A novel use of multivariate statistics to diagnose test-to-test variation in complex measurement systems

Abstract Vehicle testing is critical to demonstrating the cost-benefits of new technologies that will reduce fuel consumption, CO 2 and toxic emissions. However, vehicle testing is also costly, time consuming and it is vital that these are conducted efficiently and that the information they yield is maximised. Vehicles are complex systems, but it is straightforward to install intensive instrumentation to record many data channels. Due to costs, relatively few repeated test cycles are conducted. Identifying correlations within these datasets is challenging and requires expert input who ultimately focus on small subsets of the original data. In this paper, a novel application of Partial Least Squares (PLS) regression is used to explore the complete data set, without the need for data exclusion. Two approaches are used, the first collapses the data set and analyses all data channels without time variations, while the second unfolds the data set to avoid any information loss. The technique allows for the systematic analysis of large datasets in a very time efficient way meaning more information can be obtained about a testing campaign. The methodology is used successfully to identify sources of imprecision in four different case studies to analyse sources of imprecision in vehicle testing on a chassis dynamometer. These findings will lead to significant improvements in vehicle testing, allowing both substantial savings in testing effort and increased likelihood confidence in demonstrating the cost-benefit of new products. The measurement analysis technique can also be applied to other fields where repeated testing or batch processes are conducted.