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