In manufacturing, unexpected failures are considered a primary operational
risk, as they can hinder productivity and can incur huge losses.
State-of-the-art Prognostics and Health Management (PHM) systems incorporate
Deep Learning (DL) algorithms and Internet of Things (IoT) devices to ascertain
the health status of equipment, and thus reduce the downtime, maintenance cost
and increase the productivity. Unfortunately, IoT sensors and DL algorithms,
both are vulnerable to cyber attacks, and hence pose a significant threat to
PHM systems. In this paper, we adopt the adversarial example crafting
techniques from the computer vision domain and apply them to the PHM domain.
Specifically, we craft adversarial examples using the Fast Gradient Sign Method
(FGSM) and Basic Iterative Method (BIM) and apply them on the Long Short-Term
Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network
(CNN) based PHM models. We evaluate the impact of adversarial attacks using
NASA's turbofan engine dataset. The obtained results show that all the
evaluated PHM models are vulnerable to adversarial attacks and can cause a
serious defect in the remaining useful life estimation. The obtained results
also show that the crafted adversarial examples are highly transferable and may
cause significant damages to PHM systems.

通过对 Prognostics and Health Management (PHM) 系统进行敌对样本攻击测试，本论文发现 IoT 传感器和 DL 算法都容易受到攻击，这可能导致 PHM 系统出现严重缺陷，从而减少设备的使用寿命。