Condition monitoring for pattern recognition in manufacturing

Condition monitoring techniques stand as essential instruments for evaluating the health and performance of machinery and systems, serving as a foundational element of modern engineering. However, many existing techniques, including advanced approaches, are often tailored to specific domains, limiting their flexibility and adaptability. This paper introduces the CO ndition mo N itoring D etection via c OR relation-based norms (CONDOR), a fully unsupervised, system-agnostic, and multiscale method that leverages matrix norms and correlation matrices derived from time series data recorded by sensors during machine operation. Designed for real-time application, the approach is particularly effective in manufacturing environments characterized by cyclic processes, where consistent inputs yield predictable behaviors. The methodology was validated on both synthetic and real-world datasets, successfully identifying operational patterns that align with common manufacturing system behaviors. Importantly, patterns identified in synthetic data were consistently detected in real-world scenarios, underscoring CONDOR’s robustness and reliability. Comparisons with state-of-the-art algorithms further highlight its superior ability to detect patterns and establish stable clusters, making it a promising tool for condition monitoring in diverse industrial contexts. • CONDOR is an unsupervised, system-agnostic, and multiscale condition monitoring method. • Relies on matrix norms applied to correlation matrices from cyclic sensor data. • Enables real-time monitoring without training or feature engineering. • Detects patterns and transitions under noise and gradual wear conditions. • Outperforms state-of-the-art methods in accuracy, robustness, and computational speed.