Data-driven building fault detection and diagnostics (FDD) is heavily dependent on sensors. However, common sensors from building automation systems are designed to enable basic building control sequences and are not optimized to maximize accuracy in FDD. Installing additional sensors that provide more detailed building system information is key to maximizing the performance of FDD solutions. In this paper, we present a sensor cost analysis workflow to quantify the economic implications of installing new sensors for FDD using the concept of sensor threshold marginal cost (STMC) in the simulation (EnergyPlus) environment. STMC does not represent actual sensor cost. Rather, it represents a target cost based on the economic benefit that would be realized through improved FDD performance and one or more specified economic criteria. We calculate STMCs for multiple possible fault types and use fault prevalence information to aggregate STMCs into a single dollar value to determine the cost-effectiveness of a potential sensor investment. We conducted a case study using Oak Ridge National Laboratory's Flexible Research Platform (FRP) test facility as a reference. The case study demonstrates the feasibility of the analysis and highlights the key cost considerations in sensor selection for FDD. The concept of STMC is used to evaluate the cost effectiveness of single sensors and sensor groups. The results indicate that non-energy benefits can outweigh energy benefits, depending on how improved comfort and reduced maintenance are valued. Second, while performance improves as the candidate sensor set grows, diminishing returns are likely to make larger sensor sets less cost effective. To improve FDD performance cost effectively, selecting only the few most impactful sensor is critical, and our cost analysis workflow is designed to serve that function.