Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Support Vector Data Description
Machine Learning
Estimating the Support of a High-Dimensional Distribution
Neural Computation
Neural Computation
Computers and Industrial Engineering
Research on SVM Classification Performance in Rolling Bearing Diagnosis
ICICTA '10 Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation - Volume 03
Fault diagnosis of ball bearings using machine learning methods
Expert Systems with Applications: An International Journal
Increasing availability of industrial systems through data stream mining
Computers and Industrial Engineering
Computers and Industrial Engineering
Feature extraction for novelty detection as applied to fault detection in machinery
Pattern Recognition Letters
Basic vibration signal processing for bearing fault detection
IEEE Transactions on Education
Computers and Industrial Engineering
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Rolling-element bearings are among the most used elements in industrial machinery, thus an early detection of a defect in these components is necessary to avoid major machine failures. Vibration analysis is a widely used condition monitoring technique for high-speed rotating machinery. Using the information contained in the vibration signals, an automatic method for bearing fault detection and diagnosis is presented in this work. Initially, a one-class @n-SVM is used to discriminate between normal and faulty conditions. In order to build a model of normal operation regime, only data extracted under normal conditions is used. Band-pass filters and Hilbert Transform are then used sequentially to obtain the envelope spectrum of the original raw signal that will finally be used to identify the location of the problem. In order to check the performance of the method, two different data sets are used: (a) real data from a laboratory test-to-failure experiment and (b) data obtained from a fault-seeded bearing test. The results showed that the method was able not only to detect the failure in an incipient stage but also to identify the location of the defect and qualitatively assess its evolution over time.