Performance, cost, and risk
What are the performance, cost, and risk impacts of implementing this product?
- Performance: Identify potential failures of HVAC systems with moderately low false positive and negative rates.
- Cost: Costs associated with implementing, training and validating a machine learning algorithm.
- Risk: False negatives and positives can be reduced under these algorithms, but are not avoided altogether.
Implementation requirements
What capabilities would a business/organization/institution need to have to implement this product?
- Processes: HVAC monitoring should be ongoing, data-driven decision-making should be enabled.
- Resources: HVAC sensor (temperature) data, computational infrastructure, railway maintenance teams.
- Competences: Machine learning expertise.
- Technologies: Machine learning algorithms and maintenance management systems to provide advice to decision-makers.
Related works
- Veldman et al. (2022). A Framework for HVAC Malfunction Detection using Machine Learning.
Contact information
For further inquiries regarding this product, feel free to get in touch with:
- Mariëlle Stoelinga, University of Twente. m [dot] i [dot] a [dot] stoelinga [at] utwente [dot] nl
