基干T-PL5贡献图方法的故障诊断技术(英文)

• 作 者: 李钢 秦洒钊 吉吟东 周东华
• 机 构: Department of Automation, Tsinghua National Laboratory for Information Science and Technology, Tsing
• 出 处: 自动化学报
• 发表日期: 2009年06期
• 关键词: latent projection contributions unrelated Tennessee Eastman 故障诊断 statistic opera

Multivariate statistical process monitoring (MSPM) is an efficient data-driven fault detection and diagnosis approach for complex industrial processes. Partial least squares or projection to latent structures (PLS) is one of the latent projection structures used in MSPM, which uses process data X and quality data Y together. In this paper, we discuss a new fault diagnosis approach based on total projection to latent structures (T-PLS). Four kinds of monitoring statistics are used in T-PLS, and a new definition of variable contributions to T 2 of PLS is proposed. Then, definitions of variable contributions to all statistics are derived to identify the faults. Control limits for contribution plots are calculated to identify whether a variable is in abnormal situation or not. Further, the proposed method separates the identified variables into faulty variables related to Y and unrelated to Y more clearly than conventional method. A case study on Tennessee Eastman process (TEP) indicates the efficiency of the proposed approach.

Multivariate statistical process monitoring (MSPM) is an efficient data-driven fault detection and diagnosis approach for complex industrial processes. Partial least squares or projection to latent structures (PLS) is one of the latent projection structures used in MSPM, which uses process data X and quality data Y together. In this paper, we discuss a new fault diagnosis approach based on total projection to latent structures (T-PLS). Four kinds of monitoring statistics are used in T-PLS, and a new definition of variable contributions to T 2 of PLS ​​is proposed. Then, definitions of variable contributions to all statistics are derived to identify the faults. Control limits for contribution plots are calculated to identify whether a variable is in abnormal situation or not. Further, the proposed method separates the identified variables into faulty variables related to Y and unrelated to Y more clearly than conventional methods. A case study on Tennessee Eastman process (TEP) indicates t he efficiency of the proposed approach.