By Steven X. Ding
Data-driven layout of Fault analysis and Fault-tolerant keep watch over structures offers simple statistical procedure tracking, fault prognosis, and keep an eye on tools and introduces complicated data-driven schemes for the layout of fault analysis and fault-tolerant regulate structures catering to the desires of dynamic business techniques. With ever expanding calls for for reliability, availability and security in technical tactics and resources, technique tracking and fault-tolerance became vital matters surrounding the layout of computerized regulate platforms. this article indicates the reader how, due to the speedy improvement of data expertise, key thoughts of data-driven and statistical approach tracking and keep an eye on can now develop into ordinary in business perform to deal with those matters. to permit for self-contained learn and facilitate implementation in genuine purposes, very important mathematical and regulate theoretical wisdom and instruments are incorporated during this booklet. significant schemes are awarded in set of rules shape and confirmed on commercial case platforms. Data-driven layout of Fault prognosis and Fault-tolerant keep watch over platforms can be of curiosity to technique and keep watch over engineers, engineering scholars and researchers with a keep an eye on engineering background.
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Extra info for Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems
Note that ⎣ ⎡ ⎣ ⎡ ¯ > Jth = 2 pr ob y¯ > Jth . 2 Some Elementary Methods and Algorithms 27 As a result, the following algorithm can be used for the computation of the threshold. 12) S2: Set Jth θ Jth = z (1−Δ)/2 √ . 9). T 2 test statistic is defined by ⎦ JT 2 = n y¯ T Σ −1 y¯ . 15) where χ 2 (m) is the chi-squared distribution with m degrees of freedom. T 2 test statistic is also called Hotelling’s T 2 test statistic, as it was first proposed by Hotelling in 1931. For applying the T 2 test statistic to FD-P1, the threshold computation can be done using the following algorithm.
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.
1007/978-1-4471-6410-4_4, © Springer-Verlag London 2014 49 50 4 Fault Detection in Processes with Deterministic Disturbances The fault detection problem we now address is to detect f ◦= 0 by means of a number of available measurement data of y, y(1), . . , y(n). 3) i=0 and set the threshold Jth = max ⇒d⇒22,[k,k+n] = δd2 . 4) k As a result, the detection logic is ⎦ J (k) ∈ Jth =≤ fault-free (H0 ) . 2 Some Essential Concepts Comparing with the fault detection schemes presented in the last chapter, similarities and differences can be recognized in the essential steps of the above solution.
Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems by Steven X. Ding