DOI: 10.3724/SP.J.1004.2009.00759

Acta Automatica Sinica (自动化学报) 2009/35:6 PP.759-765

Total PLS Based Contribution Plots for Fault Diagnosis

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.

Key words:Data-driven, total projection to latent structures (T-PLS), contribution plots, fault diagnosis

ReleaseDate:2014-07-21 14:42:11

Funds:Supported by National Basic Research Program of China (973 Program)(2009CB320602), National Natural Science Foundation of China(60721003, 60736026), and Changjiang Professorship by Ministry of Education of P.R. China

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