Download e-book for kindle: Artificial Neural Networks for the Modelling and Fault by Krzysztof Patan

By Krzysztof Patan

ISBN-10: 3540798714

ISBN-13: 9783540798712

An unappealing attribute of all real-world platforms is the truth that they're liable to faults, malfunctions and, extra commonly, unforeseen modes of - haviour. This explains why there's a non-stop desire for trustworthy and common tracking structures according to compatible and e?ective fault prognosis recommendations. this is often very true for engineering systems,whose complexity is completely becoming a result of inevitable improvement of recent in addition to the data and communique know-how revolution. certainly, the layout and operation of engineering platforms require an elevated consciousness with appreciate to availability, reliability, protection and fault tolerance. therefore, it truly is normal that fault prognosis performs a basic function in glossy keep watch over idea and perform. this is often re?ected in lots of papers on fault analysis in lots of control-oriented c- ferencesand journals.Indeed, a largeamount of knowledgeon version basedfault analysis has been accrued via scienti?c literature because the starting of the Nineteen Seventies. consequently, a large spectrum of fault prognosis concepts were built. an incredible type of fault prognosis innovations is the version established one, the place an analytical version of the plant to be monitored is thought to be available.

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2 Models Used in Fault Diagnosis 13 If the structure of the process as well as the parameters are known, the process model is represented by BM (s) . 5) GM (s) = AM (s) Assume that fu (t) and fy (t) are additive faults acting on the input and output, respectively. If GP (s) = GM (s), the output error has the form e (s) = y(s) − GM u(s) = GP (s)fu (s) + fy (s). 6) Faults that influence the input or output of the process result in changes of the residual e (t) with different transients. The polynomials of GM (s) can also be used to form a polynomial error : e(s) = AM (s)y(s) − BM (s)u(s) = Ap (s)fy (s) + Bp (s)fu (s).

N is the set of inputs to the neuron, Gi (z −1 ) is the linear transfer function, bj , j = 0, 1, . . , r, and aj , j = 0, 1, . . 5 Locally Recurrent Networks u2 (k) un (k) w1 w2 ϕ(k) + ... u1 (k) 39 σ(·) y(k) wn d1 . . d2 dr z−1 ... z−1 z−1 Fig. 7. Neuron architecture with local activation feedback respectively. 11b), the linear transfer function has r zeros and p poles. Note that the inputs ui (k), i = 1, 2, . . , n may be taken from the outputs of the previous layer, or from the output of the neuron.

10. Fault diagnosis as pattern recognition ton tf rom Decision making false decisions 1 0 ❄❄ thor time true decisions ☛ ✁☛ ❄ ✁ ❄ tdt time Fig. 11. 11 illustates the benchmark zone definition. Decisions before the benchmark start-up ton and after the benchmark time horizon thor are out of interest. The time of the fault start-up is represented by tf rom . When a fault occurs in the system, a residual should deviate from the level assigned to the fault-free case (Fig. 11). The quality of the fault detection system can be evaluated using a number of performance indices [52, 26]: 26 2 Modelling Issue in Fault Diagnosis • Time of fault detection tdt – period of time needed for the detection of a fault measured from tf rom to a permanent, true decision about a fault, as presented in Fig.

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Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes by Krzysztof Patan

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