  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.
