  Deep learning-based time series models are being extensively utilized in
engineering and manufacturing industries for process control and optimization,
asset monitoring, diagnostic and predictive maintenance. These models have
shown great improvement in the prediction of the remaining useful life (RUL) of
industrial equipment but suffer from inherent vulnerability to adversarial
attacks. These attacks can be easily exploited and can lead to catastrophic
failure of critical industrial equipment. In general, different adversarial
perturbations are computed for each instance of the input data. This is,
however, difficult for the attacker to achieve in real time due to higher
computational requirement and lack of uninterrupted access to the input data.
Hence, we present the concept of universal adversarial perturbation, a special
imperceptible noise to fool regression based RUL prediction models. Attackers
can easily utilize universal adversarial perturbations for real-time attack
since continuous access to input data and repetitive computation of adversarial
perturbations are not a prerequisite for the same. We evaluate the effect of
universal adversarial attacks using NASA turbofan engine dataset. We show that
addition of universal adversarial perturbation to any instance of the input
data increases error in the output predicted by the model. To the best of our
knowledge, we are the first to study the effect of the universal adversarial
perturbation on time series regression models. We further demonstrate the
effect of varying the strength of perturbations on RUL prediction models and
found that model accuracy decreases with the increase in perturbation strength
of the universal adversarial attack. We also showcase that universal
adversarial perturbation can be transferred across different models.
