Adaptive Algorithms in Vibration Diagnosis at Belarusian State University
Discover the use of adaptive algorithms in vibration diagnosis at Belarusian State University of Informatics and Radioelectronics. Explore the concepts of machine learning systems, rolling bearing structures, test rig equipment, and more in the field of vibration analysis and fault prediction processes.
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Presentation Transcript
Belarusian State University of Informatics and Radioelectronics ADAPTIVE ALGORITHMS IN VIBRATION DIAGNOSIS A.V. Tsurko Liahushevich S.I. Associate Professor The Republic of Belarus, Minsk 2013
Plan Rolling bearing structure Test rig equipment Vibration noise Vibration diagnosis process Adaptive algorithm concept Machine learning systems
Rolling bearing structure 1) Outer Ring 2) Ball 3) Cage 4) Ball Race 5) Inner Ring
Test Rig Equipment Rolling Bearing Induction motor Accelerometer Heavy metal base o table Rubber spacer Computer
Vibration noise Normal bearing: waveform and the spectrum Defective outer race: waveform and the spectrum
Vibration diagnosis process The process includes five sequential phases: 1) Theoretical model development 2) Empirical data obtaining 3) Diagnostic feature extraction 4) Fault state classification 5) Fault progress prediction and decisions
Adaptive algorithm concept Adaptation means (re)tuning of system parameters for better performance In modern vibration analysis adaptation algorithms are mainly used for classification at the fourth diagnosis phase.
Machine learning systems Uncertainty of input data require using of adaptive data processing Best effectiveness in classification is provided by machine learning systems Such systems adapt to input data in process of training (learning) by selected data set under control of human-supervisor Most common and effective machine learning techniques are ANN (BP-based MLP) and SVM
Artificial Neural Network (ANN) and Support Vector Machine (SVM) SVM places a Hyperplane (H): H1 does not separate the classes. H2 does, but only with a small margin. H3 separates them with the maximum margin. ANN is an interconnected group of nodes, akin to the vast network of neurons in a brain.
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