This paper presents a novel data-driven approach for detecting cracks in reciprocating compressor valves by analyzing vibration data. The main idea is that the time-frequency representation will show typical patterns, depending on the fault state and other variables. The problem of detecting these patterns reliably is solved by taking a detour via two dimensional autocorrelation. This emphasizes the patterns and reduces noise effects, thus identifying appropriate features becomes easier. The features are then classified using well known pattern recognition approaches. The methods performance is validated by analyzing real world measurement data.

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