# Gaussian Derivative Change-point Detection for Early Warnings of Industrial System Failures

arXiv: https://arxiv.org/abs/2410.22594 · HTML: https://mlsanity.com/paper/2410.22594/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 15 of 15)

- [Scalable Variational Gaussian Process Classification](https://mlsanity.com/paper/1411.2005/) (2014)
- [Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP)](https://mlsanity.com/paper/1503.01057/) (2015)
- [Variational Fourier features for Gaussian processes](https://mlsanity.com/paper/1611.06740/) (2016)
- [An Evaluation of Change Point Detection Algorithms](https://mlsanity.com/paper/2003.06222/) (2020)
- [Bayesian Optimization with Gradients](https://mlsanity.com/paper/1703.04389/) (2017)
- [Remaining Useful Lifetime Prediction via Deep Domain Adaptation](https://mlsanity.com/paper/1907.07480/) (2019)
- [Scaling Gaussian Processes with Derivative Information Using Variational Inference](https://mlsanity.com/paper/2107.04061/) (2021)
- [Random Forests for Change Point Detection](https://mlsanity.com/paper/2205.04997/) (2022)
- [Kernel Methods and their derivatives: Concept and perspectives for the Earth system sciences](https://mlsanity.com/paper/2007.14706/) (2020)
- [A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets](https://mlsanity.com/paper/2202.04258/) (2022)
- [A likelihood ratio approach to sequential change point detection for a general class of parameters](https://mlsanity.com/paper/1802.07696/) (2018)
- [Dataset: Rare Event Classification in Multivariate Time Series](https://mlsanity.com/paper/1809.10717/) (2018)
- [A Change Point Detection Integrated Remaining Useful Life Estimation Model under Variable Operating Conditions](https://mlsanity.com/paper/2401.04351/) (2024)
- [Optimal change point detection in Gaussian processes](https://mlsanity.com/paper/1506.01338/) (2015)
- [Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes](https://mlsanity.com/paper/2312.03176/) (2023)

## Built on by (most cited first, 2 of 2)

- [Distribution-free inference on the number of changepoints](https://mlsanity.com/paper/2609.08234/) (2026)
- [Reliability Engineering for AI Systems: Challenges, Methods, and Directions](https://mlsanity.com/paper/2609.35316/) (2026)

## Abstract (arXiv)

An early warning of future system failure is essential for conducting predictive maintenance and enhancing system availability. This paper introduces a three-step framework for assessing system health to predict imminent system breakdowns. First, the Gaussian Derivative Change-Point Detection (GDCPD) algorithm is proposed for detecting changes in the high-dimensional feature space. GDCPD conducts a multivariate Change-Point Detection (CPD) by implementing Gaussian derivative processes for identifying change locations on critical system features, as these changes eventually will lead to system failure. To assess the significance of these changes, Weighted Mahalanobis Distance (WMD) is applied in both offline and online analyses. In the offline setting, WMD helps establish a threshold that determines significant system variations, while in the online setting, it facilitates real-time monitoring, issuing alarms for potential future system breakdowns. Utilizing the insights gained from the GDCPD and monitoring scheme, Long Short-Term Memory (LSTM) network is then employed to estimate the Remaining Useful Life (RUL) of the system. The experimental study of a real-world system demonstrates the effectiveness of the proposed methodology in accurately forecasting system failures well before they occur. By integrating CPD with real-time monitoring and RUL prediction, this methodology significantly advances system health monitoring and early warning capabilities.