The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
Abstract
In numerous industrial applications where safety, efficiency, and reliability are among primary concerns, condition-based maintenance (CBM) is often the most effective and reliable maintenance policy. Prognostics, as one of the key enablers of CBM, involves the core task of estimating the remaining useful life (RUL) of the system. Neural networks-based approaches have produced promising results on RUL estimation, although their performances are influenced by handcrafted features and manually specified parameters. In this paper, we propose a multiobjective deep belief networks ensemble (MODBNE) method. MODBNE employs a multiobjective evolutionary algorithm integrated with the traditional DBN training technique to evolve multiple DBNs simultaneously subject to accuracy and diversity as two conflicting objectives. The eventually evolved DBNs are combined to establish an ensemble model used for RUL estimation, where combination weights are optimized via a single-objective differential evolution algorithm using a task-oriented objective function. We evaluate the proposed method on several prognostic benchmarking data sets and also compare it with some existing approaches. Experimental results demonstrate the superiority of our proposed method.
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Deep learning and its applications to machine health monitoring
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2018.05.050 · Cited by 2,894
A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings
Wang, B., Lei, Y., Li, N., & Li, N. (2020) · IEEE Transactions on Reliability · doi:10.1109/tr.2018.2882682 · Cited by 1,917
Remaining useful life estimation in prognostics using deep convolution neural networks
Li, X., Ding, Q., & Sun, J.-Q. (2018) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2017.11.021 · Cited by 1,755
A review on the application of deep learning in system health management
Khan, S., & Yairi, T. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.024 · Cited by 1,133
Listou Ellefsen, A., Bjørlykhaug, E., Æsøy, V., Ushakov, S., & Zhang, H. (2019) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2018.11.027 · Cited by 523
Metadata
- Authors
- Zhang, C., Lim, P., Qin, A. K., & Tan, K. C.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2017-10
- Volume
- 28
- Issue
- 10
- Pages
- 2306-2318
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W2471161958
- Cited by
- 870
- References
- 76
- FWCI
- 35.63
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, C., Lim, P., Qin, A. K., & Tan, K. C. (2017). Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2016.2582798
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