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 COndition moNitoring Detection via cORrelation-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.