## 1 Introduction
The method of generative adversarial network (GAN) generates data deriving from learning different failure characteristics to expand the training data and solve the data imbalance problem [14]. However, most diagnostic models proposed so far are based on supervised learning that identifies labels [15], and identifying different types of fault data is more challenging. In the industry, it is a hard task to label fault data as it is regarded as an \"unknown category,\" so simulation is usually applied to the faults of this type. A PCCNN algorithm [16] is used in the computation of probabilistic confidence levels to distinguish between \"known classes\" and \"unknown classes\" of failure classes. However, the current research is still applied as a primary source in the open-source simulation dataset for the low simulation efficacy in different cases. Most previous studies based on the method of data-driven intelligent fault diagnosis (DIFD) focused on the improvement of the generalization performance and fault diagnosis with several reconfigurations. [PERSON] et al. proposed [17] domain adaptation from transfer learning and other techniques to achieve cross-domain fault diagnosis. [PERSON] et al. [18] provided an overview of knowledge transfer for rotary machinery fault diagnosis (RMFD) by applying different transfer learning techniques in four categories: transfer between multiple fault classes, transfer between numerous locations, transfer between working conditions, and transfer between various machines. Different machines have different failure classes and data characteristics. [PERSON] et al. [19] proposed transfer learning based on stacked autoencoders (SAEs) algorithms combined with classification and domain-blending to improve the accuracy of diagnostic models and the versatility of fault diagnosis data for different machines. In preceding research, models of fault diagnosis were established with the combination of transfer learning and deep neural networks. Based on the results, the research carries out the solution to the imbalance of sample fault data. [PERSON] et al. [20]proposed two transfer strategies to analyze the probable scenarios in practical cases and suggested transfer strategies applicable in each case. In previous works, the fault-based modeling, and data-driven methods for known fault diagnosis has been performed, while the authors of this paper made a preliminary survey of fault detection in data-driven with unknown class and transfer-learning in similar datasets. This paper proposed TPCCNN (Transfer PCCNN) focusing on monitoring vibration frequencies which can be featured by FFT and trained and transferred in PCCNN model for further fault diagnosis for the first time. The rest of this paper is organized as follows:  
The Section 2 presents the principle of TPCCNN, the introduction of PCCNN, and the method of TPCCNN-based fault diagnosis including feature extraction, pre-trained model, and fine-tuning. This is followed by a presentation about the experimental setting, datasets, processes, and results of TPCCNN. The Section 4 gives the experimental results to demonstrate the efficiency of the proposed method. Finally, the paper gives conclusion and future work.