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.
翻译:最先进的预测维护( PdM) 技术在降低复杂机器的维护成本和停机率,同时通过广泛使用 Internet- Things( IoT) 和 Deep Learning( DL) 来提高总体生产率方面表现出了巨大的成功。 不幸的是, IoT 传感器和 DL 算法都容易受到网络攻击。 例如, DL 算法因其易受对抗性实例的影响而为人所知。 这种对抗性攻击在 PdM 域内探索得非常少。 这是因为用于分类任务的计算机视野域的对抗性攻击不能直接适用于多变时间序列( MTS) 的 PdM 域域域。 在此工作中,我们提出一个端到端的方法,通过广泛分析不同类型的对抗性攻击的效果,并为DLLF PdM 模型提供新的对抗性防御技术。 首先,我们提出新的MTF 预测性肝脏( GGDDD) 和MTMMM IM IM 模型(MTMG- GG- GG- Gral-G-MT) 的 方法在GMTMTMTMDMT- GG-G- GG-S- Stated- Stated- Stated- Stated- Stated Stated Stated Stated Stated 上, 上能能能能能能能能能能向它提供一种高得多的变变。