Current preventative maintenance paradigms in nuclear power plants carry several costly risks and challenges associated with component downtime and the need for human data collection. Preventative maintenance may be enabled by an online monitoring system that accurately assesses component condition and identifies potential faults. We present an approach for autonomous online monitoring and multiagent planning for robotic data collection. Under the occurrence of a fault, we utilize a machine learning model to form an initial guess of its nature, which we then refine by selectively measuring certain variables to gain additional information via a situation-aware variable selection model. To generate a multi-robot plan to conduct these measurements, we develop a preference-based planning framework within a linear temporal logic–based planning approach that prioritizes collecting data from the most important features. Finally, we demonstrate our approach on a case study using a simulated nuclear power plant circulating water system, showing fault diagnostic performance as well as simulated robot data collection.