About the Experience in Operation of Reciprocating Compressors Under Control of the Vibration Monitoring System

Author:

Kostyukov V.N.,Naumenko A.P.

Publisher

Elsevier BV

Subject

Applied Mathematics

Reference15 articles.

1. The Piston Compressor: The Methodology of the Real-Time Condition Monitoring;Kostyukov;Journal of Physics: Conference Series,2012

2. V.N. Kostyukov, A.P. Naumenko, Condition monitoring of reciprocating machines. COMADEM 2009 (Condition Monitoring and Diagnostic Engineering Management): Proceedings of the 22rd International Congress on Condition Monitoring and Diagnostic Engineering Management. Spain, San Sebastián, 2009, pp. 113-120.

3. A.P. Naumenko, Real-time condition monitoring of reciprocating machines. 6th international conference on condition monitoring and machinery failure prevention technologies CM2009/MFPT2009: materials of a conference. Irish, Dublin, 2009, 1202-1213.

4. V.N. Kostyukov, A.P. Naumenko, Regulatory and methodological support of piston compressors vibration analysis monitoring. COMADEM 2013 (Condition Monitoring and Diagnostic Engineering Management): Proceedings of the 26th International Congress on Condition Monitoring and Diagnostic Engineering Management (June 11-12, 2013). Finland, Helsinki, 2013, 7 p.

5. G OST R 56233-2014. Condition monitoring and diagnostics of machines. Hazardous equipment monitoring. Vibration generated by land-based reciprocating compressors.

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1. Fault diagnosis of reciprocating compressor based on the prediction of comprehensive index extracted from the expansion process in indicator diagram;Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering;2023-03-23

2. A new method for reciprocating compressor fault diagnosis based on indicator diagram feature extraction;Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy;2023-03-15

3. Application of state parameter learning for fault diagnosis on the large reciprocating compressor;Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering;2023-01-09

4. Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review;Processes;2021-05-21

5. Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor;Neurocomputing;2020-03

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