Predictive maintenance solution for industrial systems -- an unsupervised approach based on log periodic power law
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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.
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References · 8
- 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 407
- 2019A Survey of Predictive Maintenance: Systems, Purposes and Approachescited by 21
- 2021Fast Online Changepoint Detection via Functional Pruning CUSUM statisticscited by 10
- 2024Estimating Model Performance Under Covariate Shift Without Labelscited by 9
- 2024Confidence-based Estimators for Predictive Performance in Model Monitoringcited by 8
- 2023A Log-Linear Non-Parametric Online Changepoint Detection Algorithm based on Functional Pruningcited by 7
- 2023A Constant-per-Iteration Likelihood Ratio Test for Online Changepoint Detection for Exponential Family Modelscited by 2
- 2024Open-Source Drift Detection Tools in Action: Insights from Two Use Casescited by 2
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