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Some Recent Developments in SHM Based on Nonstationary Time Series Analysis

机译：基于非平稳时间序列分析的SHM的一些最新进展

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摘要

Many of the algorithms used for structural health monitoring (SHM) are based on, or motivated by, time series analysis. Quite often, detection methods are variants of approaches developed within the statistical process control (SPC) community. Many of the algorithms used represent mature theory and have a rigorous probabilistic or mathematical basis. However, one of the main issues facing SHM practitioners is that the structures of interest rarely respect the assumptions inherent in deriving algorithms. In the case of time series data, SPC-based approaches usually require the data to be stationary and, unfortunately, SHM data are often nonstationary because of benign variations in the environment of the structure of interest, or because of deliberate operational changes in the use of the structure. This nonstationarity can manifest itself as slowly varying trends on the data or in abrupt switches between regimes. Recent work in nonstationary time series methods for SHM has made considerable progress in accommodating nonstationarity and some of that work is discussed within this paper: in terms of understanding slowly varying trends, the cointegration algorithm from econometrics is presented; for understanding abrupt switches, Bayesian mixtures of experts are presented. Another issue in time series analysis is indirectly related to the assumption of linear behavior of structures and the impact of this assumption is briefly considered in terms of its effects on detection thresholds in SPC-like methods; again, progress has been made recently. Some issues still remain, and these are discussed also.
机译：用于结构健康状况监视（SHM）的许多算法都是基于时间序列分析或由时间序列分析激发的。通常，检测方法是在统计过程控制（SPC）社区内开发的方法的变体。使用的许多算法代表了成熟的理论，并具有严格的概率或数学基础。但是，SHM从业人员面临的主要问题之一是，感兴趣的结构很少尊重推导算法中固有的假设。对于时间序列数据，基于SPC的方法通常要求数据是固定的，但不幸的是，由于目标结构环境的良性变化或使用中的故意操作更改，SHM数据通常是不稳定的结构。这种非平稳性可以表现为数据上缓慢变化的趋势，或者在不同制式之间突然切换。 SHM的非平稳时间序列方法的最新工作在适应非平稳性方面取得了长足的进步，本文对其中的一些工作进行了讨论：从理解缓慢变化的趋势出发，提出了计量经济学的协整算法；为了理解突变开关，提出了贝叶斯专家混合。时间序列分析中的另一个问题与结构线性行为的假设间接相关，并且根据其对类似于SPC的方法中检测阈值的影响来简要考虑此假设的影响。再次，最近取得了进展。仍然存在一些问题，并进行了讨论。

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