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“预测与比较”辅助的变点检测（CPD）框架被引入，并与其它最新的在线CPD方法进行了对比表征。该框架在误报率和失控平均运行长度方面优于现有方法。其核心在于通过从序列分析中改进标准方法（如CUSUM规则）来实现这些质量指标的优化。具体而言，该方法用更复杂的预测模型（预测步骤）替代通常使用的趋势估计泛函（如运行均值），并将预测结果与实际数据进行比较（比较步骤）。预测步骤中采用的两种模型为ARIMA模型和LSTM递归神经网络。然而，该框架以通用形式表述，允许使用本文测试范围之外的其他预测或比较方法。该方法的力量通过一个摩擦学案例研究得以证明：在极少数误报的情况下，成功检测出区分磨合期、稳态期和差异磨损期的变点。