{'fraction_non_alphanumeric': 0.018018018018018018, 'fraction_numerical': 0.0, 'mean_word_length': 5.1192660550458715, '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}}
Graphical models for diagnosis knowledge representation and inference

One popular approach for fault diagnosis is based on reasoning about the behavior of a system in failure space. Diagnosis is performed by considering a set of observations (or symptoms) and by explaining it in terms of a set of root causes. There are many modeling methods to capture the system's faulty behavior, such as behavioral Petri nets, multi-signal flow graphs, and Bayesian networks. In this paper, we will investigate the equivalence of these three modeling formalism by way of application to a car engine diagnosis problem, and discuss the advantages and disadvantages of each method