An enhanced kernel learning data-driven method for multiple fault detection and identification in industrial systems

作者：Sun, Chengyuan; Ma, Hongjun^*

来源：Information Sciences, 2022, 615: 431-448.

DOI：10.1016/j.ins.2022.10.053

• Dynamic behavior
• Quality-related
• Multiple fault detection
• Kernel learning

Traditional fault detection methods focus mainly on a single abnormal condition of the sys-tem. However, successive multiple faults are more common than a single fault in industrial systems. Hence, this paper proposes a novel algorithm for detecting and identifying mul-tiple faults associated with the quality indicators of the process. Considering the dynamic feature and measurement noise in the system, an enhanced kernel learning data-driven (EKLDD) algorithm is designed to improve the performance of modeling and multiple fault detection. In addition, a monitoring scheme is proposed to evaluate the quality status under every fault based on the fault line and the angle statistics. Lastly, a simulation case and a real-world case are presented to illustrate the feasibility and effectiveness of the pro-posed EKLDD method.

• 单位
  东北大学