# A Survey on Fault Diagnosis of Rolling Bearings
## 5 Datasets, Practices, Limitations/Challenges, and Future Research Trends
### Limitations and Challenges  
Although many rolling bearing fault diagnosis methods were proposed and achieved promising results. They have limitations. Most of these methods essentially focus on how to increase the effectiveness of the diagnosis whilst paying little attention to the intelligence and adaptability of the diagnosis systems. Specifically, the limitations/challenges of existing techniques are summarized as follows. Some research directions/topics were also pointed out to address these limitations. 1. **Limitations of fault detection methods:** Some rolling bearing fault detection methods, such as morphological transform-based methods, filter-based methods, decomposition-based methods, and deconvolution-based methods, often need rich domain/prior knowledge to design and use. For example, it should be known in advance how these methods operate, what their advantages and disadvantages are, and whether they are suitable or effective for the task at hand. However, experts with such knowledge are often costly to employ. In addition, the running condition of rolling bearings in actual services is complex and dynamic, making it very hard to develop a method to meet the actual environment. Capturing the periodic impact component caused by the fault in the signal is a good way to achieve fault detection but very challenging. To address this limitation, it is promising to develop an intelligent method that can automatically generate a detection model to adaptively remove the background interference and effectively retain the fault-related impulses. 2. **Limitations of traditional fault type recognition methods:** Traditional rolling bearing fault type recognition methods often include three key steps, i.e., feature extraction, feature reduction, and classification. The results of a previous step may influence the outcomes of the following step. To ensure the whole diagnostic process is feasible and effective, each step must be designed elaborately by experienced researchers, such as determining which type of features to choose/extract, which features to use, which classifier to use, and whether the classifier needs to be optimized. However, it should be noted that such a well-designed diagnostic method may only be effective for a specific fault diagnosis task. Therefore, it is promising to design methods that can automatically deal with these subtasks of fault type recognition. In addition, obtaining representative features of sample signals is the key to achieving good results. Therefore, it is a good research direction that develops a diagnostic method to automatically and simultaneously extract and construct representative features from the original bearing signals, to reduce the difficulty of distinguishing samples and improve the accuracy of fault type recognition. 3. **Limitations of deep learning-based fault type recognition methods:** Although the deep-learning-based rolling bearing fault type recognition methods can automatically achieve feature extraction, feature reduction, and classification, most of the methods are based on neural networks, which need researchers to design their architectures and adjust the corresponding parameters. The process of model design and parameter adjustment process will consume a significant amount of time and resources. Moreover, the interpretability of the neural network-based methods is not good, i.e., cannot directly express the fault identification process. In addition, these methods usually require a large number of samples to train. However, in practical engineering applications, it is typically difficult to obtain a large number of fault samples, which will limit the use of deep learning-based diagnosis methods. Therefore, it is necessary to develop new rolling bearing fault type recognition methods that do not need rich manual effort to design the architectures and select the parameters, can effectively deal with limited training data, and learn interpretable models for fault type recognition.