<abstract xmlns="http://www.w3.org/1999/xhtml"><p>A new approach to achieve fault diagnosis and prognosis of bearing based on hidden Markov model (HMM) with multi-features is proposed. Firstly, the time domain, frequency domain, and wavelet packet decomposition are utilized to extract the condition features of bearing vibration signals, and the PCA method is merged into multi-features to reduce their dimensionality. Then the low-dimensional features are processed to obtain the scalar probabilities of each bearing condition, which are multiplied to generate the observed values of HMM. The results reveal that the established approach can well diagnose fault conditions and achieve the remaining life estimation of bearing.</p></abstract>

A new approach to achieve fault diagnosis and prognosis of bearing based on hidden Markov model (HMM) with multi-features is proposed. Firstly, the time domain, frequency domain, and wavelet packet decomposition are utilized to extract the condition features of bearing vibration signals, and the PCA method is merged into multi-features to reduce their dimensionality. Then the low-dimensional features are processed to obtain the scalar probabilities of each bearing condition, which are multiplied to generate the observed values of HMM. The results reveal that the established approach can well diagnose fault conditions and achieve the remaining life estimation of bearing.

Fault Diagnosis and Prognosis of Bearing Based on Hidden Markov Model with Multi-Features

School of Water Conservancy and Hydropower

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

A new approach to achieve fault diagnosis and prognosis of bearing based on hidden Markov model (HMM) with multi-features is proposed. Firstly, the time...

Fault Diagnosis and Prognosis of Bearing Based on Hidden Markov Model with Multi-Features

Online veröffentlicht: 30. März 2020

Seitenbereich: 71 - 84

Eingereicht: 05. Dez. 2019

Akzeptiert: 14. Jan. 2020

DOI: https://doi.org/10.2478/amns.2020.1.00008

Schlüsselwörter
hidden Markov model, fault diagnosis, prognosis, multi-features, wavelet packet

© 2020 Weiguo Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fault Diagnosis and Prognosis of Bearing Based on Hidden Markov Model with Multi-Features

Online veröffentlicht: 30. März 2020

Seitenbereich: 71 - 84

Eingereicht: 05. Dez. 2019

Akzeptiert: 14. Jan. 2020

DOI: https://doi.org/10.2478/amns.2020.1.00008

Schlüsselwörterhidden Markov model, fault diagnosis, prognosis, multi-features, wavelet packet

© 2020 Weiguo Zhao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
hidden Markov model, fault diagnosis, prognosis, multi-features, wavelet packet