Detection of Abrupt Changes - Theory and Application

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Keywords

• Changes in the parameter vector of a random process, detection, statistical methods, on-line algorithms, performance analysis.
• Automatic segmentation of signals, on-line detection of transient events.
• Fault or damage detection, dynamical systems, sensor faults, actuator faults, process faults, multidimensional signal processing.

Applications

• Monitoring and condition-based maintenance of industrial plants.
• Monitoring complex measurement systems.
• Signal processing for pattern recognition (geophysics, seismology, vibrations, speech processing, biomedicine, acoustics...).

Summary

Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established.
We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.

Readership

Engineers and researchers in the fields of signal processing, automatic control and supervision, time series analysis, applied statistics, quality control.

Citations and Reviews

• Google Scholar

• MathSciNet

        Reviews

• K.S. Riedel, Technometrics, vol.36, no 3, 1994, pp.326-327.

• I.J. Rudas, Control Engineering Practice, vol.2, no 4, Aug. 1994, pp.729-730.

• N. Peach, Jal Royal Statistical Society A, vol.158, Part 1, 1995, pp.185-186.

• R.A. Khan, MathSciNet: Mathematical Reviews on the Web, American Mathematical Society, 1995.

• H.V. Poor, Automatica, vol.32, no 8, Aug. 1996, pp.1235-1236.

• F. Kerestecioglu, Int. Jal Adaptive Control and Signal Processing, vol.12, no 4, June 1998, pp.386-387.

• T.D. Popescu, Studies in Informatics and Control, vol.9, no 2, June 2000, pp.155-157.

• A.J. Duben, Computing Reviews, May 1995, p.232, The Association for Computing Machinery. See also ACM Portal: The Guide to Computing Literature.

The book in 5 files

• File 1 (44 pages) - Contents, Preface, Introduction: pdf (321k) or ps.gz (197k).

• File 2 (172 pages) - Part I (chapters 2-5): pdf (1028k) or ps.gz (457k).

• File 3 (170 pages) - Part II (chapters 6-9): pdf (991k) or ps.gz (449k).

• File 4 (60 pages) - Part III (chapters 10-11): pdf (844k) or ps.gz (632k).

• File 5 (23 pages) - Bibliography, Index: pdf (126k) or ps.gz (49k).

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