A Survey on Fault Diagnosis of Rolling Bearings
Abstract
:1. Introduction
2. Background, Taxonomy, and Scope
2.1. Fault Forms/Types of Rolling Bearing
- (1)
- Fatigue
- (2)
- Wear
- (3)
- Deformation
- (4)
- Corrosion
- (5)
- Fracture
2.2. Taxonomy and Scope
3. Rolling Bearing Fault Detection
3.1. Morphological Transform-Based Fault Detection Methods
3.2. Filter-Based Fault Detection Methods
3.3. Decomposition-Based Fault Detection Methods
3.4. Deconvolution-Based Fault Detection Methods
4. Rolling Bearing Fault Type Recognition
4.1. Traditional Fault Type Recognition Methods
- (1)
- Feature extraction
- (2)
- Feature reduction
- (3)
- Classification
4.2. Deep Learning Based Fault Type Recognition Methods
5. Datasets, Practices, Limitations/Challenges, and Future Research Trends
5.1. Commonly Used Datasets and Practices
5.2. Limitations and Challenges
- (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.
5.3. Future Research Directions
- (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]. The models evolved by GP typically have better interpretability than neural networks. However, there is little work on GP for fault diagnosis [182,183,184]. Figure 6 shows an example of using GP to solve fault type recognition, where the GP method is used to automatically generate informative and discriminative features from original vibration signals for recognizing different fault types. The left example tree of Figure 6 is the solution evolved by GP, showing high interpretability. In addition, the solutions are often creative and even not considered by human experts [183,184]. However, both topics have not been fully investigated in the fault diagnosis community. Therefore, it is promising to develop effective evolutionary deep learning approaches to fault diagnosis.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Peng, B.; Bi, Y.; Xue, B.; Zhang, M.; Wan, S. A Survey on Fault Diagnosis of Rolling Bearings. Algorithms 2022, 15, 347. https://doi.org/10.3390/a15100347
Peng B, Bi Y, Xue B, Zhang M, Wan S. A Survey on Fault Diagnosis of Rolling Bearings. Algorithms. 2022; 15(10):347. https://doi.org/10.3390/a15100347
Chicago/Turabian StylePeng, Bo, Ying Bi, Bing Xue, Mengjie Zhang, and Shuting Wan. 2022. "A Survey on Fault Diagnosis of Rolling Bearings" Algorithms 15, no. 10: 347. https://doi.org/10.3390/a15100347