Development of automotive body which can detected structural damage and it's structural health monitoring

近年来，无线通信技术的发展能够为驾驶员提供有用的信息和主动安全。虽然许多研究人员试图通过车载电视摄像机或探测车系统来监测交通拥堵、天气状况、路面状况和汽车维护，但这些传感器信号在车辆运动时具有非平稳和瞬态特性。结构健康监测用于此目的。在结构健康监测中，来自内置于结构中的传感器的响应信号用于监测结构的状况。当应变、振动、声音和红外传感器用于监测真实的结构时，它们通常受到环境和载荷变化的影响，经常导致现象和响应信号之间的非线性关系。由于来自连续经历振动的车辆状结构的响应信号包含关于车辆的振动的信息， ...更多信息 为了准确地解释响应信号的变化，需要一种高度精确的技术来监测和分析车辆的结构。为了预测现象的发生，我们需要建立一个模型，将响应信号与现象联系起来。高度准确的预测需要强大的泛化能力。一般的学习工具可以用于建模。这些技术所采用的学习过程使得能够创建复杂的非线性模型，但是它们需要适当地定义各种学习参数。即使是能够正确分类学习数据的模型也可能只产生局部解。为了获得具有很强泛化能力的模型，必须通过试错法确定学习参数，因此，本研究将使用统计技术进行学习和估计的支持向量机方法应用于结构健康诊断。由于用于创建基于统计学习理论的高度概括性的模型的方法显然是最优化问题，因此期望SVM给出对现象的高度准确的预测。我们研究和评估SVM应用到振动响应，这是依赖于各种因素，并使用它来诊断车辆的结构健康。少

In recent years, development of the wireless communication technology is able to provide useful information and active safety for the drivers. Though many researchers try to monitor a traffic congestion, weather condition, road surface condition and car maintenance by the on-board TV camera or the probe car system, sensor signals of these have non-stationary and transitory characteristics while the vehicle is in motion. Structural health monitoring is used for this purpose. In structural health monitoring, the response signals from sensors built into a structure are used to monitor the condition of the structure. When strain, vibration, sound and infrared sensors are used for monitoring real structures, they are generally affected by environmental and load changes often resulting in a nonlinear relationship between phenomena and the response signals. Since the response signals from a vehicle-like structure that continually experiences vibration contain information about the vehicle's o … More perating conditions and environment, a highly accurate technique for monitoring and analyzing the vehicle's structure is required to interpret changes in the response signal accurately.In order to predict the occurrence of phenomena we need to create a model that relates the response signals to the phenomena. Strong generalization capabilities are required for highly accurate predictions. General learning tools can be used for modeling. The learning processes employed by these techniques enable the creation of a complicated nonlinear model, but they require defining the various learning parameters appropriately. Even a model capable of classifying learning data correctly may yield only a local solution. In order to obtain a model that has great generalizability, the learning parameters must be determined by trial and error.In this study, therefore, the Support Vector Machines method that uses a statistical technique for learning and estimating is applied to structural health diagnosis. Since the method for creating a model of great generalizability based on a theory of statistical learning is clearly an optimization problem, SVM is expected to give highly accurate prediction of phenomena. We investigate and evaluate SVM by applying it to the vibration response, which is dependent on various factors, and using it to diagnose the structural health of a vehicle. Less