As a precision mechanical component to reduce friction between components, the rolling bearing is widely used in many fields because of its slight friction loss, strong bearing capacity, high precision, low power consumption, and high mechanical efficiency. This paper reviews several excellent kinds of study and their relevance to the fault detection of rolling bearings. We summarize the fault location, sensor types, bearing fault types, and fault signal analysis of rolling bearings. The fault signal types are divided into one-dimensional and two-dimensional images, which account for 40.14% and 31.69%, respectively, and their classification is clarified and discussed. We counted the proportions of various methods in the references cited in this paper. Among them, the method of one-dimensional signal detection with external sensors accounted for 3.52%, the method of one-dimensional signal detection with internal sensors accounted for 36.62%, and the method of two-dimensional signal detection with external sensors accounted for 19.72%. The method of two-dimensional signal detection with internal sensors accounted for 11.97%. Among these methods, the highest detection rate is 100%, and the lowest detection rate is more than 70%. The similarities between the different methods are compared. The research results summarized in this paper show that with the progress of the times, a variety of new and better research methods have emerged, which have sped up the detection and diagnosis of rolling bearing faults. For example, the technology using artificial intelligence is still developing rapidly, such as artificial neural networks, convolutional neural networks, and machine learning. Although there are still defects, such methods can quickly discover a fault and its cause, enrich the database, and accumulate experience. More and more advanced techniques are applied in this field, and the detection method has better robustness and superiority.

Rolling element bearings (REBs) are the most frequent cause of machine breakdowns. Traditional methods for fault diagnosis in rolling bearings rely on feature extraction and signal processing techniques. However, these methods can be affected by the complexity of the underlying patterns and the need for expert knowledge during signal analysis. This paper proposes a novel signal-to-image method in which the raw signal data are transformed into 2D images using continuous wavelet transform (CWT). This transformation enhances the features extracted from the raw data, allowing for further analysis and interpretation. Transformed images of both normal and faulty rolling bearings from the Case Western Reserve University (CWRU) dataset were used with deep-learning models from the ResNet family. They can automatically learn and identify patterns in raw vibration signals after continuous wavelet transform is used, eliminating the need for manual feature extraction. To further improve the training results, squeeze-and-excitation networks (SENets) were added to improve the process. By comparing results obtained from several models, we found that SE-ResNet152 has the best performance for REB fault diagnosis.