A new approach to detection of defects in rolling element bearings based on statistical pattern recognition

The paper presents a new approach to the classification of rolling element bearing faults by implementing statistical pattern recognition. Diagnostics of rolling element bearing faults actually represents the problem of pattern classification and recognition, where the key step is feature extraction...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2009-11, Vol.45 (1-2), p.91-100
Main Authors: Stepanic, Pavle, Latinovic, Ilija V., Djurovic, Zeljko
Format: Article
Language:English
Subjects:
Algorithms
CAE) and Design
Classifiers
Computer-Aided Engineering (CAD
Design parameters
Engineering
Feature extraction
Industrial and Production Engineering
Mechanical Engineering
Media Management
Neural networks
Original Article
Pattern classification
Pattern recognition
Roller bearings
Vibration analysis
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Summary:The paper presents a new approach to the classification of rolling element bearing faults by implementing statistical pattern recognition. Diagnostics of rolling element bearing faults actually represents the problem of pattern classification and recognition, where the key step is feature extraction from the vibration signal. Characterization of each recorded vibration signal is performed by a combination of signal's time-varying statistical parameters and characteristic rolling element bearing fault frequency components obtained through the envelope analysis method. In this way, an 18-dimensional vector of the vibration signal feature is obtained. Dimension reduction of the 18-dimensional feature vectors was performed afterward into two-dimensional vectors representing the training set for the design of parameter classifiers. The classification was performed in two classes, into defective and functional rolling element bearings. Main trait of parameter classifiers is simplicity in their design process, as opposed to classifiers based on neural networks, which employ complex training algorithms.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-009-1953-7