Data Mining and Knowledge Discovery for Process Monitoring by Xue Z. Wang BEng, MSc, PhD (auth.)

By Xue Z. Wang BEng, MSc, PhD (auth.)

Modern computer-based keep watch over structures may be able to gather a large number of info, demonstrate it to operators and shop it in databases however the interpretation of the knowledge and the next choice making is based as a rule on operators with little laptop help. This ebook introduces advancements in computerized research and interpretation of process-operational information either in real-time and over the operational historical past, and describes new thoughts and methodologies for constructing clever, state-space-based platforms for approach tracking, keep an eye on and diagnosis.

The ebook brings jointly new equipment and algorithms from procedure tracking and regulate, facts mining and information discovery, synthetic intelligence, development attractiveness, and causal courting discovery, in addition to sign processing. It additionally presents a framework for integrating plant operators and supervisors into the layout of method tracking and keep an eye on systems.

The subject matters coated include

• a clean examine present platforms for method tracking, keep an eye on and diagnosis

• a framework for constructing clever, state-space-based systems

• a evaluation of knowledge mining and information discovery

• facts preprocessing for function extraction, measurement aid, noise removing and inspiration formation

• multivariate statistical research for strategy tracking and control

• supervised and unsupervised tools for operational kingdom identification

• variable causal courting discovery in graphical versions and creation rules

• software program sensor design

• old facts analysis

Data Mining and data Discovery for approach tracking and Control is necessary examining for researchers and graduate scholars in technique keep an eye on and information and data engineering. keep an eye on and strategy engineers must also locate this publication of value.

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Extra info for Data Mining and Knowledge Discovery for Process Monitoring and Control

Sample text

9 The windowing operation. 2 Signal Transformation Using Wavelets Wavelet transfonuation is designed to address the problem of non-stationary signals [99, 100, 101]. It involves representing a time function in tenus of simple, fIxed building blocks, tenued wavelets. 8(b)). Dilation, also known as scaling, compresses or stretches the mother wavelet, and translation shifts it along the time axis. 10. 11: the smaller the scale factor, the more compressed the wavelet is. 10 Scaling ofa sinusoid function.

If there are dependency on the data patterns, it is necessary to remove or explore. (6) availability of prior knowledge. Some tools require prior knowledge which might be not available. On the other hand, some others do not allow input of prior knowledge, causing a waste of prior knowledge. It is important to be aware of the complexity of data which tends to contain noise and erroneous components and has missing values. Other challanges come from lack of understanding of the domain problem and assumptions associated with individual techniques.

Ad atabase examp e. l Take probabilistic networks as an example. 2 shows a collection of 10 data patterns, each is described by three attributes. The task of dependency modelling using probabilistic networks is two-fold - learning the network structure and compiling a conditional probabilistic table. 2 Chapter 2 Data Mining and Knowledge Discovery - an Overview 21 it is not possible to know the most probable dependencies directly. 5 (a) and (b) illustrate two such possible dependencies. Theoretically, for a given database there is a unique structure which has the highest joint probability and can be found by some algorithm such as those developed by Cooper and Herskovits [54] and Bouckaert [55, 56].

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