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In this subsection, PC1DARMF algorithm is employed to classify stochastic fault types.
A fault detection and identification algorithm, called optimal stochastic fault detection filter, is determined.
With the same steps in Section 4.2, Tables 9, 10, 11 and 12 list corresponding statistics for stochastic fault classification directly for saving details of derivations.
The nonlinear system is represented as a sequence of linear time-varying approximations and at each linear approximation the design of an optimal stochastic fault detection filter for linear time-varying systems is applied.
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The performances of stochastic faults classification are poorer than performances of deterministic faults.
Table 13 tabulates comparisons between PC1DARMF algorithm and MSBA [17] for classifying stochastic faults in TEP.
Deterministic faults diagnosis fares better than the stochastic faults diagnosis, since deterministic ones are apt to form similar or regular trends regardless of specified faulty conditions and noise levels while stochastic ones fail to retain basic morphologies for signal patterns.
Considering the performances of conventional Kalman filter may seriously degrade when it suffers stochastic faults and unknown input, which is very common in engineering problems, a new type of adaptive three-stage extended Kalman filter (AThSEKF) is proposed to solve state and fault estimation in nonlinear discrete-time system under these conditions.
Consensus theory is employed for fusion of results provided by different sources The results show with only small quantity of training data provided and the testing data being much more different from training data, the performances of deterministic or stochastic faults diagnosis is better than or equally well as multivariate statistics based approaches studied in [17].
The ground motion at EBL is estimated using Stochastic Finite Fault source Modeling Technique by considering scenario earthquakes along two major faults of Kachchh (Kachchh Mainland Fault (Mw7.6) and Katrol Hill Fault (Mw7.5)), one of Saurashtra (North Kathiawar Fault (Mw 6.5)) and one of Mainland Gujarat (West Cambay Fault (Mw 6.0)).
The basic idea of the stochastic finite-fault model is that the fault plane can be subdivided into several subfaults (or elements, sources).
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