Download Data-driven Design of Fault Diagnosis and Fault-tolerant by Steven X. Ding PDF

By Steven X. Ding

Data-driven layout of Fault analysis and Fault-tolerant keep watch over structures provides uncomplicated statistical technique tracking, fault prognosis, and keep watch over equipment and introduces complicated data-driven schemes for the layout of fault prognosis and fault-tolerant keep an eye on platforms catering to the wishes of dynamic business strategies. With ever expanding calls for for reliability, availability and security in technical procedures and resources, approach tracking and fault-tolerance became vital concerns surrounding the layout of automated regulate structures. this article exhibits the reader how, because of the swift improvement of knowledge expertise, key ideas of data-driven and statistical procedure tracking and keep watch over can now develop into regular in commercial perform to handle those matters. to permit for self-contained learn and facilitate implementation in genuine functions, very important mathematical and keep an eye on theoretical wisdom and instruments are incorporated during this ebook. significant schemes are provided in set of rules shape and validated on business case structures. Data-driven layout of Fault analysis and Fault-tolerant regulate structures should be of curiosity to procedure and keep an eye on engineers, engineering scholars and researchers with a keep an eye on engineering background.

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Extra resources for Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems

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1 Parameters of DTS200 13 Parameters Symbol Value Unit Cross section area of tanks Cross section area of pipes Max. height of tanks Max. flow rate of pump 1 Max. flow rate of pump 2 Coeff. of flow for pipe 1 Coeff. of flow for pipe 2 Coeff. 45 cm2 cm2 cm cm3 /s cm3 /s In the steady state at the operating point, it holds ⎡ ⎤ h 1,o Q 1,o A ⎣ h 2,o ⎦ + B Q 2,o h 3,o = 0 ⇐⇒ A B ho Qo = 0. 2) It is remarkable that in practice fault diagnosis is often realized as the process runs in the steady state.

References 1. AMIRA (1996) Three-tank-system DTS200, Practical Instructions. AMIRA, Duisburg 2. GUNT (2011) Experiment Instructions RT682. T, Gerätebau GmbH, Germany 3. Thornhill N, Patwardhan S, Shah S (2008) A continous stirred tank heater simulation model with applications. J Process Control 18:347–360 4. Downs J, Fogel E (1993) A plant-wide industrial process control problem. Comput Chem Eng 17:245–255 5. Chiang LH, Russell EL, Braatz RD (2001) Fault detection and diagnosis in industrial systems.

If the FAR is considered as the detection performance, our original fault detection problem can be formulated as: given an acceptable FAR Δ, find the threshold Jth so that ⎣ ⎡ ¯ > Jth | f = 0 = Δ. 4) Alternatively, we can, for instance, also set the threshold Jth to ensure that a fault with a size larger than some value can be detected with a high probability. In that case, the computation of the FAR = prob{|y| ¯ > Jth | f = 0} is the major task. 2 Elementary Concepts in Fault Detection As a summary and generalization of the above discussion, we now re-study the addressed fault detection problem in the statistical framework of hypothesis testing, and will, in this context, introduce some elementary concepts.

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