Data modeling for machine learning-based fault detection and prediction in building life support systems

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Analysis reveals a synthetic dataset improves fault detection in building life support systems, suggesting more reliable monitoring systems.

The proposed method effectively simulates operating conditions for fault detection in building life support systems, improving reliability.
A synthetic dataset was created, comprising temperature, pressure, humidity, CO2, and energy consumption metrics, simulating real-world scenarios.
Statistical analysis and preprocessing, including outlier processing and class balancing, ensured data quality for machine learning training.
The study emphasizes the synthetic dataset's role in developing intelligent monitoring systems for building safety and efficiency.

Dahe et al. (2025) studied this question.

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View Full Paper

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Analysis reveals a synthetic dataset improves fault detection in building life support systems, suggesting more reliable monitoring systems.

The proposed method effectively simulates operating conditions for fault detection in building life support systems, improving reliability.
A synthetic dataset was created, comprising temperature, pressure, humidity, CO2, and energy consumption metrics, simulating real-world scenarios.
Statistical analysis and preprocessing, including outlier processing and class balancing, ensured data quality for machine learning training.
The study emphasizes the synthetic dataset's role in developing intelligent monitoring systems for building safety and efficiency.

Dahe et al. (2025) studied this question.

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