This paper investigates a variety of signal-monitoring and data-driven processing techniques to classify seed faults imposed on floating ring main crankshaft compressor bearings. Simulated main bearing shaft motion using an adaptation of the mobility method is first applied to demonstrate the plausibility of the method. Condition monitoring for three different fault types is experimentally investigated through seeded fault testing. A novel method for feature extraction utilizes a fast Fourier frequency-domain transformation coupled with a binning method that uses information across the entire frequency range. A principal component transformation process is then applied to reduce the dimension of the frequency-based feature vector to a small set of generalized features. A Bayesian classifier on the generalized features designed through seeded fault training data is shown to have excellent classifier performance across all fault types. A single-axis position measurement of the crankshaft shows the most promising results compared to a traditional accelerometer on the bearing housing and a novel accelerometer on the crankshaft. The single-axis measurement provides a cost-efficient alternative method to the two-axis orbit measurement typically used for traditional journal bearings.