# Predictive maintenance solution for industrial systems -- an unsupervised approach based on log periodic power law

arXiv: https://arxiv.org/abs/2408.05231 · HTML: https://mlsanity.com/paper/2408.05231/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding](https://mlsanity.com/paper/1802.04431/) (2018)
- [A Survey of Predictive Maintenance: Systems, Purposes and Approaches](https://mlsanity.com/paper/1912.07383/) (2019)
- [Fast Online Changepoint Detection via Functional Pruning CUSUM statistics](https://mlsanity.com/paper/2110.08205/) (2021)
- [Estimating Model Performance Under Covariate Shift Without Labels](https://mlsanity.com/paper/2401.08348/) (2024)
- [Confidence-based Estimators for Predictive Performance in Model Monitoring](https://mlsanity.com/paper/2407.08649/) (2024)
- [A Log-Linear Non-Parametric Online Changepoint Detection Algorithm based on Functional Pruning](https://mlsanity.com/paper/2302.02718/) (2023)
- [Open-Source Drift Detection Tools in Action: Insights from Two Use Cases](https://mlsanity.com/paper/2404.18673/) (2024)
- [A Constant-per-Iteration Likelihood Ratio Test for Online Changepoint Detection for Exponential Family Models](https://mlsanity.com/paper/2302.04743/) (2023)

## Abstract (arXiv)

A new unsupervised predictive maintenance analysis method based on the renormalization group approach used to discover critical behavior in complex systems has been proposed. The algorithm analyzes univariate time series and detects critical points based on a newly proposed theorem that identifies critical points using a Log Periodic Power Law function fits. Application of a new algorithm for predictive maintenance analysis of industrial data collected from reciprocating compressor systems is presented. Based on the knowledge of the dynamics of the analyzed compressor system, the proposed algorithm predicts valve and piston rod seal failures well in advance.