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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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 28; H. 10; S. 2306 - 2318
Hauptverfasser:	Chong Zhang, Pin Lim, Qin, A. K., Kay Chen Tan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.10.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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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.
AbstractList	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.
Author	Pin Lim Kay Chen Tan Qin, A. K. Chong Zhang
Author_xml	– sequence: 1 surname: Chong Zhang fullname: Chong Zhang email: zhangchong@u.nus.edu organization: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore – sequence: 2 surname: Pin Lim fullname: Pin Lim email: pin.lim@rolls-royce.com organization: Adv. Technol. Center of Rolls Royce Singapore, Singapore, Singapore – sequence: 3 givenname: A. K. surname: Qin fullname: Qin, A. K. email: kai.qin@rmit.edu.au organization: Sch. of Sci., RMIT Univ., Melbourne, VIC, Australia – sequence: 4 surname: Kay Chen Tan fullname: Kay Chen Tan email: eletankc@nus.edu.sg organization: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore, Singapore
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/27416606$$D View this record in MEDLINE/PubMed
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SubjectTerms	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
Title	Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
URI	https://ieeexplore.ieee.org/document/7508982 https://www.ncbi.nlm.nih.gov/pubmed/27416606 https://www.proquest.com/docview/2174451401 https://www.proquest.com/docview/1826718541
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