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0000-0002-5196-3451 Nonparametric statistical methods are gaining importance in industrial process monitoring due to their robustness to the underlying distribution of the data, a common situation when dealing with real industrial processes. Control charts are regularly used to monitor the behavior of a system over time, often assuming a normal distribution, thus, the exactness of results obtained relies on the truthfulness of given assumptions. Nonparametric solutions based on permutations are limited to deal with small samples due to the computational complexity. Approaches based on rank transformations have shown relatively great power, however, their use in the analysis of series, such as control chart monitoring, involves re-ranking calculations that might become too complex when facing large data flows. This can be avoided by restricting the incorporation of new data into the analysis at the expense of losing power. Sequential rank transformations have shown attractive properties in terms of power and computational complexity, and the normal scores variant has reduced the analytical complexity extending its applicability by adapting parametric approaches that assume normality. This thesis proposes the use of sequential normal scores (SNS) for industrial process monitoring and compares its performance over a wide variety of practical situations and other nonparametric alternatives. The performance showed robustness over different distributions, in terms of the Empirical Alarm Probability (EAP), and an increase in power as new observations were incorporated in the analysis. Master of science in Manufacturing Systems

Since their introduction in 1954, cumulative sum (CUSUM) control charts have seen a widespread use beyond the conventional realm of statistical process control (SPC). While off-the-shelf implementations aimed at practitioners are available, their successful use is often hampered by inherent limitations which make them not easily reconcilable with real-world scenarios. Challenges commonly arise regarding a lack of robustness due to underlying parametric assumptions or requiring the availability of large representative training datasets. We evaluate an adaptive distribution-free CUSUM based on sequential ranks which is self-starting and provide detailed pseudo-code of a simple, yet effective calibration algorithm. The main contribution of this paper is in providing a set of ready-to-use tables of control limits suitable to a wide variety of applications where a departure from the underlying sampling distribution to a stochastically larger distribution is of interest. Performance of the proposed tabularized control limits is assessed and compared to competing approaches through extensive simulation experiments. The proposed control limits are shown to yield significantly increased agility (reduced detection delay) while maintaining good overall robustness.