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.

翻译：本文提出了一种基于重正化群方法的新型无监督预测性维护分析方法，该方法常用于发现复杂系统中的临界行为。该算法分析单变量时间序列，并基于一项新提出的定理检测临界点，该定理通过拟合对数周期幂律函数来识别临界点。本文展示了将新算法应用于从往复式压缩机系统收集的工业数据进行预测性维护分析。基于对所分析压缩机系统动力学的认知，所提算法能够提前预测阀门和活塞杆密封故障。