In modern industrial systems, mechanical rolling bearings, as the core elements of rotating mechanical equipment, play an important role in supporting rotors, reducing friction, and transmitting loads (Liu and Yu, 2021). Therefore, realizing early fault diagnosis and health condition monitoring of rolling bearings is an urgent need to guarantee the safe, efficient, and continuous operation of industrial systems (Li et al., 2019). Traditional bearing fault diagnosis methods usually require a large amount of fault data with clear labels collected from specific equipment and performing complex feature engineering to extract fault-sensitive features. However, in actual application scenarios, they face challenges such as scarce labeled data, variable operating conditions, equipment differences, and difficulties in cold start. To address these difficulties, transfer learning, as a training method, has been applied to intelligent algorithms (Shang et al., 2024). The core principle of transfer learning is to transfer the algorithm parameters learned in the “source domain” to another related but different “target domain” to solve the difficulties brought about by the limited training data in the target domain. Hu et al. (2024) combined a deep neural network with SKNet and Inception-ResNet-v2 for bearing fault diagnosis. Li et al. (2025) designed an adaptive feature mode decomposition method for the same purpose. Wang et al. (2025) developed a model for diagnosing faults in aviation bearings based on a long short-term memory and an optimized deep residual shrinkage network. This article introduces a transfer learning-based fault diagnosis algorithm for rolling bearings. The proposed algorithm enhances a conventional convolutional neural network (CNN) by introducing a domain category judgment module and an inter-domain conditional probability distribution difference module to realize transfer learning between source and target domain samples. Simulation tests were subsequently conducted. The innovation of this paper lies in realizing the transfer learning of the intelligent fault diagnosis algorithm by using the domain category judgment module and the inter-domain conditional probability distribution difference module to make the algorithm quickly learn the feature distribution rules even when there are not enough training labels in the face of a new fault diagnosis environment, thereby achieving rapid diagnosis of bearing faults in the new environment and improving the training efficiency of the algorithm.

When a bearing malfunctions, its inherent fault characteristic frequencies will be excited during operation. Different types of faults have different fault characteristic frequencies (Liu et al., 2025). Intelligent algorithms obtain the patterns of bearing fault characteristics through sample training and then identify the types of bearing faults. Usually, a large number of samples are required for training. However, mechanical rolling bearings with faults are difficult to be used for a long time or in large quantities. Moreover, even for the same type of fault, variations in equipment and operating conditions can lead to shifts in fault characteristics, making it difficult for trained intelligent algorithms to adapt to new equipment or operating environments (Guo et al., 2025). Consequently, when pre-trained or existing intelligent algorithm models are attempted to be applied to brand-new bearing equipment, the absence of historical failure data from the target equipment often prevents their direct application.

The core components of transfer learning include the source domain, the target domain, and the transfer methodology. The source domain generally refers to a dataset with abundant labeled samples—in this paper, it denotes the dataset used to train the generic fault diagnosis model. The target domain typically consists of sparsely labeled or unlabeled data; here, it refers to the dataset of new equipment or operating conditions for transfer learning. The transfer methodology comprises the techniques used to transferring diagnostic knowledge from the source domain to the target domain, which may include model-based, instance-based, feature-based methods (Xie et al., 2025).

Model-based transfer method does not require identical label spaces between the source and target domains. However, if there are significant differences in the distribution of samples in the source domain and the target domain, and the quality of labeled samples in the target domain is not high, it will affect the fine-tuning effect of the model (Zhang et al., 2024). The instance-based transfer method is easy to operate, but when the difference in the sample distribution between the source domain and the target domain is too large, it will lead to a decrease in the number of similar samples in the two domains, affecting the training effect (Chen et al., 2024). The feature-based method adjusts the model parameters from the level of the sample feature space, reducing the difference in the sample feature distribution between the two domains and trying to maintain the classification effect of the model on the source domain samples. The above parameter adjustment process does not involve the classification labels of the target domain samples, so it is applicable to the fault diagnosis in the unsupervised domain.

Finally, this paper adopts a feature-based transfer approach to train the fault diagnosis model. The overall training architecture is depicted in Figure 1.

