Data-driven Methods for Fault Detection and Diagnosis in by Evan L. Russell PhD, Leo H. Chiang MS, Richard D. Braatz PhD

By Evan L. Russell PhD, Leo H. Chiang MS, Richard D. Braatz PhD (auth.)

Early and exact fault detection and prognosis for contemporary chemical vegetation can minimise downtime, raise the security of plant operations, and decrease production bills. The process-monitoring innovations which have been leading in perform are in accordance with types developed nearly solely from method information. The target of the booklet is to give the theoretical historical past and useful concepts for data-driven method tracking. Process-monitoring concepts provided contain: valuable part research; Fisher discriminant research; Partial least squares; Canonical variate analysis.
The textual content demonstrates the applying of the entire data-driven approach tracking concepts to the Tennessee Eastman plant simulator - demonstrating the strengths and weaknesses of every strategy intimately. This aids the reader in choosing the right technique for his method program. Plant simulator and homework difficulties during which scholars observe the process-monitoring ideas to a nontrivial simulated approach, and will examine their functionality with that received within the case reviews within the textual content are integrated. a few extra homework difficulties motivate the reader to enforce and procure a deeper realizing of the techniques.
The reader will receive a history in data-driven strategies for fault detection and analysis, together with the facility to enforce the recommendations and to grasp tips to choose the perfect process for a specific application.

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Extra info for Data-driven Methods for Fault Detection and Diagnosis in Chemical Processes

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Suggest reasons why these ideas took so long to work their way into industrial process applications. 2. Write a short report on the lower control limits for the T2 statistic discussed by [209]. For what type of chemical processes and faults will such limits be useful? ). Suggest reasons why most of the chemical process control and statistics literature ignores the lower control limit. Justify your statements. 3. Write a short report on the single variable CUSUM and EWMA control charts, including the mathematical expressions for the upper control limits in terms of a distribution function and assumptions on the noise statistics.

The method for automatically determining h described in [125] is not used here. Experience indicates that h = 1 or 2 is usually appropriate when DPCA is used for process monitoring. The fault detection and diagnosis measures for static PCA generalize directly to DPCA. For fault identification, the measures for each observation variable can be calculated by summing the values of the measures corresponding to the previous h lags. 8 Other PCA-based Methods 51 process (113). 44) can result in the PCA representation correlating more information.

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