  The state-of-the-art predictive maintenance (PdM) techniques have shown great
success in reducing maintenance costs and downtime of complicated machines
while increasing overall productivity through extensive utilization of
Internet-of-Things (IoT) and Deep Learning (DL). Unfortunately, IoT sensors and
DL algorithms are both prone to cyber-attacks. For instance, DL algorithms are
known for their susceptibility to adversarial examples. Such adversarial
attacks are vastly under-explored in the PdM domain. This is because the
adversarial attacks in the computer vision domain for classification tasks
cannot be directly applied to the PdM domain for multivariate time series (MTS)
regression tasks. In this work, we propose an end-to-end methodology to design
adversarially robust PdM systems by extensively analyzing the effect of
different types of adversarial attacks and proposing a novel adversarial
defense technique for DL-enabled PdM models. First, we propose novel MTS
Projected Gradient Descent (PGD) and MTS PGD with random restarts (PGD_r)
attacks. Then, we evaluate the impact of MTS PGD and PGD_r along with MTS Fast
Gradient Sign Method (FGSM) and MTS Basic Iterative Method (BIM) on Long
Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural
Network (CNN), and Bi-directional LSTM based PdM system. Our results using
NASA's turbofan engine dataset show that adversarial attacks can cause a severe
defect (up to 11X) in the RUL prediction, outperforming the effectiveness of
the state-of-the-art PdM attacks by 3X. Furthermore, we present a novel
approximate adversarial training method to defend against adversarial attacks.
We observe that approximate adversarial training can significantly improve the
robustness of PdM models (up to 54X) and outperforms the state-of-the-art PdM
defense methods by offering 3X more robustness.
