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- W4307019698 abstract "Fault detection and diagnosis (FDD) are crucial for safe operation of process systems. Multivariate approaches have been widely employed in process FDD due to the highly correlated nature of data resulting from the complexity of process systems. The principal component analysis and the partial least squares are among the frequently used multivariate methods; the ability to handle highly correlated data without much preprocessing is a key benefit of these methods. In the recent development, intelligent FDD is a term that refers to the application of machine learning (ML) models, such as artificial neural networks, support vector machines (SVMs), and deep neural networks, to process plant fault diagnosis. This chapter discusses the two most commonly used supervised ML methods for FDD, namely the neural network and the SVMs. ML methods are becoming ubiquitous; like other fields of applications, process safety in general, and FDD in particular, are benefiting from the use of the ML tools." @default.
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- W4307019698 date "2022-10-21" @default.
- W4307019698 modified "2023-09-23" @default.
- W4307019698 title "Machine Learning for Process Fault Detection and Diagnosis" @default.
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- W4307019698 doi "https://doi.org/10.1002/9781119817512.ch6" @default.
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