Wouldn’t you want to live in a world where building owners could pre-empt maintenance errors before failure occurs?
With Chiller Doctor V1.0, which is developed using AI/Machine Learning based solution, it helps building owners to minimise the risk of unexpected failure in chillers, improve energy consumption in buildings, and reduce operation costs.
Flow of data from IoT sensors augmented with inputs from operation crew, feeding into an AI-based Chiller Doctor, to provide recommendations for maintenance of chillers and buildings
Chiller Doctor collects data from various sensors located at different locations and/or from Building Management Systems (BMS). With the collected data, supervised machine learning techniques are applied to detect the faults and classify the faults to corresponding fault categories.
Presently, there does not exist any cost-effective AI-based industry solution for Chiller Fault Detection in the tropics. We are inviting local building owners to contribute chiller data to refine our AI algorithms to more accurately diagnose the health of the chillers.
Who is it for?
Individual, Technology Providers, Institute of Higher Learnings (IHLs) and Research Institutes (RIs) are welcome to use our technologies.
Made possible with artificial intelligence (AI) solutions, the “Chiller Doctor V1.0” performs the following benefits:
- Early detection of faults to reduce risk of failure
- Diminishing the reliance on monthly system maintenance
Click here to register interest in Chiller Doctor.
1. Where has this solution been used?
Currently, we are in collaboration with a government agency to develop Chiller Doctor as a service in one of its portal solution. It helps building owners to identify chiller faults, diagnose them and recommend fixes effectively. An early pilot is currently being conducted and interested building owners can contact us for more information.
2. How much will it cost clients to use Chiller Doctor V1.0 service?
The service is provided at no charge during the current pilot stage, as we continually enhance its accuracy, refine the underlying AI algorithm and enrich the data inputs with support from industry partners’ datasets. The selection of building owners will be subjective to qualifying terms and conditions.
3. How accurate is the tool's capabilities?
At present, the initial test is at 92% accuracy. During the pilot stage, we invite building owners to contribute chiller data to refine our AI algorithms and help to further improve its accuracy of diagnosing faults.
4. How much will building owners be expected to save using Chiller Doctor's AI-driven capabilities?
Savings will be measured based on operational cost savings through the reduced need for monthly systems maintenance, early detection of potential breakdown to lower unforeseen operation cost, and potential energy saving through optimisation to reduce carbon footprints. The quantum of savings will depend on the operating parameters and characteristics of each property/building.
For further enquiries on Chiller Doctor, please contact DSL_Tech@imda.gov.sg.