ИСПОЛЬЗОВАНИЕ АЛГОРИТМОВ МАШИННОГО ОБУЧЕНИЯ ДЛЯ ОБНАРУЖЕНИЯ И ДИАГНОСТИКИ НЕИСПРАВНОСТЕЙ В СИСТЕМАХ ЖИЗНЕОБЕСПЕЧЕНИЯ

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The increasing interest in integrating machine learning methods into life support systems management is driven by their potential to enhance error detection, diagnostics, and forecasting capabilities. This paper focuses on analyzing current research to identify the strengths and weaknesses of machine learning models in the field of fault detection and prediction in building life support systems. It aims to provide insights into addressing issues such as data quality, algorithmic complexity, and interpretability. A review of contemporary scientific articles in this area was conducted, analyzing innovations and the practical implementation of machine learning models. The primary goal was to determine the optimal models for various scenarios, considering data characteristics, system complexity, task requirements, and desired outcomes. The information analysis led to formulating recommendations for integrating machine learning methods into life support systems, including the use of clustering methods for unlabeled data, XGBoost for handling unbalanced labeled data, support vector machines (SVM) for binary classification tasks, and decision trees (DT) or random forests (RF) for multi-class classification. Additionally, artificial neural networks (ANN) can be used for processing nonlinear data distributions. These recommendations are practically significant for enhancing the performance and energy efficiency of life support systems through the effective use of machine learning technologies. By providing guidance on model selection and implementation strategies, this work contributes to researchers, practitioners, and industry representatives in fault detection and diagnostics within life support systems.