Knowledge-extracted deep learning diagnosis and its cloud-based management for multiple faults of chiller

Machine learning has the inspiring potential for fault prediction in HVAC systems, which is essential for the system energy efficiency. For the scenario of multiple faults, however, machine learning model is not efficient because the training datasets of this scenario are difficult to collect in the real applications. This paper proposes the novel knowledge-embedded deep belief network (DBN) method to diagnose the electronic-thermal and thermal-thermal multiple faults for chillers in the buildings. Firstly, the characteristics of electronic-thermal and thermal-thermal faults are analyzed through the experiments. Through detecting the sensor biases, the sensorthermal faults are decoupled successfully. Secondly, the representative features are extracted using the proposed DBN model. In the fine-tuning process, the original DBN network is optimized through embedding the extracted knowledge rules. We integrate the knowledge-embedded DBN, extreme learning machine (ELM) and knearest neighbor (KNN) as the diagnosis model. The experimental datasets are used to validate the proposed multi-fault diagnosis method. The results show that its diagnosis performance is satisfactory when the training dataset of some fault is absent. Finally, we develop the cloud-based diagnosis and online management platform. The proposed method is deployed on the cloud to realize online diagnosis and smart management.

Automated summary

This paper proposes a knowledge-embedded deep belief network (DBN) method to diagnose multiple faults in chillers in buildings. The characteristics of electronic-thermal and thermal-thermal faults are analyzed, and the sensor biases are successfully decoupled. Representative features are extracted using the proposed DBN model. The proposed method integrates knowledge-embedded DBN, extreme learning machine (ELM), and k-nearest neighbor (KNN) as the diagnosis model.