Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling : A method for bearing prognostics

By Hasan Ocak, K. Loparo, F. M. Discenzo. 2007

294 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/6c4e372039f3ce66197cbf40210b3861ac0fa28f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Naipeng Li
2. Yaguo Lei
3. Ningbo Li
4. Noureddine Zerhouni
5. Xuefeng Chen
6. Kamal Medjaher
7. Liang Guo
8. Tao Yan

In Mechanical Engineering

1. Yaguo Lei
2. Naipeng Li
3. Kwok Leung Tsui
4. Noureddine Zerhouni
5. Dong Wang
6. Kamal Medjaher
7. Ningbo Li
8. Xuefeng Chen

Citations in the corpus

• A review on prognostics and health monitoring of proton exchange membrane fuel cell (2017, methodology): “Reinforcement learning approach is also suitable for this strategy [56].”
• Wavelets for fault diagnosis of rotary machines: A review with applications (2014, background): “track bearing wear as well as its severity [125].”
• Remaining Useful Life Estimation of Critical Components With Application to Bearings (2012, methodology): “Furthermore, contrary to regression models or neural networks where the structure is not interpretable [12], the states in the MoG-HMM can be interpreted as the health conditions of the component.”
• Cloud-enabled prognosis for manufacturing (2015, methodology): “Ocak et al. [225] applied wavelet packet decomposition and HMM for bearing prognosis.”
• A Data-Driven Failure Prognostics Method Based on Mixture of Gaussians Hidden Markov Models (2012, methodology): “Furthermore, contrary to other tools used in the framework of data-driven approach, such as the regression models or neural networks where the structure is not interpretable [54], the states in the MoG-HMM can be interpreted as the health conditions of the component.”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “[136] fused multiple features using the hidden Markov model (HMM) and calculated the probabilities of the HMM in the healthy stage as a VHI of bearings.”
• Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018, methodology): “[93] used wavelet packet transform to extract node energies from normal bearing fault signals and then used the node energies to train a hidden Markov model.”
• PRONOSTIA : An experimental platform for bearings accelerated degradation tests. (2012, background): “In the case of machining tools, a milling data set experiments related to milling machine for different speeds, feeds and depth of cut can be found in [6].”
• Remaining useful life estimation - A review on the statistical data driven approaches (2011, background): “Some other useful references on HMMs in failure prognosis and RUL estimation are Ocak et al. (2007), Tai et al.”
• Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings (2015, methodology): “Traditionally, estimation of the lifetime of a bearing is based on the ANSI/AFBMA standard life rating formula [25].”