Abstract The model updating of the electro-mechanical properties of Piezoelectric Energy Harvesters (PEHs) using experimental data within a Bayesian inference setting is discussed. The implementation requires: a predictive model for the harvester response; an assumption for its prediction error; a prior multivariate probabilistic density function for the electromechanical properties; and experimental measurements of the harvester response. Different approaches are compared with respect to the Bayesian model updating, including point estimates of the updated properties based on Maximum a Posteriori and Maximum Likelihood Estimates, as well as a full description of the posterior density for the model characteristics, obtained through a Transitional Markov Chain Monte Carlo approach. A model class selection implementation is also discussed that allows for the consideration of some PEH properties as either deterministic or aleatoric (uncertain) variables. The overall framework offers an elegant approach to calibrate PEH numerical/analytical model or identify variability trends for the PEH manufacturing process.

中文翻译：

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压电能量采集器机电特性的贝叶斯识别

摘要 讨论了使用贝叶斯推理设置中的实验数据更新压电能量收集器 (PEH) 机电特性的模型。实施需要： 收割机响应的预测模型；对其预测误差的假设；机电特性的先验多元概率密度函数；和收割机响应的实验测量。关于贝叶斯模型更新的不同方法进行了比较，包括基于最大后验和最大似然估计的更新属性的点估计，以及通过过渡马尔可夫链获得的模型特征的后验密度的完整描述蒙特卡罗方法。还讨论了模型类选择实现，它允许将某些 PEH 属性视为确定性或任意（不确定）变量。整体框架提供了一种优雅的方法来校准 PEH 数值/分析模型或识别 PEH 制造过程的可变性趋势。