Department of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.
Changchun Changguang Yuanchen Microelectronic Technology Co., Ltd., Changchun 130000, China.
Sensors (Basel). 2023 Jun 25;23(13):5891. doi: 10.3390/s23135891.
With the steady improvement of advanced manufacturing processes and big data technologies, modern industrial systems have become large-scale. To enhance the sensitivity of fault detection (FD) and overcome the drawbacks of the centralized FD framework in dynamic systems, a new data-driven FD method based on Hellinger distance and subspace techniques is proposed for dynamic systems. Specifically, the proposed approach uses only system input/output data collected via sensor networks, and the distributed residual signals can be generated directly through the stable kernel representation of the process. Based on this, each sensor node can obtain the identical residual signal and test statistic through the average consensus algorithms. In addition, this paper integrates the Hellinger distance into the residual signal analysis for improving the FD performance. Finally, the effectiveness and accuracy of the proposed method have been verified in a real multiphase flow facility.
随着先进制造工艺和大数据技术的稳步提高，现代工业系统已经变得大规模化。为了提高故障检测（FD）的灵敏度，并克服动态系统中集中式 FD 框架的缺点，提出了一种基于 Hellinger 距离和子空间技术的新的数据驱动 FD 方法，用于动态系统。具体来说，所提出的方法仅使用通过传感器网络收集的系统输入/输出数据，并且可以通过过程的稳定核表示直接生成分布式残差信号。在此基础上，每个传感器节点都可以通过平均一致性算法获得相同的残差信号和测试统计信息。此外，本文将 Hellinger 距离集成到残差信号分析中，以提高 FD 性能。最后，在实际的多相流设备中验证了所提出方法的有效性和准确性。
