Gradual fault early stage diagnosis for air source heat pump system using deep learning techniques

Abstract

Due to slow development and no evident characteristic of gradual fault in air source heat pump (ASHP) systems, existing methods are insufficient in detecting gradual fault at early stages, which causes many ASHPs to be running under minor gradual fault. Gradual fault in systems, including minor gradual fault, will decrease efficiency, increase energy consumption, reduce environmental thermal comfort, and increase carbon emissions. This paper proposes a novel gradual fault diagnosis approach, which mainly includes three contributions. Firstly, for ASHP modeling, a convolution-sequence (C-S) model is proposed; Secondly, a pre-process thinking for fault diagnosis is proposed, which makes the diagnosis method have a more suitable dataset; Finally, a convolutional neural network with an optimized convolution kernel (one-dimensional convolution kernel) is used to diagnose the specific failure for ASHP. The optimal hyper-parameter selection is identified with many attempts. Furthermore, a detailed comparison between different fault diagnosis method models is also studied. In the last part of the results and discussion, the outcome of the diagnosis effectiveness by the C-S model accuracy is obtained. Therefore, the proposed method has a desirable effect on gradual fault detection and diagnosis, which means it is a feasible and high-precision detection and diagnosis method for gradual fault in ASHP systems.