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Multiple Health Phases Based Remaining Useful Lifetime Prediction on Bearings

Perner, Petra

Advances in Data Mining. Applications and Theoretical Aspects, 2016, Vol.9728, p.110-124 [Periódico revisado por pares]

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Título:
Multiple Health Phases Based Remaining Useful Lifetime Prediction on Bearings
Autor: Perner, Petra
Assuntos: Bag of words ; Data mining ; Gaussian mixture model ; Machine bearings ; Multi-variate gaussian distribution ; Remaining useful lifetime
É parte de: Advances in Data Mining. Applications and Theoretical Aspects, 2016, Vol.9728, p.110-124
Descrição: Bearings are key components for all industrial machinery systems. The health status of bearings has great impact on the performance of rotating machineries. Remaining useful lifetime (RUL) estimation on bearings can effectively improve the reliability and availability of industrial machineries. In this paper, a multiple health phases based method is proposed for RUL estimation with application to bearings. Bags of word is brought into the method to model the time-frequency domain features of bearing vibration signals. Besides that, a gaussian mixture model is utilized to model the lifetime of various bearings to build accurate lifetime prediction model. Finally, the experiments demonstrate that the proposed method achieves a good performance comparing with other existing methods.
Títulos relacionados: Lecture Notes in Computer Science
Editor: Switzerland: Springer International Publishing AG
Idioma: Inglês

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