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

TL;DR

This paper introduces an unsupervised predictive maintenance method using a renormalization group approach and log periodic power law fitting to detect critical points in industrial systems, enabling early failure prediction.

Contribution

It presents a novel unsupervised algorithm based on critical behavior analysis for predictive maintenance, specifically applied to reciprocating compressor systems.

Findings

Successfully predicts valve and piston seal failures in advance

Detects critical points using log periodic power law fits

Applies renormalization group approach to industrial data

Abstract

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.