Esmaili Parisa, Martiri Luca, Esmaili Parvaneh, Cristaldi Loredana
Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, I-20133 Milan, Italy.
Department of Computer Engineering, Cyprus International University, Northern Cyprus, via Mersin 10, 99258 Nicosia, Türkiye.
Sensors (Basel). 2025 Jul 16;25(14):4431. doi: 10.3390/s25144431.
Advances in Industry 4.0 and the emergence of Industry 5.0 are driving the development of intelligent, sustainable manufacturing systems, where embedded sensing and real-time health diagnostics play a critical role. However, implementing robust predictive maintenance in production environments remains challenging due to the variability in machine operations and the lack of access to internal control data. This paper introduces a lightweight, embedded-compatible framework for health status signature extraction based on empirical mode decomposition (EMD), leveraging only data from a single triaxial accelerometer. The core of the proposed method is a cycle-synchronized segmentation strategy that uses accelerometer-derived velocity profiles and cross-correlation to align signals with machining cycles, eliminating the need for controller or encoder access. This ensures process-aware decomposition that preserves the operational context across diverse and dynamic machining conditions to address the inadequate segmentation of unstable process data that often fails to capture the full scope of the process, resulting in misinterpretation. The performance is evaluated on a challenging real-world manufacturing benchmark where the extracted intrinsic mode functions (IMFs) are analyzed in the frequency domain, including quantitative evaluation. As results show, the proposed method shows its effectiveness in detecting subtle degradations, following a low computational footprint, and its suitability for deployment in embedded predictive maintenance systems on brownfield or controller-limited machinery.
工业4.0的进步和工业5.0的出现推动了智能、可持续制造系统的发展，其中嵌入式传感和实时健康诊断发挥着关键作用。然而，由于机器操作的变异性以及无法获取内部控制数据，在生产环境中实施强大的预测性维护仍然具有挑战性。本文介绍了一种基于经验模态分解（EMD）的轻量级、嵌入式兼容的健康状态特征提取框架，该框架仅利用来自单个三轴加速度计的数据。所提出方法的核心是一种周期同步分割策略，该策略使用由加速度计得出的速度曲线和互相关来使信号与加工周期对齐，从而无需访问控制器或编码器。这确保了过程感知分解，在各种动态加工条件下保留操作上下文，以解决不稳定过程数据分割不足的问题，这种不足往往无法捕捉过程的全貌，从而导致误解。在一个具有挑战性的实际制造基准上对性能进行了评估，在频域中分析提取的本征模态函数（IMF），包括定量评估。结果表明，所提出的方法在检测细微退化方面显示出有效性，具有低计算量，并且适用于在棕地或控制器受限的机器上部署嵌入式预测性维护系统。
