Self-correction method for sensor faulty heat pump system based on machine learning

Abstract

Sensor faults are a common type of failure in heat pump systems, which can seriously affect the normal operation of systems. Self-correction of the sensor fault in the system is crucial. State-of-the-art sensor fault correction methods based on data-driven and physical models face challenges, such as the need for co-located sensors, accurate physical models, and a large amount of labeled data, greatly limiting their applicability. This paper proposes using machine learning methods for fault self-correction. Firstly, a data self-correction strategy based on the convolutional autoencoder is introduced. Furthermore, an artificial sample generation strategy is proposed to address the scarcity of sensor fault data for data-driven training of the self-correction model. The results demonstrate that the proposed method effectively self-corrects both single and multiple faults. Simultaneously, thermal fault diagnosis evaluations reveal over 90 % accuracy in corrected data, with a maximum diagnostic improvement of 53.5 %. Furthermore, the study shows that the number of parameters is crucial for effective correction, underscoring that over-constraint is essential for successful self-correction.