{'fraction_non_alphanumeric': 0.021148036253776436, 'fraction_numerical': 0.0, 'mean_word_length': 5.63, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A framework for unsupervised fault detection and diagnosis based on clustering assisted kriging observer

This paper presents an unsupervised data-driven method for Fault Detection and Diagnosis (FDD) of nonlinear dynamic processes. The proposed approach is based on the combination of automatic and non-automatic clustering techniques with a data-driven observer based on Multivariate Dynamic Kriging (MDK) metamodels. The proposed framework is studied via its application to a well-known benchmark simulation case study based on the control of a three-tanks system, showing promising performance in terms of accuracy, robustness and simplicity of applications.