Fault Detection of Linear Bearings in Brushless AC Linear Motors by Vibration Analysis

Bianchini, Claudio; Immovilli, Fabio; Cocconcelli, Marco; Rubini, Riccardo; Bellini, A.

doi:10.1109/tie.2010.2098354

Cited by 102 publications

(51 citation statements)

References 40 publications

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“…The outer-race defect of rolling bearings induces a specific vibration frequency as shown below: [2,9] 1 cos 2…”

Section: Characteristic Frequenciesmentioning

confidence: 99%

“…In (1) and (2), the frequencies that are commonly used as a diagnostic measure for bearing fault detection in [2,3,5,9,10], and vary depending on the load conditions. …”

Section: Characteristic Frequenciesmentioning

confidence: 99%

“…For bearing fault detection, various types of sensors and condition-monitoring systems have been employed [2][3][4][5][6][7][8][9]. Vibration measurements are commonly used to identify bearing faults [2,[5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…Vibration measurements are commonly used to identify bearing faults [2,[5][6][7][8][9]. The amplitude at the bearing faultrelated frequency is used as the fault index in the vibration spectrum [2,6,13].…”

Section: Introductionmentioning

confidence: 99%

“…The amplitude at the bearing faultrelated frequency is used as the fault index in the vibration spectrum [2,6,13]. In addition, there are also harmonic frequencies in the vibration spectrum in motors with faulty bearings [3,5,9]. The spectral analysis of vibration signals is performed using the fast Fourier transform (FFT) method, which is the basic tool used together with some other approaches such as machine learning and statistical analysis.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals

Hwang¹,

Youn²,

Sun³

et al. 2015

Journal of Electrical Engineering and Technology

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-In this paper, we propose a new method for detecting bearing faults using vibration signals. The proposed method is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices. Using SVMs, the cross-validations are used for a training process, and a two-stage classification process is used for detecting bearing faults and their status. The proposed approach is applied to outer-race bearing fault detection in threephase squirrel-cage induction motors. The experimental results show that the proposed method can effectively identify the bearing faults and their status, hence improving the accuracy of fault diagnosis.

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“…The outer-race defect of rolling bearings induces a specific vibration frequency as shown below: [2,9] 1 cos 2…”

Section: Characteristic Frequenciesmentioning

confidence: 99%

“…In (1) and (2), the frequencies that are commonly used as a diagnostic measure for bearing fault detection in [2,3,5,9,10], and vary depending on the load conditions. …”

Section: Characteristic Frequenciesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals

Hwang¹,

Youn²,

Sun³

et al. 2015

Journal of Electrical Engineering and Technology

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Fault Diagnosis and Detection

Mahmoud

Xia

2013

Analysis and Synthesis of Fault‐Tolerant Control Systems

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Here he has been involved in research on structural health monitoring and energy harvesting. He currently works at the Department of Mechatronics Engineering, Marmara University, as an Assoc. Prof. Dr. His research interests are fault diagnosis, energy harvesting, intelligent materials, automation systems, structural health monitoring, and artificial intelligence systems. He is the director of Fault Diagnosis and Structural Health Monitoring Lab in Marmara University. He is an editorial board member at many international journals. He has publications in many international journals and symposiums on failure diagnosis and fault detection.Dr. Muhammet Ünal works as an assistant professor in the Department of Mechatronics Engineering, Marmara University, Istanbul (Turkey). His research interests are system identification, parameter optimization, real-time control, and structural health monitoring. His primary research interests are vibration analysis and fault diagnosis of mechanical systems. Dr. Ünal has written several papers in vrious fields of mechatronics engineering and one book on control engineering. He is also the first author of the book Optimization of PID Controllers Using Ant Colony and Genetic Algorithms, which is published in Springer.

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Rolling Ball-Bearing Fault Classification Using Variational Mode Decomposition and Footprint of Hilbert Transform

Dubey¹

2020

Advances in Intelligent Systems and Computing

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Fault Detection of Linear Bearings in Brushless AC Linear Motors by Vibration Analysis

Cited by 102 publications

References 40 publications

Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals

Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals

Fault Diagnosis and Detection

Rolling Ball-Bearing Fault Classification Using Variational Mode Decomposition and Footprint of Hilbert Transform

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