Bearing Fault Diagnosis With Incomplete Training Data: Fault Data With Partial Diameters

Existing data-driven bearing fault diagnosis studies are based on strong assumptions: complete fault samples are required. The number of fault data can be more or less, but the data of each fault class must be available. However, such a condition is difficult to meet in the industry. Therefore, this paper addresses an open issue: bearing fault diagnosis with incomplete training data. In other words, only partial fault data are available in the training process. This issue is more in line with the industrial situation, and the issue is worthy of in-depth research. In response to this issue, the Cepstrum Scale-Distance based Framework (CSD-Framework) is proposed, including C-stage, S-stage, and D-stage. The three stages realized vibration signal transformation, multi-scale adaptive adjustment, and multi-metric distance matching, respectively. This is a general framework, suitable for analyzing vibration signals, and is convenient to be combined with advanced AI algorithms. On this basis, the Multi-Metric-Adaptive-Clustering (MMAClustering) algorithm and the Multi-Metric-Weight-Classify (MMW-Classify) algorithm are proposed to form the D-stage of CSD. The proposed method has three advantages: 1) generic; 2) scalable; 3) good ability to classify unseen data. Experimental results showed that the performance of CSD was better than a variety of existing AI algorithms, as well as Ceps-AI methods based on cepstrum and AI algorithms.

Automated summary

Existing data-driven bearing fault diagnosis studies require complete fault samples, which is challenging to meet in the industry. This paper proposes a Cepstrum Scale-Distance based Framework (CSD-Framework) to address the issue of bearing fault diagnosis with incomplete training data. The framework includes three stages that transform vibration signals, adaptively adjust on multiple scales, and match distances using multiple metrics. The proposed method outperforms existing AI algorithms and Ceps-AI methods in terms of classification performance.