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：一种由预测性机器学习模型辅助的变点检测（CPD）框架，称为“预测与比较”，本文对其进行了介绍，并与其他最新的在线CPD方法进行了特征对比。在误报率和失控平均运行长度方面，该框架优于这些方法。该方法的核心在于改进序贯分析中的标准方法（如CUSUM规则）在这类质量指标上的表现。其实现方式是将通常使用的趋势估计函数（如运行均值）替换为更复杂的预测模型（预测步骤），并将这些模型的预测结果与实际数据进行比较（比较步骤）。预测步骤中使用的两个模型为ARIMA模型和LSTM递归神经网络。然而，该框架以通用形式表述，以便允许使用本文测试之外的其他预测或比较方法。本方法的有效性通过一个摩擦学案例研究得到验证——在该案例中，于极低误报条件下成功检测出分界磨合、稳态和异常磨损阶段的变点。