On-line vibration monitoring of bearing faults in induction machine usin Cyclic Spectral Analysis

Tafinine, Farid

doi:10.1051/matecconf/20168309010

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The ball defects default can be seen as an inner and outer race default since the defective ball can touch the inner or outer ring. The characteristic frequencies for defects are given by [2], [3], [4]: For a ball fault:…”

Section: Bearing Faults and Experimental Setupmentioning

confidence: 99%

Comparative study between two artificial intelligence techniques (NN and SVM) applied in fault diagnosis of Wind Turbine

Tafinine

2024

S. F. J. of Dev.

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This paper addresses the application of artificial intelligence (AI) techniques for the classification of faults in wind turbine. Wind Energy Conversion Systems have become a focal point in the research of renewable energy sources. Reliability of wind turbine is critical to extract this maximum amount of energy from the wind. Many condition monitoring techniques that are based on steady-state analysis are being applied to wind generators. Bearing faults in this generator causes mechanical vibrations and the variations in the air gap density. The air gap flux density is modulated and currents are generated at different frequencies related to the mechanical vibrations. Characteristic bearing frequencies are associated with the physical construction of bearing and its failure mode. Classical spectral analysis using the Fourier transform was used to detect different bearing failure modes. The magnitudes of the frequencies components formed by the bearing defect are small compared to the rest of the current spectrum. This large difference in magnitude makes difficult the detection and classification of bearing faults. In this paper, we show that the utilization of different approaches of artificial intelligence such as neuronal network (NN) and support vector machines (SVM) can be discriminate different failure modes and gives a good basis for an automatic and non-invasive condition monitoring for wind turbine.

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Section: Bearing Faults and Experimental Setupmentioning

confidence: 99%

Comparative study between two artificial intelligence techniques (NN and SVM) applied in fault diagnosis of Wind Turbine

Tafinine

2024

S. F. J. of Dev.

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An FBG-based method for load measurement of a cylindrical rolling element bearing

Wang,

Liang,

2024

Meas. Sci. Technol.

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Contact forces between raceways and rolling elements of a bearing stand as crucial operational aspects defining the bearing's performance. While monitoring bearing load through load cells or strain gauges on the shaft or housing is feasible, it may not precisely reflect the distributed load transmitted by the rollers directly to the raceways. This paper introduces a method to calculate these contact forces, relying on a linear assumption correlating the loads to the measured strains on the outer race of a bearing. In our research, we conducted both static and dynamic tests on cylindrical roller bearings through simulations and experiments. We designed an experimental test rig to conduct both static and dynamic experiments and utilized FBG optical fiber sensors for contact force measurement in bearings due to their multiple advantages, including high sensitivity, resistance to corrosion and magnetic interference, and ease of installation on the bearing outer race due to their shape and size. Our findings indicate a consistent linear relationship between contact forces and strains measured on the bearing's outer race. Furthermore, we calculated the linear coefficient K values from three test groups: static tests under simulation study, static tests under experimental study, and dynamic tests under experimental study. The K values obtained from static tests (simulation and experimental studies) and dynamic tests (experimental study) align consistently. Following this, we calculated contact forces in both static and dynamic experiments by multiplying the measured strains with K. Our calculations resulted in an error percentage of less than 2% for static tests and below 5% for dynamic tests, highlighting the accuracy of our approach in determining these contact forces.

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On-line vibration monitoring of bearing faults in induction machine usin Cyclic Spectral Analysis

Cited by 2 publications

References 15 publications

Comparative study between two artificial intelligence techniques (NN and SVM) applied in fault diagnosis of Wind Turbine

Comparative study between two artificial intelligence techniques (NN and SVM) applied in fault diagnosis of Wind Turbine

An FBG-based method for load measurement of a cylindrical rolling element bearing

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