• Nikita L. Kamyshev, Institute of Computational Modelling SB RAS (Krasnoyarsk, Russia)
• Olga S. Isaeva, Institute of Computational Modelling SB RAS (Krasnoyarsk, Russia)

This paper presents a solution for creating an autonomous microclimate monitoring system for technical facilities, capable of promptly detecting anomalies under conditions of non-stationary data and limited sensor infrastructure. The research aim is to develop monitoring tools that ensure continuous control, anomaly detection, and their interpretation. The core content of the work includes the development of a functional system model, the implementation of telemetry collection and heterogeneous data storage modules, and the deployment of an original hybrid algorithm for time series analysis. The key element is the proposed method, which combines spectral analysis to extract periodic signal components with adaptive statistical thresholds dynamically calculated based on sliding historical windows. Unlike existing counterparts relying on resource-intensive deep learning, the proposed approach utilizes physically grounded features to adapt to seasonal data variability without a preliminary training stage on labeled datasets. To unify the anomaly detection approach and alert routing, specific alert rules were configured, integration with a data visualization system was performed, and a chatbot was created to handle notifications and queries. To enhance fault tolerance, a component liveness control mechanism was implemented. The scientific novelty lies in proposing a hybrid adaptive anomaly detection method based on spectral-statistical time series analysis. The proposed approach ensures result interpretability by revealing the physical nature of failures through signal decomposition into harmonic components. Verification results on a real dataset of 5.3 million records confirmed the approach's effectiveness: detection recall reached 0.991 with a precision of 0.877. Load testing demonstrated the architecture's stability under low CPU utilization (<6%) and indicated reserves for scalability. The project successfully identified incidents related to air conditioning system malfunctions. The developed system provides a full cycle from data collection to preventive alerting, offering understandable diagnostic tools for preventing emergency situations while supporting two levels of interaction: a simplified interface for end-users and advanced analytical functions for specialists.

Internet of Things (IoT), microclimate monitoring, anomaly detection, spectral analysis, adaptive thresholds, fault tolerance, Prometheus, AlertManager

2026-06-05