Data Mining Methodologies for Supporting Engineers during System Identification

After four years of work, I am now close to finishing my PhD. Below is the abstract of my thesis which is about data mining for system identification support:

Data alone are worth almost nothing. While data collection is increasing exponentially worldwide, a clear distinction between retrieving data and obtaining knowledge has to be made. Data are retrieved while measuring phenomena or gathering facts. Knowledge refers to data patterns and trends that are useful for decision making. Data interpretation creates a challenge that is particularly present in system identification, where thousands of models explain a given set of measurements. Manually interpreting such data is not reliable by hand. One solution is to use data mining. This thesis thus proposes an integration of techniques from data mining, a field of research where the aim is to find knowledge from data, into an existing multiple-model system identification methodology.

It is shown that, within a framework for decision support, data mining techniques constitute a valuable tool for engineers performing system identification. For example, clustering techniques group similar models together in order to guide subsequent decisions since they might indicate different possible states of a structure. A main issue concerns the number of clusters, which, usually, is unknown.

For determining the correct number of clusters in data and estimating the quality of a clustering algorithm, a score function is proposed. The score function is a reliable index for estimating the number of clusters in a given data set, thus increasing clustering results understanding for engineers. Furthermore, useful information for engineers performing system identification is achieved through the use of feature selection techniques. They allow selection of relevant parameters that explain candidate models. The core algorithm is a feature selection strategy based on global search.

In addition to providing information about the candidate model space, data mining is found to be a valuable tool for supporting decisions related to subsequent sensor placement. When integrated in a methodology for iterative sensor placement, clustering is found to provide useful support through providing a rational basis for subsequent sensor placement on existing structures. Regarding initial sensor placement, greedy and global search strategies should be selected according to the context. Experiments show that whereas global search is more efficient for initial sensor placement, a greedy strategy is more suitable for iterative sensor placement.

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