Pareto-Optimal Adaptive Loss Residual Shrinkage Network for Imbalanced Fault Diagnostics of Machines

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 4; pp. 2233 - 2243
• Main Authors: Yu, Yaoxiang, Guo, Liang, Gao, Hongli, Liu, Yuekai, Feng, Tingting
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive loss function; Adaptive systems; Convolution; Datasets; Fault diagnosis; Feature extraction; imbalanced data; Industrial applications; Machine learning; Milling (machining); multiobjective genetic algorithm (GA); Neural networks; Optimization; Pareto front; Pareto optimum; residual shrinkage network; Shrinkage; Training
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2021.3094186

In the industrial applications of mechanical fault diagnosis, machines work in normal condition at most time. In other words, most of the collected datasets are highly imbalanced. Although deep learning has been widely applied in intelligent diagnosis, it is unsuitable for such imbalanced situation. In addition, few studies attempted to determine the parameters in the diagnosis models. For solving such problems, Pareto-optimal adaptive loss residual shrinkage network (PALRSN) is proposed. First, a fixed length-based encoding method is implemented to represent the candidate architectures of PALRSN. Then, multiply accumulate operations and Gmean value representing the model complexity and identification performance, respectively, on imbalanced datasets are selected as the optimization targets to search for the optimal PALRSN architecture. In the training process, an adaptive loss function assigns different misclassification costs on all categories according to their number discrepancy to highlight the minority samples. The proposed method is validated by bearing data and milling cutter data with different imbalanced ratio. The experimental results demonstrate that such approach outperforms the state-of-the-art methods in imbalanced classification.