Presented in this paper is the development of a vibration-based novelty detection algorithm for locating and identifying valve wear within industrial reciprocating compressors through the combined use of time-frequency analysis, image-based pattern recognition, and one-class support vector machines. A commonly reported cause of valve wear-related machine downtime is wear in the valve seat, causing a change in flow profile into and out of the compression chamber. Seeded faults are introduced into the valve manifolds of the ESH-1 industrial compressor and vibration data collected and separated into individual crank cycles before being analyzed using time-frequency analysis. The result is processed as an image and features used for classification are extracted using 1st and 2nd order images statistics and shape factors. A one-class support vector machine learning algorithm is then trained using data collected during healthy operation and then used to both detect and locate anomalous valve behavior with a greater than 82% success rate.