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Data-Driven Fault Detection in Building HVAC Systems

Listing Description

This innovation focuses on developing and implementing data-driven methodologies for automated fault detection in building HVAC systems. Leveraging insights from the research based strategies with machine learning techniques to identify malfunctions such as inefficient storage discharge or sensor inaccuracies promptly. The methodology facilitates cost-effective monitoring of heating and cooling systems under real-world operating conditions, utilizing sensor networks (e.g., LoRaWAN) in conjunction with cloud-based analytical infrastructures.

Offer Details

Specifications and Deliverables:

#Algorithm Development: Creation and deployment of a machine learning-based algorithm for fault classification in multivariate time series data.​

#Hybrid Modeling Approach: Integration of rule-based and machine learning models to enhance detection accuracy with limited data availability.​

#System Integration: Compatibility with existing monitoring infrastructures via LoRaWAN or similar interfaces.​

#Analytical Platform: Cloud-based data analysis platform offering customized fault diagnostics and reporting functionalities.​

#Customization: Tailoring to specific HVAC system types and operational conditions.​

#Pilot Implementation: Optional setup of a pilot project including sensor deployment, data acquisition, and an analytical dashboard.

Value Proposition:

By providing early detection of common HVAC system faults without the need for extensively calibrated physical models or large training datasets, this solution enhances operational reliability and energy efficiency. The incorporation of machine learning enables continuous optimization and adaptation to various system configurations, leading to reduced operational costs, minimized manual maintenance, and extended lifespan of technical components. This approach is particularly advantageous for solar thermal installations, thermal storage units, heat pumps, and hybrid systems in both residential and commercial buildings.

Input Required from Customer:

#System Description: Detailed information about the HVAC system to be monitored, including components, schematics, and existing sensor placements.​

#Fault Detection Objectives: Specification of target fault types for detection (e.g., energy losses, component failures, comfort deviations).​

#Data Access: Provision of historical and real-time data streams (e.g., temperatures, flow rates, operational states, weather conditions).​

#Operational Data: Access to performance data for model validation and training support.​

#Pilot Participation: Willingness to integrate and test the system within a pilot or test environment.

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Adress:

Hofackerstrasse 30
Muttenz
Basel-Landschaft
4132
Switzerland

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