Machinery fault diagnosis with imbalanced data using deep generative adversarial networks

By Wei Zhang, Xiang Li, Xiaoyu Jia, Hui Ma, Zhong Luo, Xu Li. 2020

328 citations recorded by the source corpus.

Abstract Despite the recent advances of intelligent data-driven fault diagnosis methods on rotating machines, balanced training data for different machine health conditions are assumed in most studies. However, the signals in machine faulty states are usually difficult and expensive to collect, resulting in imbalanced training dataset in most cases. That significantly deteriorates the effectiveness of the existing data-driven approaches. This paper proposes a deep learning-based fault diagnosis method to address the imbalanced data problem by explicitly creating additional training data. Generative adversarial networks are firstly used to learn the mapping between the distributions of noise and real machinery temporal vibration data, and additional realistic fake samples can be generated to balance and further expand the available dataset afterwards. Through experiments on two rotating machinery datasets, it is validated that the data-driven methods can significantly benefit from the data augmentation, and the proposed method offers a promising tool on fault diagnosis with imbalanced training data.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/71112070d6356a7290bb81aa2f431c5b6d281356

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Chuang Sun
2. Xuefeng Chen
3. Ruqiang Yan
4. Shibin Wang
5. Zhibin Zhao

In Mechanical Engineering

1. Shibin Wang
2. Chuang Sun
3. Ruqiang Yan
4. Xuefeng Chen
5. Zhibin Zhao

Citations in the corpus

• Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, methodology): “For imbalanced learning, GAN was also used to combine with CNN models to generate new labeled samples in [59, 60].”
• A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “GANs have applications in generating quality faulty state data in machines, as collecting faulty signals is di ffi cult and expensive to collect[254].”