DC-DC Buck circuit fault diagnosis with insufficient state data based on deep model and transfer strategy

As the common power supply of electronic systems, the fault of DC-DC Buck circuit will trigger the faulty power supply which eventually cause serious problems in system tasks. Therefore, it is of great significance to accurately identify the faults of DC-DC Buck circuit. Due to the high cost of collecting circuit fault data set, the lack of effective fault data will lead to the inability to establish an intelligent fault diagnosis model. In this paper, the model of source circuit with sufficient data is used to solve the problem of fault diagnosis of target circuit with lack of data. A deep transfer kernel extreme learning machine auto encoder (DKEA) model is designed. The activation function of Gaussian error linear units (GELU) is used to describe the probability of neuron input, and the kernel extreme learning machine is employed as the classifier to complete the diagnosis task. First, only the output voltage signal of the circuit is collected to extract its primary features in time and frequency domain, and then the primary features are mined and classified by using the deep extreme learning machine auto encoder (ELM-AE) model. Sufficient source domain data is used to train the deep ELM-AE model, and the parameters obtained are used as the initialization parameters of the target domain diagnosis model. With the help of transfer learning, a small number of training samples in the target domain are used to fine-tune the deep ELM-AE model to adapt to the remaining testing samples in the target domain. The experimental results proves that the proposed method can effectively achieve the transfer diagnosis between different DC-DC Buck circuits based on the source domain data.