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- W2022278625 abstract "Industrial and measurement fault events are often connected with sudden changes (jumps) in some measured value. In this paper new continuous and discrete time mathematical models are proposed for chemical measurement error and noise processes which are suited to describe jumps, as well as drift and white noise components in the form of stochastic differential equations. The most simple discrete time stationary model has an autoregressive form with non-normally (mixed) distributed white noise sequence, therefore the most important problems connected with jump detection can be simply formulated and solved. The properties of the proposed models and algorithms are demonstrated on real industrial noise processes obtained from recording of strip columns in a great petrochemical plant. The proposed models and algorithms are of importance in case of intelligent automated control and monitoring system for fault detection." @default.
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- W2022278625 date "1984-01-01" @default.
- W2022278625 modified "2023-09-27" @default.
- W2022278625 title "Chemical noise and measurement error processes. Continuous and discrete time models" @default.
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- W2022278625 doi "https://doi.org/10.1016/0009-2509(84)85083-6" @default.
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