000013234 001__ 13234
000013234 005__ 20161114160332.0
000013234 04107 $$aeng
000013234 046__ $$k2009-06-22
000013234 100__ $$aGryllias K., C.
000013234 24500 $$aEffects of input features in support vector machine based rolling element bearing fault detection and trending

000013234 24630 $$n2.$$pComputational Methods in Structural Dynamics and Earhquake Engineering
000013234 260__ $$bNational Technical University of Athens, 2009
000013234 506__ $$arestricted
000013234 520__ $$2eng$$aRolling Element Bearings consist one of the most widely used industrial machine elements, being the interface between the stationary and the rotating part of the machine. Due to their importance a plethora of monitoring methods and fault diagnosis procedures have been developed, in order to reduce maintenance costs, improve productivity, and prevent malfunctions and failures during operation which could lead to the downtime of the machine. Towards this direction, among different automatic diagnostic methods, the Support Vector Machine (SVM) method has been shown to present a number of advantages. Support Vector Machine is a relatively new computational learning method based on Statistical Learning Theory and combines fundamental concepts and principles related to learning, well-defined formulation and self-consistent mathematical theory. The key aspects about the use of SVMs as a rolling element bearing health monitoring tool are the lack of actual experimental data, the optimal selection of the type and the number of input features, and the correct selection of the kernel function and its corresponding parameters. A large number of input features have been proposed, being divided in two big categories: A) Traditional signal statistical features in the time domain, such as mean value, rms value, variance, skewness, kurtosis etc, B) Frequency domain based indices, such as energy values obtained at characteristic frequency bands of the measured and the demodulated signals. In this paper, the structure and the performance of a Support Vector Machine based approach for rolling element bearing fault diagnosis is presented. The main advantage of this method is that the training of the SVM is based on a model describing the dynamic behavior of a defective rolling element bearing, enabling thus the direct application of the SVM to industrial measurements of defective bearings, without the need of training the SVM with experimental data of a defective bearing. Moreover, the different types of input features (i.e. traditional statistical time domain indices versus frequency domain indices) are evaluated and compared, indicating a superiority of the frequency domain features.

000013234 540__ $$aText je chráněný podle autorského zákona č. 121/2000 Sb.
000013234 653__ $$aCondition Monitoring, Fault Detection, Support Vector Machines, Vibration Analysis, Rolling Element Bearings. Abstract. Rolling Element Bearings consist one of the most widely used industrial machine elements, being the interface between the stationary and the rotating part of the machine. Due to their importance a plethora of monitoring methods and fault diagnosis procedures have been developed, in order to reduce maintenance costs, improve productivity, and prevent malfunctions and failures during operation which could lead to the downtime of the machine. Towards this direction, among different automatic diagnostic methods, the Support Vector Machine (SVM) method has been shown to present a number of advantages. Support Vector Machine is a relatively new computational learning method based on Statistical Learning Theory and combines fundamental concepts and principles related to learning, well-defined formulation and self-consistent mathematical theory. The key aspects about the use of SVMs as a rolling element bearing health monitoring tool are the lack of actual experimental data, the optimal selection of the type and the number of input features, and the correct selection of the kernel function and its corresponding parameters. A large number of input features have been proposed, being divided in two big categories: A) Traditional signal statistical features in the time domain, such as mean value, rms value, variance, skewness, kurtosis etc, B) Frequency domain based indices, such as energy values obtained at characteristic frequency bands of the measured and the demodulated signals. In this paper, the structure and the performance of a Support Vector Machine based approach for rolling element bearing fault diagnosis is presented. The main advantage of this method is that the training of the SVM is based on a model describing the dynamic behavior of a defective rolling element bearing, enabling thus the direct application of the SVM to industrial measurements of defective bearings, without the need of training the SVM with experimental data of a defective bearing. Moreover, the different types of input features (i.e. traditional statistical time domain indices versus frequency domain indices) are evaluated and compared, indicating a superiority of the frequency domain features.

000013234 7112_ $$aCOMPDYN 2009 - 2nd International Thematic Conference$$cIsland of Rhodes (GR)$$d2009-06-22 / 2009-06-24$$gCOMPDYN2009
000013234 720__ $$aGryllias K., C.$$iYiakopoulos, C.$$iAntoniadis, I.
000013234 8560_ $$ffischerc@itam.cas.cz
000013234 8564_ $$s290821$$uhttps://invenio.itam.cas.cz/record/13234/files/CD332.pdf$$yOriginal version of the author's contribution as presented on CD, section: Identification methods in structural dynamics - i (MS).
000013234 962__ $$r13074
000013234 980__ $$aPAPER