Population-based structural health monitoring (PBSHM) aims to share valuable
information among members of a population, such as normal- and damage-condition
data, to improve inferences regarding the health states of the members. Even
when the population is comprised of nominally-identical structures, benign
variations among the members will exist as a result of slight differences in
material properties, geometry, boundary conditions, or environmental effects
(e.g., temperature changes). These discrepancies can affect modal properties
and present as changes in the characteristics of the resonance peaks of the
frequency response function (FRF). Many SHM strategies depend on monitoring the
dynamic properties of structures, so benign variations can be challenging for
the practical implementation of these systems. Another common challenge with
vibration-based SHM is data loss, which may result from transmission issues,
sensor failure, a sample-rate mismatch between sensors, and other causes.
Missing data in the time domain will result in decreased resolution in the
frequency domain, which can impair dynamic characterisation. The hierarchical
Bayesian approach provides a useful modelling structure for PBSHM, because
statistical distributions at the population and individual (or domain) level
are learnt simultaneously to bolster statistical strength among the parameters.
As a result, variance is reduced among the parameter estimates, particularly
when data are limited. In this paper, combined probabilistic FRF models are
developed for a small population of nominally-identical helicopter blades under
varying temperature conditions, using a hierarchical Bayesian structure. These
models address critical challenges in SHM, by accommodating benign variations
that present as differences in the underlying dynamics, while also considering
(and utilising), the similarities among the blades.

本文讨论了采用层次贝叶斯方法的结构健康监测策略，解决了人员差异，数据缺失和变化的挑战，并为一小批通用直升机叶片开发了概率 FRF 模型。