Fault detection on fluid machinery using Hidden Markov Models. (February 2020)

• Record Type: Journal Article
• Authors: Arpaia, P.; Cesaro, U.; Chadli, M.; Coppier, H.; De Vito, L.; Esposito, A.; Gargiulo, F.; Pezzetti, M.
• Is Part Of: Measurement. Volume 151(2020)
• Publication Date: 2020-02
• Subjects: Fluid machinery -- Fault diagnostics -- Fault detection -- Hidden Markov Models; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.107126
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Hidden Markov Models are employed to build a fault detection model. The model is trained by merely exploiting data from normal machine operation. Multiple physical quantities are measured on the fluid machinery for the detection model building. The model is validated by simulating a fault of the machine by properly corrupting data. 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.