Inclinometer probes are devices that can be used to measure deformations within earthwork slopes. This paper demonstrates a novel application of Bayesian techniques to real-world inclinometer data, providing both anomaly detection and forecasting. Specifically, this paper details an analysis of data collected from inclinometer data across the entire UK rail network. Practitioners have effectively two goals when processing monitoring data. The first is to identify any anomalous or dangerous movements, and the second is to predict potential future adverse scenarios by forecasting. In this paper we apply Uncertainty Quantification (UQ) techniques by implementing a Bayesian approach to anomaly detection and forecasting for inclinometer data. Subsequently, both costs and risks may be minimised by quantifying and evaluating the appropriate uncertainties. This framework may then act as an enabler for enhanced decision making and risk analysis. We show that inclinometer data can be described by a latent autocorrelated Markov process derived from measurements. This can be used as the transition model of a non-linear Bayesian filter. This allows for the prediction of system states. This learnt latent model also allows for the detection of anomalies: observations that are far from their expected value may be considered to have `high surprisal', that is they have a high information content relative to the model encoding represented by the learnt latent model. We successfully apply the forecasting and anomaly detection techniques to a large real-world data set in a computationally efficient manner. Although this paper studies inclinometers in particular, the techniques are broadly applicable to all areas of engineering UQ and Structural Health Monitoring (SHM).

翻译：测斜探头是用于测量土石边坡变形的设备。本文展示了一种将贝叶斯技术应用于实际测斜仪数据的新方法，同时实现异常检测与预测功能。具体而言，本文详细分析了覆盖英国铁路网络的测斜仪监测数据。从业者在处理监测数据时主要面临两个目标：一是识别任何异常或危险变形，二是通过预测预判未来潜在的不利情景。本文通过实施贝叶斯方法进行测斜仪数据的异常检测与预测，应用了不确定性量化（UQ）技术。通过量化与评估适当的不确定性，可同时实现成本与风险的最小化。该框架可作为增强决策制定与风险分析的基础工具。我们证明，测斜仪数据可通过由测量值推导的隐式自相关马尔可夫过程进行描述。该过程可作为非线性贝叶斯滤波器的转移模型，从而实现对系统状态的预测。该学习得到的隐式模型还能用于异常检测：若观测值显著偏离其期望值，则可视为具有"高意外性"，即相对于学习得到的隐式模型编码，此类观测值蕴含较高的信息量。我们以高效的计算方式，成功将预测与异常检测技术应用于大规模真实数据集。尽管本文以测斜仪为研究对象，但所提方法可广泛适用于工程领域中的不确定性量化与结构健康监测（SHM）各类应用场景。