  Despite the great progress of neural network-based (NN-based) machinery fault
diagnosis methods, their robustness has been largely neglected, for they can be
easily fooled through adding imperceptible perturbation to the input. For fault
diagnosis problems, in this paper, we reformulate various adversarial attacks
and intensively investigate them under untargeted and targeted conditions.
Experimental results on six typical NN-based models show that accuracies of the
models are greatly reduced by adding small perturbations. We further propose a
simple, efficient and universal scheme to protect the victim models. This work
provides an in-depth look at adversarial examples of machinery vibration
signals for developing protection methods against adversarial attack and
improving the robustness of NN-based models.
