A Quality-Related Fault Detection Method Based on the Dynamic Data-Driven Algorithm for Industrial Systems

作者：Sun, Cheng-Yuan; Yin, Yi-Zhen; Kang, Hao-Bo^*; Ma, Hong-Jun

来源：IEEE Transactions on Automation Science and Engineering, 2022, 19(4): 3942-3952.

DOI：10.1109/TASE.2021.3139766

• Fault detection
• Kernel
• Monitoring
• Heuristic algorithms
• Nonlinear dynamical systems
• Entropy
• Principal component analysis
• Dynamic feature
• quality-related
• fault detection
• KECA
• DKECR

For nearly a decade, quality-related fault detection algorithms have been widely used in industrial systems. However, the majority of these detection strategies rely on static assumptions of the operating environment. In this paper, taking the time series of variables into consideration, a dynamic kernel entropy component regression (DKECR) framework is proposed to address the instability of quality-related fault detection due to the existing dynamic characteristics. Compared with the typical kernel entropy component analysis method, the proposed method constructs the relationship between process states and quality states to further interpret the direct effect on the product taken by the fault. In the proposed approach, process measurements are converted to a lower-dimensional subspace with a specific angular structure that is more comprehensive than traditional subspace approaches. In addition, the angular statistics and their relevant thresholds are exploited to enhance the quality-related fault detection performance. Finally, the proposed method will be compared with three methods by means of a numerical example and two industrial scenarios to demonstrate its practicality and effectiveness.

• 单位
  东北大学; 中国科学院