Detection of Abrupt Changes Theory and Application by Michele Basseville, Igor V. Nikiforov

By Michele Basseville, Igor V. Nikiforov

This e-book offers mathematical instruments and methods for fixing swap detection difficulties in vast domain names like sign processing, managed platforms and tracking. The e-book covers a large type of stochastic methods, from scalar self sustaining observations to multidimensional based ARMA and state-space types, the houses of the algorithms for statistical swap detection, tuning and optimizing switch detection in real-world purposes. The e-book is meant for engineers and researchers considering sign processing, computerized keep an eye on and supervision, time sequence research, utilized records, qc and preventive upkeep and tracking of crops.

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Part I is devoted to changes in the scalar parameter of an independent sequence. In chapter 2, we introduce the reader to the theory of on-line change detection algorithms in the framework of an independent random sequence parameterized by a scalar parameter. We first consider the case of known parameters before and after change. 1, we begin with the description of elementary algorithms of common use in industrial applications (quality control, for example) : these are Shewhart control charts, finite or infinite moving average control charts, and filtered derivative algorithms.

Useful results can be obtained with the aid of parametric models of relatively small size with respect to the dimension of the physical model of the process (which is based on partial differential equations, for example) . Even though it is often believed that parametric techniques are useful for diagnosis purposes only when there is a bijection between the parametric model and the physical one (see the survey [Isermann, 1984], for example), diagnosis in terms of the physical model can be inferred from a small black-box parametric model.

The main reason for this is to allow S-wave detection in this online processing. P -wave detection is a difficult problem, because the data contain many nuisance signals coming from the environment of the seismic station, and discriminating between these events and a true P -wave is not easy. The same situation holds for the S-wave, where the difficulty is greater, because of low signal-to-noise ratio and numerous nuisance signals between P -wave and S-wave. After P -wave and S-wave detection, off-line accurate estimation of onset times is requested for both types of waves.

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