Fault Detection on Fluid Machinery using Hidden Markov Models

Abstract A fault detection method exploiting Hidden Markov Models (HMMs) is proposed for fluid machinery without adequate a priori information about faulty conditions. The method is trained only on data acquired during normal machine operation. For anomaly detection, typical quantities measured in monitoring fluid machines, namely 3-axis acceleration, electric power consumption, temperature, inlet and outlet pressure, are monitored. Principal Component Analysis is exploited for features extraction. Then, data is clustered and an HMM is trained. Finally, the trained model is employed together with a goodness-of-fit test to detect faulty states by processing online data. The method was tested and validated at CERN on screw compressors for cryogenic cooling.

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