Enhancing Aerospace Fault Diagnosis With Conditioned Multiscale Generative Adversarial Networks

In the aerospace field, equipment failures can lead to substantial economic losses and pose significant safety risks, making effective fault diagnosis crucial. Traditional fault diagnosis methods typically require large, precisely labeled datasets, which are challenging to obtain in aerospace applications due to the rarity and unpredictability of faults. To overcome these limitations, this article proposes a novel conditioned multiscale generative adversarial networks (GANs) approach designed to enhance fault diagnosis performance under small-sample conditions. Initially, raw vibration signals undergo preprocessing using the short-time Fourier transform, which expands frequency-domain features while preserving essential time-frequency characteristics. Subsequently, conditioned multiscale GANs are trained on these limited datasets, employing multiscale convolutional kernels to extract and fuse rich features, thus generating high-quality synthetic samples. Finally, these synthetic samples are combined with the original dataset to train a convolutional neural network offline, which can subsequently perform real-time online fault diagnosis. Extensive validation on two aerospace-related datasets demonstrates that the proposed method significantly enhances fault diagnosis accuracy and efficiency, even when the available training data is severely limited.