Fault Detection and Isolation with Robust Principal Component Analysis
نویسندگان
چکیده
منابع مشابه
Sensor fault detection and isolation by robust principal component analysis
Sensors are essential components of modern control systems. Any faults in sensors will affect the overall performance of a system because their effects can easily propagate to manipulative variables through feedback control loops and also disturb other process variables. The task for sensor validation is to detect and isolate faulty sensors and estimate fault magnitudes afterwards to provide fa...
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Diagnosis method based on Principal Component Analysis (PCA) has been widely developed. However, this method deals only with data which are described by single-valued variables. The purpose of the present paper is to generalize the diagnosis method to interval PCA. The fault detection is performed using the new indicator [SPE]. To identify the faulty variables, this work proposes a new method b...
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Contribution plots of the monitored statistics, Q and T, are investigated to locate faulty variables when the statistics are out of their control limits. It is a popular method for fault isolation; however, it is well known that the smearing out of contributions leads to misdiagnose the faulty variables. Alternatively, the reconstruction-based contribution approach is claimed to guarantee corre...
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ژورنال
عنوان ژورنال: International Journal of Applied Mathematics and Computer Science
سال: 2008
ISSN: 1641-876X
DOI: 10.2478/v10006-008-0038-3