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.

本文引入一种名为 “预测和比较” 的预测机器学习模型辅助的变点检测框架，用于改进传统的连续分析方法，基于假阳性率和失控平均运行长度方面的质量指标，从而提高连续分析方法的精度，而在使用预测动态平均值这种基本趋势估计功能时，采用了更为先进的 ARIMA 模型。