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首页> 外文学位 >Management of uncertainty in sensor validation, sensor fusion, and diagnosis of mechanical systems using soft computing techniques.
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Management of uncertainty in sensor validation, sensor fusion, and diagnosis of mechanical systems using soft computing techniques.

机译：使用软计算技术管理传感器验证，传感器融合和机械系统诊断中的不确定性。

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This dissertation provides means to deal with uncertainty in complex sensor driven systems through sensor validation, sensor fusion, and diagnosis. These means include probability theory, neural network theory, and fuzzy logic. In particular, this thesis introduces means for fuzzy sensor validation and fusion which were compared with a probabilistic data association scheme. The fuzzy sensor validation and fusion approach uses non-symmetric validation regions in which sensors readings are assigned confidence values. Each sensor has its own dynamic validation curve which is shaped according to sensor characteristics, taking into account the range, external factors affecting the sensor, reliability of the sensor, etc. The curves have their maximum value at the predicted value which is arrived at using fuzzy exponential weighted moving average time series predictor. Confidence curves attain minima at the boundaries of the validation gate which are determined by the maximum physically possible change a system can undergo in one time sample. Since readings outside the gate are implausible, they are discarded. Fusion is performed using a weighted average of sensor readings and confidence values, the predicted value scaled by the operating condition, and--if available--functionally redundant values calculated from sensors other than the directly redundant ones. Each method performs best in the presence of certain types of noise and recommendations are made as to which approach is more appropriate under various conditions. Several applications from extant systems (intelligent vehicle highway systems, gas turbine power plants, milling machines) show the feasibility of the approaches developed.; Another aspect of this dissertation is to provide a tool for diagnosis in the presence of vague symptoms. This is achieved though fuzzy abduction which can diagnose crisp as well as soft faults. This means that faults can be diagnosed if they occur to some degree. The proposed algorithm computes a closeness measure taking into account the distance from an observed symptom set to the modeled symptom set for all failure combinations. It then ranks the failure sets according to maximum closeness measure and minimum cover, i.e., number of faults. As an extension, a framework for fuzzy influence diagrams is provided which uses this closeness measure.
机译：本文通过传感器验证，传感器融合和诊断为复杂传感器驱动系统的不确定性提供了解决方法。这些手段包括概率论，神经网络理论和模糊逻辑。特别是，本文介绍了与概率数据关联方案进行比较的模糊传感器验证和融合方法。模糊传感器验证和融合方法使用非对称验证区域，在其中向传感器读数分配了置信度值。每个传感器都有自己的动态验证曲线，该曲线根据传感器的特性而定，并考虑到范围，影响传感器的外部因素，传感器的可靠性等。这些曲线的最大值在预测值上，可​​通过使用模糊指数加权移动平均时间序列预测变量。置信曲线在验证门的边界处达到最小值，这由系统一次采样中可能发生的最大物理变化确定。由于大门外的读数难以置信，因此将其丢弃。融合是使用传感器读数和置信度值的加权平均值，根据操作条件缩放的预测值以及（如果可用）根据从直接冗余传感器以外的传感器计算而来的功能冗余值执行的。每种方法在存在某些类型的噪声的情况下效果最佳，并针对在各种条件下哪种方法更合适提出了建议。现有系统（智能汽车高速公路系统，燃气轮机发电厂，铣床）的一些应用表明了所开发方法的可行性。本文的另一个方面是提供一种在存在模糊症状时进行诊断的工具。这可以通过模糊绑架实现，该绑架可以诊断出脆性故障和软故障。这意味着如果在某种程度上发生故障，则可以对其进行诊断。所提出的算法考虑到所有故障组合从观察到的症状集到建模的症状集的距离，计算了紧密度度量。然后，它根据最大接近度度量和最小覆盖率（即故障数）对故障集进行排序。作为扩展，提供了使用这种接近度度量的模糊影响图框架。

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