dor_id: 4110219

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336.#.#.3: Artículo de Investigación

336.#.#.a: Artículo

351.#.#.6: https://jart.icat.unam.mx/index.php/jart

351.#.#.b: Journal of Applied Research and Technology

351.#.#.a: Artículos

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856.4.0.u: https://jart.icat.unam.mx/index.php/jart/article/view/805/738

100.1.#.a: Arun, P.; Lincon, S. Abraham; Prabhakaran, N.

524.#.#.a: Arun, P., et al. (2019). An Automated method for the analysis of bearing vibration based on spectrogram pattern matching. Journal of Applied Research and Technology; Vol. 17 Núm. 2. Recuperado de https://repositorio.unam.mx/contenidos/4110219

245.1.0.a: An Automated method for the analysis of bearing vibration based on spectrogram pattern matching

502.#.#.c: Universidad Nacional Autónoma de México

561.1.#.a: Instituto de Ciencias Aplicadas y Tecnología, UNAM

264.#.0.c: 2019

264.#.1.c: 2019-10-30

653.#.#.a: Bearing fault; pattern matching; spectrogram; structural similarity index metric; vibration

506.1.#.a: La titularidad de los derechos patrimoniales de esta obra pertenece a las instituciones editoras. Su uso se rige por una licencia Creative Commons BY-NC-SA 4.0 Internacional, https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es, para un uso diferente consultar al responsable jurídico del repositorio por medio del correo electrónico gabriel.ascanio@icat.unam.mx

884.#.#.k: https://jart.icat.unam.mx/index.php/jart/article/view/805

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041.#.7.h: eng

520.3.#.a: As a mean for non-intrusive inspection of bearing systems, the scope of predicting their condition from the acoustic vibrations liberated during their operation, utilizing signal processing methods, has been of extensive research, over decades. Vibration being highly non-stationary, time domain as well as spectral features cannot characterize its behavior. Even though spectrogram is a time-frequency domain feature extraction technique, its interpretation is tedious and perhaps, subjective. In the proposed method, the spectrogram images of the normal vibration data is compared with that of the contextual vibration, using Structural Similarity Index Metric (SSIM). It is hypothesized that the pattern similarity between the contextual spectrogram and the baseline is low when the bearing is faulty. The SSIM between the spectrogram image of normal bearing vibration data and the baseline is different from those between the baseline and vibration data corresponding to Inner Race Failure (IRF), Roller Element Defect (RED) and Outer Race Failure (ORF). Via the proposed method of spectrogram pattern matching based on SSIM, the subjectivity in the comparative interpretation of spectrogram is eliminated fully. The SSIM corresponding to the vibrations acquired from the normal and faulty bearings differ with a P value of 4.43693x 10-16. The technique can distinguish defective bearings with, 95.74% sensitivity, 96% accuracy and 100% specificity, without dismantling or open intervention

773.1.#.t: Journal of Applied Research and Technology; Vol. 17 Núm. 2

773.1.#.o: https://jart.icat.unam.mx/index.php/jart

022.#.#.a: ISSN electrónico: 2448-6736; ISSN: 1665-6423

310.#.#.a: Bimestral

264.#.1.b: Instituto de Ciencias Aplicadas y Tecnología, UNAM

doi: https://doi.org/10.22201/icat.16656423.2019.17.2.805

harvesting_date: 2023-11-08 13:10:00.0

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file_creator: Mónica Aparicio Estrada

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Artículo

An Automated method for the analysis of bearing vibration based on spectrogram pattern matching

Arun, P.; Lincon, S. Abraham; Prabhakaran, N.

Instituto de Ciencias Aplicadas y Tecnología, UNAM, publicado en Journal of Applied Research and Technology, y cosechado de Revistas UNAM

Licencia de uso

Procedencia del contenido

Cita

Arun, P., et al. (2019). An Automated method for the analysis of bearing vibration based on spectrogram pattern matching. Journal of Applied Research and Technology; Vol. 17 Núm. 2. Recuperado de https://repositorio.unam.mx/contenidos/4110219

Descripción del recurso

Autor(es)
Arun, P.; Lincon, S. Abraham; Prabhakaran, N.
Tipo
Artículo de Investigación
Área del conocimiento
Ingenierías
Título
An Automated method for the analysis of bearing vibration based on spectrogram pattern matching
Fecha
2019-10-30
Resumen
As a mean for non-intrusive inspection of bearing systems, the scope of predicting their condition from the acoustic vibrations liberated during their operation, utilizing signal processing methods, has been of extensive research, over decades. Vibration being highly non-stationary, time domain as well as spectral features cannot characterize its behavior. Even though spectrogram is a time-frequency domain feature extraction technique, its interpretation is tedious and perhaps, subjective. In the proposed method, the spectrogram images of the normal vibration data is compared with that of the contextual vibration, using Structural Similarity Index Metric (SSIM). It is hypothesized that the pattern similarity between the contextual spectrogram and the baseline is low when the bearing is faulty. The SSIM between the spectrogram image of normal bearing vibration data and the baseline is different from those between the baseline and vibration data corresponding to Inner Race Failure (IRF), Roller Element Defect (RED) and Outer Race Failure (ORF). Via the proposed method of spectrogram pattern matching based on SSIM, the subjectivity in the comparative interpretation of spectrogram is eliminated fully. The SSIM corresponding to the vibrations acquired from the normal and faulty bearings differ with a P value of 4.43693x 10-16. The technique can distinguish defective bearings with, 95.74% sensitivity, 96% accuracy and 100% specificity, without dismantling or open intervention
Tema
Bearing fault; pattern matching; spectrogram; structural similarity index metric; vibration
Idioma
eng
ISSN
ISSN electrónico: 2448-6736; ISSN: 1665-6423

Enlaces