How to Spot Bearing Problems When Data is Scarce

In the world of industrial production, spotting problems in bearings is crucial. However, getting enough data to train models to spot these issues is often a challenge. Traditional deep learning models struggle when data is limited. This is where generative adversarial networks (GANs) come in. They can create more data to improve the performance of these models. But the quality of the generated data is key. Poor quality data can lead to inaccurate diagnoses. A new approach has been developed to tackle this issue. It uses a continuous wavelet convolution strategy (CWCL) instead of the usual convolution operation in GANs. This strategy can capture the frequency domain features of signals. This means it can provide a more detailed picture of what's going on with the bearing. In addition, a multi-size kernel attention mechanism (MSKAM) has been designed. This mechanism can extract features from bearing vibration signals at different scales. It can also adaptively select the most important features for the generation task. This improves the accuracy and authenticity of the generated signals. The structural similarity index (SSIM) is used to evaluate the quality of the generated signals. It calculates the similarity between the generated signals and the real signals in both the time and frequency domains. This provides a quantitative measure of the quality of the generated data. Extensive experiments were conducted on the CWRU and MFPT datasets. The results showed that the proposed approach is effective in diagnosing bearing faults with limited data. The use of GANs in fault diagnosis is not new. However, the combination of CWCL and MSKAM is a novel approach. It addresses the challenge of limited data in a unique way. The use of SSIM to evaluate the quality of the generated data is also a significant contribution. It provides a more objective measure of the quality of the generated data. This can help improve the performance of fault diagnosis models. The results of the experiments are promising. They show that the proposed approach can effectively diagnose bearing faults with limited data. This has important implications for industrial production. It can help improve the reliability and safety of machinery. However, more research is needed to fully understand the potential of this approach. Future studies could explore the use of other data augmentation techniques. They could also investigate the use of other evaluation metrics to assess the quality of the generated data.