Statistical data analysis of industrial systems

The major challenge is to be able to identify suitable techniques for preprocessing data to make it usable for further analysis to be able to detect dependencies or trends in the data.<br><br>Firstly, a new data analytics framework is presented that ensures a more effective engagement between statistician and manufacturing staff.

It successfully identifies key preprocessing challenges and the necessity to transform the data for machine learning. A series of interactions are developed to enhance the understanding of data between the two groups. Visualisation tools are embedded within the framework that highlight relevant statistical information and allows a more efficient process for performing data analytics within industry. <br><br>For validation, data provided by Seagate on their disk drive production and three further datasets are used to validate and demonstrate the impact of the approach.<br><br>A new strategy for analysing incomplete data is introduced following the identification of missingness.