Improving fuzzy c-means clustering based on feature-weight learning
Pattern Recognition Letters
Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique
Expert Systems with Applications: An International Journal
Bearing Diagnosis Using Time-Domain Features and Decision Tree
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Induction motors bearing fault detection using pattern recognition techniques
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Using the Taguchi method for effective market segmentation
Expert Systems with Applications: An International Journal
Stock fraud detection using peer group analysis
Expert Systems with Applications: An International Journal
Hi-index | 12.07 |
A K-means clustering approach is proposed for the automated diagnosis of defective rolling element bearings. Since K-means clustering is an unsupervised learning procedure, the method can be directly implemented to measured vibration data. Thus, the need for training the method with data measured on the specific machine under defective bearing conditions is eliminated. This fact consists the major advantage of the method, especially in industrial environments. Critical to the success of the method is the feature set used, which consists of a set of appropriately selected frequency-domain parameters, extracted both from the raw signal, as well as from the signal envelope, as a result of the engineering expertise, gained from the understanding of the physical behavior of defective rolling element bearings. Other advantages of the method are its ease of programming, simplicity and robustness. In order to overcome the sensitivity of the method to the choice of the initial cluster centers, the initial centers are selected using features extracted from simulated signals, resulting from a well established model for the dynamic behavior of defective rolling element bearings. Then, the method is implemented as a two-stage procedure. At the first step, the method decides whether a bearing fault exists or not. At the second step, the type of the defect (e.g. inner or outer race) is identified. The effectiveness of the method is tested in one literature established laboratory test case and in three different industrial test cases. Each test case includes successive measurements from bearings under different types of defects. In all cases, the method presents a 100% classification success. Contrarily, a K-means clustering approach, which is based on typical statistical time domain based features, presents an unstable classification behavior.