Godwin, J. and Matthews, P.C. (2014) 'Robust statistical methods for rapid data labelling.', in Data mining and analysis in the engineering field. , pp. 107-141.
Labelling of data is an expensive, labour-intensive, and time consuming process and, as such, results in vast quantities of data being unexploited when performing analysis through data mining. This chapter presents a new paradigm using robust multivariate statistical methods to encapsulate normal operational behaviour—not failure behaviour—to autonomously derive unsupervised classifier labels for previously collected data in a rapid, cost-effective manner. This enables traditional machine learning to take place on a much richer dataset. Two case studies are presented in the mechanical engineering domain, namely, a wind turbine gearbox and a rolling element bearing. A statistically sound and robust methodology is contributed, allowing for rapid labelling of data to enable traditional data mining techniques. Model development is detailed, along with a comparative evaluation of the metrics. Robust derivatives are presented and their superiority is shown. Example “R” code is given in the appendix, allowing readers to employ the techniques discussed. High levels of agreement between the derived statistical approaches and the underlying condition of the components can be found, showing the practical nature and benefit of this approach.
|Item Type:||Book chapter|
|Full text:||Publisher-imposed embargo |
(AM) Accepted Manuscript
File format - PDF (Copyright agreement prohibits open access to the full-text) (646Kb)
|Publisher Web site:||http://dx.doi.org/10.4018/978-1-4666-6086-1.ch007|
|Date accepted:||28 September 2015|
|Date deposited:||07 October 2015|
|Date of first online publication:||May 2014|
|Date first made open access:||No date available|
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