Sustainable subway indoor air quality monitoring and fault-tolerant ventilation control using a sparse autoencoder-driven sensor self-validation

Sensors providing measurements for monitoring and control of indoor air quality (IAQ) are key components of the ventilation systems in subway stations. However, faulty sensors due to harsh ambient conditions may deliver incorrect information triggering misinterpretation; causing energy waste or IAQ deterioration. This paper presents a holistic online framework for sensor self-validation in a subway station based on a sparse autoencoder (AE) architecture. The sensor self-validation procedure consists of sensor fault detection, faulty sensor identification, and faulty sensor reconstruction. First, the AE-based detection rate between 44% and 100%. Then, the faulty sensor identification was conducted through an AE-sensor validity index (SVI AE ). The faulty sensor reconstruction was conducted by the AE structure and evaluated with several performance metrics. Finally, the sustainability and fault-tolerance aspects of this framework were verified through mathematical modeling of the ventilation system; showing the effects of the faulty and reconstructed sensors on energy consumption and public health.