Data-Driven Smart Buildings
DDSB
The target system is a cooling plant for air-conditioning in a factory building, which demands high cooling load throughout the year. The onsite operators have a lot of works except for the energy savings, therefore, Automated Fault Detection and Diagnosis (AFDD) methodology was developed and demonstrated it in real time. Operational data is diagnosed every day and each morning it is possible to confirm the AFDD result of the day before.
Automated fault detection and diagnosis (AFDD) using convolutional neural network (CNN).
Information such as system configuration, equipment specifications, and equipment performance curves, as well as operating data at 15-minute intervals, such as flow rate, temperature, power, and frequency (pumps), can be shared within Annex 81 members (not with general public).
Although the current rule–based AFDD is useful for abnormal detection, there are still difficulties to locate root causes of the faults as it requires expert knowledge to define thresholds for faults that should be varied from a system to another system. Therefore, we proposed a novel AFDD method using simulated faulty data and convolutional neural network (CNN) to diagnose real data. Firstly, to achieve high diagnosis performance, an original simulation program was developed to obtain faulty behaviour data with high quality. Then, CNN was trained based on the faulty data. Finally, the trained CNN diagnosed the real operational data. The aim of this case study is to demonstrate the effectiveness of the proposed AFDD method in the real world.
