摘要：In this paper, a deep learning-based fault diagnosis framework is proposed to improve the fault diagnosis accuracy of system-level equipment such as condenser systems in nuclear power plants. The condenser system signals are non-vibrating, slowly time-varying, and multi-dimensional in nature. Therefore, in this paper, we propose a deep learning-based fault diagnosis framework, which adopts the idea of combining data warehouse modeling and deep learning fault diagnosis, and establishes the data set required for deep learning through accurate simulation modeling of typical condenser faults, so as to make full use of the feature extraction capability of deep learning under large-scale samples. Based on this, an end-to-end deep learning model is developed for accurate diagnosis of multiple condenser faults under multiple system conditions. Through the fault diagnosis experiments on the validation set data under various system conditions, the fault diagnosis accuracy is as high as 0.9584, which verifies the effectiveness of the proposed framework in fault diagnosis of system-level equipment.
