Deep Belief Networks Ensemble with Multi-objective Optimization for Failure Diagnosis

Early diagnosis that can detect faults from some symptoms accurately is critical, because it provides the potential benefits such as reducing maintenance costs, improving productivity and avoiding serious damages. Degradation pattern classification for early diagnosis has not been explored in many researches yet. This paper will use hybrid ensemble model for degradation pattern classification. Supervised training of deep models (e.g. many-layered Neural Nets) is difficult for optimization problem with unlabeled datasets or insufficient data sample. Shallow models (SVMs, Neural Networks, etc ...) are unlikely candidates for learning high-level abstractions, since they are affected by the curse of dimensionality. Therefore, deep learning network (DBN), an unsupervised learning model, in diagnosis problem has been investigated to do classification. Few researches have been done for exploring the effects of DBN in diagnosis. In this paper, an ensemble of DBNs with MOEA/D has been applied for diagnosis to handle failure degradation with multivariate sensory data. Turbofan engine degradation dataset is employed to demonstrate the efficacy of the proposed model. We believe that deep learning with multi-objective ensemble for degradation pattern classification can shed new light on failure diagnosis, and our work presented the applicability of this method to diagnosis as well as prognostics.