A change point detection (CPD) framework assisted by a predictive machine learning model called "Predict and Compare" is introduced and characterised in relation to other state-of-the-art online CPD routines which it outperforms in terms of false positive rate and out-of-control average run length. The method's focus is on improving standard methods from sequential analysis such as the CUSUM rule in terms of these quality measures. This is achieved by replacing typically used trend estimation functionals such as the running mean with more sophisticated predictive models (Predict step), and comparing their prognosis with actual data (Compare step). The two models used in the Predict step are the ARIMA model and the LSTM recursive neural network. However, the framework is formulated in general terms, so as to allow the use of other prediction or comparison methods than those tested here. The power of the method is demonstrated in a tribological case study in which change points separating the run-in, steady-state, and divergent wear phases are detected in the regime of very few false positives.

翻译：一种名为"预测与比较"（Predict and Compare）的变点检测框架被提出，该框架借助预测性机器学习模型实现，并在与现有最优在线变点检测方法的对比中展现出更低的虚警率和更优的失控平均运行长度。该方法的核心在于通过改进序贯分析中的标准算法（如CUSUM准则）来优化上述质量指标，具体实现方式为：将传统趋势估计泛函（如滑动平均）替换为更复杂的预测模型（预测步骤），并将其预测结果与实际数据进行比较（比较步骤）。预测步骤采用的两种模型包括ARIMA模型与LSTM递归神经网络。然而，该框架以通用形式构建，可兼容本实验未涉及的其他预测或比较方法。通过摩擦学案例研究验证了该方法的有效性，在极低虚警条件下成功检测出跑合阶段、稳定磨损阶段和剧烈磨损阶段的分界变点。