The Predicitve Maintenance supporting tools in general aim at the facilitating predictive maintenance tasks for various large energy systems (e.g. HVAC) installed in buildings.
Existing modern real-time data based solutions are able to support recognizing building technical systems' malfunctions, inefficiencies and optimization possibilities. Continuous data collection and data analytics are enabled by installed smart building infrastructures (such BACS system and data cloud infrastructures) and IoT technology, in order to provide the quality predictive maintenance service with near real-time metering data and history data from studying facilities in the city.
In particular companies are active in this domain. There exist commercial products to support predictive maitenance tasks such as e.g. from Siemens MindSpere toolset or HippoCMMS. Such EU research projects as SYNERGY and BEYOND do advanced research in this area too, leveraging big data, AI and IoT.
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