  Condition-based maintenance (CBM) strategies exploit machine learning models
to assess the health status of systems based on the collected data from the
physical environment, while machine learning models are vulnerable to
adversarial attacks. A malicious adversary can manipulate the collected data to
deceive the machine learning model and affect the CBM system's performance.
Adversarial machine learning techniques introduced in the computer vision
domain can be used to make stealthy attacks on CBM systems by adding
perturbation to data to confuse trained models. The stealthy nature causes
difficulty and delay in detection of the attacks. In this paper, adversarial
machine learning in the domain of CBM is introduced. A case study shows how
adversarial machine learning can be used to attack CBM capabilities.
Adversarial samples are crafted using the Fast Gradient Sign method, and the
performance of a CBM system under attack is investigated. The obtained results
reveal that CBM systems are vulnerable to adversarial machine learning attacks
and defense strategies need to be considered.
