故障条件下分析标准传感器的系统数据。 筛选数据以揭示模式,尽管感兴趣的故障表现在传感器数据中,并分析模式并与可用的物理系统信息相结合,以开发将故障与测量的传感器响应相联系的基本原理。 然后将这些原则转化为在线算法来检测故障。
    • 6. 发明申请
    • TECHNIQUE FOR DETECTING AND PREDICTING AIR FILTER CONDITION
    • 用于检测和预测空气过滤器条件的技术
    • WO2005110580A2
    • 2005-11-24
    • PCT/US2005/011621
    • 2005-04-07
    • CARRIER CORPORATION
    • KANG, PengjuFARZAD, MohsenSTRICEVIC, SlavenSADEGH, PaymanFINN, Alan, M.
    • B01D46/46
    • B01D46/0086B01D46/444B01D2273/30Y10S116/25
    • A method and system for detecting and predicting air filter condition for an air handling system operates by determining a system resistance to air flow. The system resistance is utilized to determine a detection statistic indicative of current filter condition and to predict remaining life of the air filter. The system resistance is determined using models that approximate the expected operation of the air handling system. The approximation is then compared to actual values to obtain a difference. Once the difference between the approximated value exceeds a threshold value, an alarm is initiated that is indicative of system resistance. The remaining air filter life is then determined by using historically gathered data, or by using a known degradation rate of the air filter. Once the remaining life of the air filter is estimated, replacement can be scheduled that would coincide with other maintenance.
    • 用于检测和预测空气处理系统的空气过滤器状态的方法和系统通过确定系统对气流的阻力来操作。 系统电阻用于确定表示当前过滤条件的检测统计量,并预测空气过滤器的剩余寿命。 使用近似空气处理系统的预期操作的模型确定系统阻力。 然后将近似值与实际值进行比较以获得差异。 一旦近似值之间的差超过阈值,则启动指示系统电阻的报警。 然后通过使用历史收集的数据或通过使用空气过滤器的已知降解速率来确定剩余的空气过滤器寿命。 一旦估计空气过滤器的剩余寿命,可以安排与其他维护一致的更换。
    • 7. 发明申请
    • SENSOR FAULT DIAGNOSTICS AND PROGNOSTICS USING COMPONENT MODEL AND TIME SCALE ORTHOGONAL EXPANSIONS
    • 传感器故障诊断和预处理使用组件模型和时间尺度正交扩展
    • WO2005111806A2
    • 2005-11-24
    • PCT/US2005/011620
    • 2005-04-07
    • CARRIER CORPORATION
    • KANG, PengjuFARZAD, MohsenSTRICEVIC, SlavenSADEGH, PaymanFINN, Alan, M.
    • G06F11/30
    • G05B9/02G05B2219/31359Y02P90/22
    • A method of diagnosing sensor faults for a heating, ventilation and air conditioning system includes the steps of creating a component model for a specific component within the system. The component model is created through the use of commonly available manufacturing data. Data within the system is input into the component model and compared to calculated and predicted values that are also calculated using the identical component models. Differences between the calculated and actual values is determined and compared to a threshold difference value. If the difference exceeds the threshold value, then a fault is detected. The specific type of sensor fault is determined using probability distribution analysis. Each type of sensor fault produces a different type of statistical deviation from normal distribution. By recognizing these patterns of deviations from the normal distribution, the specific type of fault such as electrical, intermittent or freezing of the sensor can be determined to provide initial information as to the severity and type of remedial action required.
    • 诊断加热,通风和空调系统的传感器故障的方法包括为系统内的特定部件创建组件模型的步骤。 组件模型是通过使用常用的制造数据创建的。 将系统中的数据输入到组件模型中,并与使用相同组件模型计算的计算值和预测值进行比较。 确定计算值和实际值之间的差异,并与阈值差值进行比较。 如果差值超过阈值,则检测到故障。 使用概率分布分析确定传感器故障的具体类型。 每种类型的传感器故障都会产生与正态分布不同的统计偏差。 通过识别这些偏离正常分布的模式,可以确定传感器的电气,间歇或冻结的特定类型的故障,以提供所需补救措施的严重性和类型的初始信息。