Biblio

The output of rolling bearings, as one of the most widely used support elements, has a significant impact on the equipment's stability and protection. Automatic and effective mining of features representing performance condition plays an important role in ensuring its reliability. However, in the actual process, there are often differences in the quality of features extracted from feature engineering, and this difference cannot be evaluated by commonly used methods, such as correlation metric and monotonicity metric. In order to accurately and automatically evaluate and select effective features, a novel assessment metric is established based on the attributes of the feature itself. Firstly, the features are extracted from different domains, which contain differential information, and a feature set is constructed. Secondly, the performances of the features are evaluated and selected based on internal distance and external distance, which is a novel feature evaluation model for classification task. Finally, an adaptive boosting strategy that combines multiple weak learners is adopted to achieve the fault identification at different severities and orientations. One experimental bearing dataset is adopted to analyze, and effectiveness and accuracy of proposed metric index is verified.

The reliability of mechanical equipment is very important for the security operation of large-scale equipment. This paper presents a rolling bearing fault diagnosis method based on Variational Mode Decomposition (VMD) and Convolutional Neural Network (CNN). This proposed method includes using VMD and CNN to extend multi-sensor data, extracting detailed features and achieve more robust sensor fusion. Representative features can be extracted automatically from the raw signals. The proposed method can extract features directly from data without prior knowledge. The effectiveness of this method is verified on Case Western Reserve University (CWRU) dataset. Compared with one sensor and traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. Because of the end-to-end feature learning ability, this method can be extended to other kinds of sensor mechanical fault diagnosis.