Fault samples, i.e., vibration signals, from rolling bearings are collected to form the source and target domains. The source domain dataset must contain complete fault category labels (He et al., 2024), whereas the target domain dataset has no strict labeling requirements.

Rolling bearings are the core components in rotating mechanical systems, and their operating states are directly related to the safety, reliability, and service life of the equipment. Once a fault occurs, if it fails to be identified and intervened in a timely manner, it is very likely to trigger a chain of mechanical failures, resulting in significant economic losses and even safety accidents. Traditional fault diagnosis methods rely on expert experience and signal processing techniques, which are effective under specific working conditions. However, they have limitations such as weak generalization ability, strong dependence on feature engineering, and high sensitivity to noise. Deep learning technology has shown great potential in bearing fault diagnosis. It can extract features from the original vibration signals and use them to determine the fault type. However, such data-driven methods highly depend on a large number of high-quality and fully labeled fault samples. In actual industrial scenarios, it is difficult to obtain such fault samples. On the one hand, it is difficult to accumulate enough samples for new equipment or rare faults. On the other hand, manual annotation requires the participation of professional engineers, which is costly and error-prone. The fault diagnosis algorithm for rolling bearings is faced with the dilemma of abundant source domain data but scarce target domain data. To address this dilemma, transfer learning has been proposed. Transfer learning alleviates the distribution shift problem caused by speed changes, load fluctuations, sensor differences, or different equipment models through knowledge transfer mechanisms, such as feature alignment, parameter fine-tuning, and adversarial training, thereby achieving high-precision diagnosis with limited target data. In this paper, the CNN algorithm was adopted to identify rolling bearing faults. During the training process, a domain category judgment module and an inter-domain conditional probability distribution difference module were introduced to implement transfer learning between source domain samples and target domain samples. Then, a simulation experiment was carried out. The experimental results showed that in the same transfer task, the diagnostic performance of the improved CNN algorithm was superior to that of the SVM and BPNN algorithms. In different transfer tasks, the diagnostic performance of the proposed algorithm did not change significantly, while that of the SVM and BPNN algorithms declined. The reason is that in different transfer tasks, the samples in the target domain change, and there are differences in the sample distributions of different fault types between the two domains. The SVM and BPNN algorithms directly apply the model trained with source-domain samples to the target domain. Due to the difference in feature distribution between the two domains, the recognition results will deviate, ultimately leading to a decrease in diagnostic performance. The CNN algorithm uses a domain category judgment module and an inter-domain conditional probability distribution difference module during training to balance the feature distribution between the two domains as much as possible during the model parameter adjustment process, so as to keep the diagnostic performance unchanged.

The shortcoming of this paper is that only a single experimental platform was used when validating the algorithm. Meanwhile, the comparison was only made with the SVM and BPNN algorithms, which made the validation results lack universality. Therefore, the future research direction is to expand the experimental platforms and the diagnostic algorithms for comparison to make the experimental results have sufficient universality.

This paper presents a transfer learning-based algorithm for diagnosing faults in mechanical rolling bearings. A conventional CNN algorithm integrates a domain category judgment module and an inter-domain conditional probability distribution difference module, thereby enabling effective knowledge transfer between source and target domain samples. Simulation tests were carried out on the PT100 bearing fault simulation test platform, where vibration signals were collected from bearings under normal conditions and with inner ring, outer ring, and ball faults at motor speeds of 1,000, 1,500, and 2,000 r/min. The fault diagnosis performance of the improved CNN algorithm in operating condition transfer tasks was compared with the SVM and BPNN algorithms. Moreover, ablation experiments were performed. It was found that the diagnostic accuracy of each algorithm remained consistent for different fault types in the same transfer task. Among the compared methods, the improved CNN algorithm achieved the highest accuracy, followed by BPNN, and the SVM algorithm had the lowest accuracy. In different transfer tasks, both SVM and BPNN algorithms exhibited a significant decline in diagnostic accuracy for transfers in “operating condition 1 → 3” and “operating condition 3 → 1,” whereas the accuracy of the proposed algorithm remained largely unchanged. The ablation study confirmed that removing either the domain category judgment module or the inter-domain conditional probability distribution difference module significantly resulted in a significant reduction in the diagnostic accuracy, and the simultaneous removal of both modules resulted in a further performance drop.