{'fraction_non_alphanumeric': 0.02103049421661409, 'fraction_numerical': 0.0, 'mean_word_length': 6.212121212121212, '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}}
Proportional hazard modeling for hierarchical systems with multi-level information aggregation

Reliability modeling of hierarchical systems is crucial for their health management in many mission-critical industries. Conventional statistical modeling methodologies are constrained by the limited availability of reliability test data, especially when the system-level reliability tests of such systems are expensive and/or time-consuming. This article presents a semi-parametric approach to modeling system-level reliability by systematically and explicitly aggregating lower-level information of system elements; i.e., components and/or subsystems. An innovative Bayesian inference framework is proposed to implement information aggregation based on the known multi-level structure of hierarchical systems and interaction relationships among their composing elements. Numerical case study results demonstrate the effectiveness of the proposed method.