Predictive maintenance of HVAC systems across multiple sites
The goal is to support HVAC service providers and controls /BMS engineers, through a predictive maintenance and diagnostics web interface, to identify and prioritize issues at their clients HVAC systems, before they strategically decide to intervene as needed.
The users can monitor the health status of their clients’ sites, systems and units, based on incoming alerts. Alerts derived from system errors, maintenance, and communication are based on diagnostics, while alerts derived from flagged incidents, anomalies, are about the system’s behavior and the result of a rule-based mechanism for predictive maintenance. The users can also monitor operational data and remotely control the HVAC units.
This case is made for the purpose of this portfolio to showcase an example of approach, process and outcome. It is a redesign of the predictive maintenance solution by CoolAutomation as described and presented in this demo video:
HVAC Predictive Maintenance: Turbocharge Your Technical Service Provision, Feb. 2022
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Disclaimer and design limitations:
This is not a project I completed for CoolAutomation. It is a speculative “what-if” case study created to demonstrate my design thinking and problem-solving approach. Some ideas are inspired by solutions I have previously implemented for other clients with similar challenges.
I did not conduct user research for this case, nor did I collaborate with or receive any information from CoolAutomation. I have never worked with the company, nor do I have access to any internal resources or proprietary data.
Where user research insights would normally be gathered, I fabricated product, stakeholder and user perspectives based on publicly available industry sources and my prior experience in similar project contexts (outside the HVAC industry).
This means the work shown here may not fully reflect real-world constraints. It simply illustrates how I would probably approach redesigning a predictive-maintenance platform if this were a real project.
Sources (all public):
- Company website
- Company YouTube demo videos
- Competitor research, online and AI supported
- Industry pain-point research, online and AI supported
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