Gearbox fault detection method under unseen working conditions and damage

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In the field of industrial maintenance, early detection of gearbox faults is crucial, yet challenging, especially under varying and unseen operating conditions and types of damage. Traditional gear fault detection methods are typically designed for known operating conditions. However, in real industrial scenarios, gear fault detection is always an open-set detection, in which working conditions are commonly unseen. To overcome these limitations, this study introduces the ResNetAutoencoder Fault Detection (RAFD) method. RAFD integrates a short-time Fourier transform with a pre-trained ResNet-18 model for feature extraction and an autoencoder for feature reconstruction, trained on normal operational data. Moreover, a similarity loss is introduced to minimize the distribution gap of different working conditions. The effectiveness of the method is evaluated against traditional autoencoder and one-class support vector machine across various operational conditions, with performance metrics including AUROC, precision, recall, and F1 -score. The results demonstrate RAFD's superior performance in both in-distribution and out-of-distribution tasks, underscoring its robustness and reliability in detecting normal and faulty gearbox states.