| dc.description.abstract | Exploring the trend of events on a multivariate time series and digging up the connection between the trends, it is possible for the engineer to find other events that could be concerned when a new anomaly occurs, and then adjust machine, for example, we can know that when the tension of the machine drops sharply, it often follows a sharp increase in the speed of wheel. We hope that there are more adjustment method can be used through the idea we proposed.
However, the traditional association rules mainly deal with category data, such as transaction data of stores, and the order of the data is ignored. Recently, it often to find the Piecewise Aggregate Approximation (PAA) and Symbolic Aggregate Approximation (SAX) is applied to engineering numerical data, which uses PAA to reduce the dimensionality of the data to reduce the huge process time required in the calculation of association rules, and then uses SAX to symbolize the data to present the transaction set data required by the association rules. Unlike the data types in traditional association rules, the order of occurrence of the data values often represents a certain meaning on multivariate time series data, and it may be related between previous one and the next one. If the dimension is reduced, it may be diluted or even ignored the characteristics of the data itself.
We hope to achieve the idea to distinguish which the event of A = (a, b) and B = (b, a) are different, and also distinguish the difference between data fields in our research. Different transaction set data was established to facilitate the association rules about the field differences on the data set in our research. The analysis based on our method allows managers to not only find out the field trends that affect each other, but also use the method of our research to make the engineer know more about the connection between the machine columns and adjust the machine for proper maintenance work or other decisions. | en_US |