Deep Neural Network Ensemble for the Intelligent Fault Diagnosis of Machines Under Imbalanced Data

• Published in: IEEE access Vol. 8; pp. 120974 - 120982
• Main Authors: Jia, Feng, Li, Shihao, Zuo, Hao, Shen, Jianjun
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Classifiers; Convolution; Convolutional neural networks; Datasets; Deep learning; deep neural network; ensemble learning; Fault diagnosis; Feature extraction; Feature recognition; imbalanced data; Intelligent fault diagnosis; Machine learning; Machinery; Neural networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3006895

Imbalanced classification using deep learning has attracted much attention in intelligent fault diagnosis of machinery. However, the existing methods use individual deep neural network to extract features and recognize the health conditions under imbalanced dataset, which may easily over-fit the mechanical data and affect the diagnosis accuracy. To deal with this problem, this paper takes the advantages of ensemble learning and proposes an ensemble convolutional neural network (EnCNN) for the intelligent fault diagnosis for machines under imbalanced data. In the proposed method, a convolutional neural network with the input of multi-sensor signals is used as the base classifier. Firstly, the mechanical imbalance dataset is first split into balanced training subsets through under-sampling strategy, and each subset is used to train a base classifier. Then the weight coefficients of each trained base classifier are calculated by G-mean score and anomalous base classifiers are screened using classifier selection. Finally, the base classifiers are integrated into EnCNN through weighted voting strategy. The proposed EnCNN is validated by the imbalanced dataset collected from a machinery fault test bench. By comparing with the related methods, the superiority of EnCNN is verified in intelligent fault diagnosis of machines under imbalanced data.