Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics

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...

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Vydané v:IEEE transaction on neural networks and learning systems Ročník 28; číslo 10; s. 2306 - 2318
Hlavní autori: Chong Zhang, Pin Lim, Qin, A. K., Kay Chen Tan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.10.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial neural networks
Belief networks
Benchmark testing
Deep belief network (DBN)
Degradation
ensemble learning
Estimation
evolutionary algorithm (EA)
Evolutionary algorithms
Industrial applications
Maintenance engineering
multiobjective
Multiple objective analysis
Neural networks
Objective function
prognostics
Reliability
Reliability aspects
Training
Useful life
ISSN:2162-237X, 2162-2388
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Shrnutí: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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ISSN:2162-237X
2162-2388
DOI:10.1109/TNNLS.2016.2582798