Download Machine Learning for Cyber Physical Systems: Selected papers by Oliver Niggemann, Jürgen Beyerer PDF

By Oliver Niggemann, Jürgen Beyerer

The paintings provides new methods to computer studying for Cyber actual platforms, stories and visions. It includes a few chosen papersfrom the overseas convention ML4CPS – laptop studying for Cyber actual platforms, which was once held in Lemgo, October 1-2, 2015.

Cyber actual platforms are characterised by way of their skill to evolve and to profit: They research their surroundings and, in keeping with observations, they study styles, correlations and predictive types. common purposes are tracking, predictive upkeep, snapshot processing and prognosis. computer studying is the most important expertise for those developments.

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Extra info for Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2015

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Dimensionality reduction leads to a compact representation of system behavior. In the next step, patterns are extracted, which represent the normal behavior of the system. Fig. 3 shows the data of a wind power station. The area of normal operations is depicted 48 Evaluation of Model-Based Condition Monitoring Systems Fig. 3. Example of dimensionality reduced cluster data. with green points, while known errors are shown as red points. The interference is small so that the green cluster can be used as model of the normal plant operation.

1 Evaluation in industrial application cases Wind power station PCA-based condition monitoring has been initially evaluated with a data set of a wind power station, which contains data collected in a time span of four years in intervals of 10 minutes (see [13]). Evaluation has been conducted for a time span of 80 days. The remaining data has been used for model learning. In the evaluation phase, 11544 observations in 7013 normal states and 4531 failure states have been analyzed. Condition monitoring results are displayed in table 1.

If a faulty state of operation is reached changes of the amplitudes within the spectrum can be identified. They can be detected with acceleration sensors (measuring different dimensions) mounted to the take-off unit. As determined before the great challenge is to find an intelligent integrative representation of the different conditions (high-dimensional vectors) since many different spectra in changing states of operation (mainly cycle times) have to be included (see Fig. 4). It was decided to choose the approach of machine learning to satisfy these requirements.

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