Location and Evaluation of Bearings Defects by Vibration Analysis and Neural Networks

Boukhobza, M. E.; Derouiche, Z.; Ahmed‐Foitih, Zoubir

doi:10.5755/j01.mech.19.4.5051

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…It is worth noting that the time-domain indicators are independent of the rotational speed when the load is non-rotating. [12] and they are calculated from vibration signals directly without any temporal frequency calculations which reduces computation time making it more easily adoptable in industry because the simplicity of its application [23].…”

Section: Relevant Feature Identificationmentioning

confidence: 99%

“…These features are non-dimensional magnitudes, so they are immune from weaknesses in the data process due to quality of the sensors or the location where they are mounted [17]. [23] found that the (RMS), crest factor and the kurtosis give a reasonably global defect indication. And according to [22], it can be considered that slight degradation has emerged when these three features values are beyond their threshold.…”

Section: Validationmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

Chalouli

Berrached

Denaï

2017

J Fail. Anal. and Preven.

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Section: Relevant Feature Identificationmentioning

confidence: 99%

Section: Validationmentioning

confidence: 99%

Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

Chalouli

Berrached

Denaï

2017

J Fail. Anal. and Preven.

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Fault feature and diagnostic method of bending micro- deformation of crankshaft of piston engine

Gao

Song

Chen

et al. 2019

Nondestructive Testing and Evaluation

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Bearing faults diagnostic in permanent magnets synchronous motor

Ferradj,

Bouchetata,

Benouzza

2024

SEES

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With the widespread use of MSAPs in fields such as robotics, automotive, aerospace, medical equipment, renewable energy, etc., the preventive diagnosis of faults affecting these types of motors has become essential to avoid unplanned shutdowns that could lead to considerable financial losses. Even though the permanent magnet synchronous motor is well known for its robustness and efficiency, however it suffers from electrical or mechanical faults. Spectral analysis has become a very important tool for the detection of these faults and we will use this method to determine the frequencies characterizing the fault in MSAPs. It is important to note that the exact distribution of failures can vary depending on the specific motor and its operating conditions. The characteristic frequencies of the bearing failure are a function of the geometric parameters of the bearing and the motor rotation frequency. Unlike the induction motor, the rotation frequency of the PMSM is fixe, which makes it easier to locate the characteristic frequencies of the bearing fault. This work presents the application of spectral analysis for the diagnosis of the bearing defect and more specifically, that of the outer ring. The experimental results obtained show the interest and effectiveness of spectral analysis for the detection of bearing defects.

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Location and Evaluation of Bearings Defects by Vibration Analysis and Neural Networks

Cited by 3 publications

References 5 publications

Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

Intelligent Health Monitoring of Machine Bearings Based on Feature Extraction

Fault feature and diagnostic method of bending micro- deformation of crankshaft of piston engine

Bearing faults diagnostic in permanent magnets synchronous motor

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