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Development of smart piezoelectric transducer self-sensing, self-diagnosis and tuning schemes for structural health monitoring applications.

机译：开发智能压电传感器自感应，自诊断和调试方案，用于结构健康监测应用。

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Autonomous structural health monitoring (SHM) systems using active sensing devices have been studied extensively to diagnose the current state of aerospace, civil infrastructure and mechanical systems in near real-time and aims to eventually reduce life-cycle costs by replacing current schedule-based maintenance with condition-based maintenance.;This research develops four schemes for SHM applications: (1) a simple and reliable PZT transducer self-sensing scheme; (2) a smart PZT self-diagnosis scheme; (3) an instantaneous reciprocity-based PZT diagnosis scheme; and (4) an effective PZT transducer tuning scheme.;First, this research develops a PZT transducer self-sensing scheme, which is a necessary condition to accomplish a PZT transducer self-diagnosis. Main advantages of the proposed self-sensing approach are its simplicity and adaptability. The necessary hardware is only an additional self-sensing circuit which includes a minimum of electric components. With this circuit, the self-sensing parameters can be calibrated instantaneously in the presence of changing operational and environmental conditions of the system. In particular, this self-sensing scheme focuses on estimating the mechanical response in the time domain for the subsequent applications of the PZT transducer self-diagnosis and tuning with guided wave propagation. The most significant challenge of this self-sensing comes from the fact that the magnitude of the mechanical response is generally several orders of magnitude smaller than that of the input signal. The proposed self-sensing scheme fully takes advantage of the fact that any user-defined input signals can be applied to a host structure and the input waveform is known. The performance of the proposed self-sensing scheme is demonstrated by theoretical analysis, numerical simulations and various experiments.;Second, this research proposes a smart PZT transducer self-diagnosis scheme based on the developed self-sensing scheme. Conventionally, the capacitance change of the PZT wafer is monitored to identify the abnormal PZT condition because the capacitance of the PZT wafer is linearly proportional to its size and also related to the bonding condition. However, temperature variation is another primary factor that affects the PZT capacitance. To ensure the reliable transducer self-diagnosis, two different self-diagnosis features are proposed to differentiate two main PZT wafer defects, i.e., PZT debonding and PZT cracking, from temperature variations and structural damages. The PZT debonding is identified using two indices based on time reversal process (TRP) without any baseline data. Also, the PZT cracking is identified by monitoring the change of the generated Lamb wave power ratio index with respect to the driving frequency. The uniqueness of this self-diagnosis scheme is that the self-diagnosis features can differentiate the PZT defects from environmental variations and structural damages. Therefore, it is expected to minimize false-alarms which are induced by operational or environmental variations as well as structural damages. The applicability of the proposed self-diagnosis scheme is verified by theoretical analysis, numerical simulations, and experimental tests.;Third, a new methodology of guided wave-based PZT transducer diagnosis is developed to identify PZT transducer defects without using prior baseline data. This methodology can be applied when a number of same-size PZT transducers are attached to a target structure to form a sensor network. The advantage of the proposed technique is that abnormal PZT transducers among intact PZT transducers can be detected even when the system being monitored is subjected to varying operational and environmental conditions or changing structural conditions. To achieve this goal, the proposed diagnosis technique utilizes the linear reciprocity of guided wave propagation between a pair of surface-bonded PZT transducers.;Finally, a PZT transducer tuning scheme is being developed for selective Lamb wave excitation and sensing. This is useful for structural damage detection based on Lamb wave propagation because the proper transducer size and the corresponding input frequency can be is crucial for selective Lamb wave excitation and sensing. The circular PZT response model is derived, and the energy balance is included for a better prediction of the PZT responses because the existing PZT response models do not consider any energy balance between Lamb wave modes. In addition, two calibration methods are also suggested in order to model the PZT responses more accurately by considering a bonding layer effect. (Abstract shortened by UMI.)
机译：已经广泛研究了使用主动感应设备的自主结构健康监测（SHM）系统，以近实时地诊断航空航天，民用基础设施和机械系统的当前状态，旨在通过取代当前基于计划的维护来最终降低生命周期成本本研究针对SHM应用开发了四种方案：（1）一种简单可靠的PZT传感器自传感方案； （2）智能的PZT自诊断方案； （3）基于瞬时互惠的PZT诊断方案； （4）一种有效的PZT换能器调谐方案。首先，本研究提出了一种PZT换能器自传感方案，这是完成PZT换能器自诊断的必要条件。所提出的自感应方法的主要优点是其简单性和适应性。必要的硬件只是一个附加的自感应电路，其中包括最少的电子组件。使用该电路，可以在系统运行和环境条件发生变化的情况下即时校准自感应参数。尤其是，这种自感应方案的重点是在时域中估算机械响应，以用于PZT换能器自诊断的后续应用以及通过导波传播进行调谐。这种自感应的最大挑战来自以下事实：机械响应的幅度通常比输入信号的幅度小几个数量级。所提出的自感测方案充分利用了以下事实：任何用户定义的输入信号都可以应用于主机结构，并且输入波形是已知的。通过理论分析，数值模拟和各种实验验证了所提出的自感应方案的性能。其次，本研究提出了一种基于已开发的自感应方案的智能PZT换能器自诊断方案。常规地，监视PZT晶片的电容变化以识别异常的PZT条件，因为PZT晶片的电容与其尺寸成线性比例，并且还与接合条件相关。但是，温度变化是影响PZT电容的另一个主要因素。为了确保可靠的换能器自诊断，提出了两种不同的自诊断功能来区分两个主要的PZT晶片缺陷（即PZT脱胶和PZT裂纹）与温度变化和结构损坏。使用基于时间反转过程（TRP）的两个索引来标识PZT脱胶，而无需任何基线数据。另外，通过监视生成的兰姆波功率比指标相对于驱动频率的变化来识别PZT裂纹。这种自我诊断方案的独特之处在于，自我诊断功能可以将PZT缺陷与环境变化和结构破坏区分开。因此，期望将由操作或环境变化以及结构损坏引起的错误警报最小化。理论分析，数值模拟和实验测试验证了所提出的自诊断方案的适用性。第三，开发了一种基于导波的PZT换能器诊断的新方法，可以在不使用现有基准数据的情况下识别PZT换能器缺陷。当许多相同大小的PZT传感器连接到目标结构以形成传感器网络时，可以应用此方法。所提出的技术的优点在于，即使被监视的系统处于变化的操作和环境条件或变化的结构条件下，完整的PZT换能器中的异常PZT换能器也可以被检测到。为实现这一目标，所提出的诊断技术利用了导波在一对表面键合的PZT换能器之间传播的线性互易性。最后，正在开发一种PZT换能器调谐方案，用于选择性Lamb波激发和传感。这对于基于Lamb波传播的结构损伤检测很有用，因为适当的换能器尺寸和相应的输入频率对于选择性Lamb波激发和传感至关重要。推导了圆形PZT响应模型，并包括了能量平衡，以便更好地预测PZT响应，因为现有的PZT响应模型没有考虑兰姆波模式之间的任何能量平衡。此外，还提出了两种校准方法，以便通过考虑粘结层效应来更准确地对PZT响应建模。 （摘要由UMI缩短。）

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