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

Chong Zhang, Pin Lim, A. K. Qin, Kay Chen Tan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

638 Citations (Scopus)


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.

Original languageEnglish
Article number7508982
Pages (from-to)2306-2318
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number10
Publication statusPublished - Oct 2017
Externally publishedYes


  • Deep belief network (DBN)
  • ensemble learning
  • evolutionary algorithm (EA)
  • multiobjective
  • prognostics

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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