This paper presents a novel data-driven approach for detecting broken reciprocating compressor valves that is based on the idea that a broken valve will affect the shape of the pressure-volume (pV) diagram. This effect can be observed when the valves are closed. To avoid disturbances due to the load control we concentrate on the expansion phase linearized using the logarithmic pV diagram. The gradient of the expansion phase serves as an indicator of the fault state of the valves. Since the gradient is also affected by the pressure conditions, they are used as an additional indicator. After feature extraction and removing offset in the feature space by solving an optimization problem, classification of different valve types can be achieved with one support vector machine classifer. The performance of the method was validated by analyzing real-world measurement data. Our results show a very high classification accuracy for varying compressor load and pressure conditions.

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