Generative Adversarial Networks for Fault Diagnosis in Machinery

Generative Adversarial Networks (GANs) have emerged as a powerful data‐driven paradigm in machinery health monitoring, offering robust strategies to overcome inherent challenges in fault diagnosis such as limited and imbalanced datasets. At their core, GANs consist of a generator network that synthesises realistic fault signals and a discriminator network that distinguishes synthetic from real data. Through adversarial training, GANs learn complex distributions of vibration signatures or other condition‐monitoring signals, facilitating data augmentation, rare‐fault simulation and domain adaptation across varied operating regimes. This approach enhances the feature space available for downstream classifiers, leading to improved detection and classification of incipient and intermittent faults in rotating components, gearboxes and bearings. Recent advancements integrate Wasserstein distance metrics and gradient penalty regularisation to stabilise training, while multisensor fusion and conditional architectures enable the generation of context‐aware and high‐fidelity samples. By reducing reliance on extensive labelled datasets, GAN‐based methods support predictive maintenance strategies across global industries, minimising unplanned downtime and optimising asset lifecycles.

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Generative Adversarial Networks for Fault Diagnosis in Machinery publication trend

The graph below shows the total number of articles in generative adversarial networks for fault diagnosis in machinery across all publications each year (not limited to Nature Index journals).

Technical terms

Generative Adversarial Network (GAN): A deep learning framework comprising two adversarial networks—a generator that creates synthetic samples and a discriminator that evaluates their authenticity.

Wasserstein GAN: A GAN variant that minimises the Wasserstein distance between real and generated data distributions, enhancing training stability.

Gradient penalty: A regularisation technique enforcing Lipschitz continuity by penalising the norm of discriminator gradients.

Multisensor fusion: The process of combining outputs from multiple sensors into a unified representation to improve diagnostic accuracy.

Data augmentation: The generation of additional training examples via synthetic transformations to address data scarcity and imbalance.

References

1. Rotating Machinery Fault Diagnosis with Limited Multisensor Fusion Samples by Fused Attention-Guided Wasserstein GAN. Symmetry (2024).
2. Fault Diagnosis Method for Imbalanced Data Based on Multi-Signal Fusion and Improved Deep Convolution Generative Adversarial Network. Sensors (2023).
3. Imbalanced Fault Diagnosis of Rolling Bearing Using Data Synthesis Based on Multi-Resolution Fusion Generative Adversarial Networks. Machines (2022).