# A Survey on Fault Diagnosis of Rolling Bearings
## 5 Datasets, Practices, Limitations/Challenges, and Future Research Trends
### Future Research Directions  
In addition to the aforementioned research directions/topics, there are some other research topics that are becoming popular in this field. This subsection will discuss these research trends. 1. **Transfer learning-based methods:** The effective performance of the fault type recognition methods usually needs to meet a basic assumption, namely, that the training samples and test samples are independent and identically distributed. However, the monitor information of rolling bearing is generally subject to working conditions, such as the characteristic frequency and amplitude changing with rotational speed, resulting in a large distribution difference between training data and test data, thereby presenting a domain migration issue. Transfer learning (TL) can extract knowledge from one or more related scenes to help improve the learning performance of scenarios in the target domain [165]. TL can relax the assumption of independent and identical distributions and provide a new solution to address the above deficiencies. The TL-based rolling bearing fault type recognition methods were proposed and achieved desirable results [166; 167; 168]. The TL-based recognition model, learning the common feature space from the source domain data and the target domain data to reduce the distribution difference between different domains, cannot adaptively adjust its parameters for target domain tasks, thereby affecting its domain adaptability and recognition accuracy. Thus, the further development of TL-based fault type recognition methods is a good direction for future research to improve the classification performance, recognition accuracy, and generalization under variable operating conditions. 2. **Few-shot learning methods:** A large amount of labelled data is also the key to ensuring the performance of existing fault type recognition methods, especially for deep learning-based methods. In real-world scenarios, it is easy to obtain enough normal samples due to the rolling bearing mostly running under normal conditions, but the fault samples are typically difficult to obtain and require extensive manual effort to label. The absence of labelled fault samples will either lead to overfitting in the training process or the class imbalance problem. Few-shot learning (FSL) is effective for distinguishing failure attribution accurately under very limited data conditions [169; 170]. Data augmentation, data/model transfer, and meta-learning constitute the three main threads of FSL methods. Thus, the comprehensive exploration of FSL-based fault type recognition methods is a good direction for future research for reducing the dependence on large amounts of data, avoiding the risk of overfitting, and improving the applicability and recognition performance. 3. **Evolutionary deep learning methods:** Evolutionary deep learning methods aim to deal with the limitations of deep learning methods, particularly neural networks, by using evolutionary computation (EC) techniques. This direction includes two main topics, i.e., using EC methods to automatically design neural networks and using EC methods to evolve deep models by themselves. On the first topic, some work was performed to evolve neural networks for fault diagnosis by finding the optimal numbers of layers, network connections, numbers of filters, etc. [171; 172; 173; 174; 175]. These methods can reduce the requirement of expertise from both the neural network domain and the problem domain, improve recognition performance, and decrease the number of parameters in the evolved models. On the second topic, pure EC methods, particularly genetic programming methods, are used to evolve deep models. GP is a computational intelligence algorithm to achieve automatic programming without human intervention and domain knowledge [176; 177]. With a flexible program expression, GP can automatically evolve variable-length models to solve a task. GP has shown promise in the computer vision domain by evolving deep models [178; 179; 180; 181].