Journal Of Applied and Computational Sciences in Mechanics

Journal Of Applied and Computational Sciences in Mechanics

Fault Diagnosis of a Reciprocating Compressor Valve Based on Neural Networks

Document Type : Original Article

Authors
Hadi Kalani 1
Mohammad Afkanpour 2
Abutaleb Gholizadeh 2
Ahmad Hajipour 3
1 Mechanical Engineering Department, Sadjad University, Mashhad, Iran.
2 Atlas Petro Poyuseh Paj, Knowledge Based Company, Khorasan Science and Technology Park, Mashhad, Iran
3 Mechanical Engineering Department, Hakim Sabzevari University, Sabzevar, Iran.
10.22067/jacsm.2026.96210.1376
Abstract
Reciprocating compressors are commonly used machinery for industrial applications. Unscheduled downtime and maintenance activity on the compressors causes considerable loss in throughput and efficiency of a plant. Of all the failures that cause unscheduled downtime in reciprocating compressors, valve related causes are predominant. The objective of this paper is to research detection and prediction of valve failures by Multi-layer perceptron neural network, MLPANN, Radial basis function, RBFANN, and Group Method of Data Handling, GMDH analysis of pressure, temperature and vibration signals, which are the most common measurements on a reciprocating compressor system. These parameters are measured on a continuous basis and baselines are established for normal (or acceptable) behavior and failure (or fault) condition. In each model, the optimized structure is obtained through a Genetic algorithm, GA, technique. The results indicated no statistically significant differences among the three models. However, the MLPANN neural network demonstrated superior performance in fault diagnosis.
Keywords
Reciprocating compressors
Fault detection
Vlave
Machine learning
Subjects
  1. Dobrivoje et al., "Modelling Valve Dynamics and Flow in Reciprocating Compressors," 2013.
  2. Zhang et al., "Fault Diagnosis of Reciprocating Compressor Valve Based on Triplet Siamese Neural Network," Machines, vol. 13, no. 4, p. 263, 2025. https://doi.org/10.3390/machines13040263
  3. Medina et al., "Scale-fractal detrended fluctuation analysis for fault diagnosis of a centrifugal pump and a reciprocating compressor," Sensors, vol. 24, no. 2, 2024. https://doi.org/10.3390/s24020461
  4. Bie et al., "Research on fault diagnosis method of reciprocating compressor valve based on IVMD-CMS model," Journal of Mechanical Science and Technology, vol. 37, no. 8, pp. 3931-3943, 2023. https://doi.org/10.1007/s12206-023-0709-x
  5. Wu et al., "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, vol. 237, no. 6, pp. 1337-1347, 2023. https://doi.org/10.1177/09576509231161855
  6. A. Elhaj, Condition monitoring of reciprocating compressor valves. The University of Manchester (United Kingdom), 2005.
  7. Liu et al., "An experimental study on engine dynamics model based in-cylinder pressure estimation," SAE Technical Paper, 0148-7191, 2012. https://doi.org/10.4271/2012-01-0896
  8. V. Tinaut et al., "Misfire and compression fault detection through the energy model, "Mechanical Systems and Signal Processing, vol. 21, no. 3, pp. 1521-1535, 2007. https://doi.org/10.1177/0954407020987077
  9. Ahmed et al., "Fault detection and diagnosis using Principal Component Analysis of vibration data from a reciprocating compressor," in Proceedings of 2012 UKACC International Conference on Control, 2012: IEEE, pp. 461-466. https://doi.org/10.1109/CONTROL.2012.6334674
  10. Potočnik and E. Govekar, "Semi-supervised vibration-based classification and condition monitoring of compressors," Mechanical Systems and Signal Processing, vol. 93, pp. 51-65, 2017. https://doi.org/10.1016/j.ymssp.2017.01.048
  11. Ruiz-Cárcel, V. H. Jaramillo, D. Mba, J. R. Ottewill, and Y. Cao, "Combination of process and vibration data for improved condition monitoring of industrial systems working under variable operating conditions," Mechanical Systems and Signal Processing, vol. 66, pp. 699-714, 2016. https://doi.org/10.1016/j.ymssp.2015.05.018
  12. Sun et al., "Instability detection of centrifugal compressors by means of acoustic measurements," Aerospace Science and Technology, vol. 82, pp. 628-635, 2018. https://doi.org/10.1016/j.ast.2018.09.006
  13. Feng et al., "A recognition and novelty detection approach based on Curvelet transform, nonlinear PCA and SVM with application to indicator diagram diagnosis," Expert Systems with Applications, vol. 38, no. 10, pp. 12721-12729, 2011. https://doi.org/10.1016/j.eswa.2011.04.060
  14. Pichler et al., "Detecting broken reciprocating compressor valves in the pV diagram," in 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2013, pp. 1625-1630. https://doi.org/10.1109/AIM.2013.6584329
  15. Pichler et al., "Detecting cracks in reciprocating compressor valves using pattern recognition in the pV diagram," Pattern Analysis and Applications, vol. 18, pp. 461-472, 2015. https://doi.org/10.1007/s10044-014-0431-5
  16. Wang, L. Song, L. Zhang, and H. Li, "Fault diagnosis for reciprocating air compressor valve using pV indicator diagram and SVM," in 2010 Third International Symposium on Information Science and Engineering, 2010: IEEE, pp. 255-258. https://doi.org/10.1109/ISISE.2010.91
  17. Jiang et al., "Research on reciprocating compressor indicator diagram fault recognition based on support vector machine," Fluid Machinery, vol. 40, pp. 21-25, 2012.
  18. Namdeo et al., "Detection of valve leakage in reciprocating compressor using artificial neural network (ANN)," 2008.
  19. C. Leu et al., "Computer program and method for detecting and predicting valve failure in a reciprocating compressor," ed: Google Patents, 2010.
  20. -Y. Guo et al., "Fault diagnosis of reciprocating compressor valve based on transfer learning convolutional neural network," Mathematical Problems in Engineering, vol. 2021, no. 1, p. 8891424, 2021. https://doi.org/10.1155/2021/8891424
  21. J. Guerra, Condition monitoring of reciprocating compressor valves using analytical and data-driven methodologies. Rochester Institute of Technology, 2013.
  22. Kumar and R. Tiwari, "A review: multiplicative faults and model-based condition monitoring strategies for fault diagnosis in rotary machines," Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 45, no. 5, p. 282, 2023. https://doi.org/10.1007/s40430-023-04203-z
  23. J. Guerra and J. R. Kolodziej, "A data-driven approach for condition monitoring of reciprocating compressor valves," Journal of Engineering for Gas Turbines and Power, vol. 136, no. 4, p. 041601, 2014. https://doi.org/10.1115/1.4025944
  24. Karimi and S. Aliabadi, "Automated Fault Detection of Tri-Lobe Compressor Using Wavelet Transformation and Support Vector Machine," Journal Of Applied and Computational Sciences in Mechanics, vol. 27, no. 1, pp. 19-34, 2016.
  25. Qin et al., "A novel scheme for fault detection of reciprocating compressor valves based on basis pursuit, wave matching and support vector machine," Measurement, vol. 45, no. 5, pp. 897-908, 2012. https://doi.org/10.1016/j.measurement.2012.02.005
  26. Pichler, "Early Fault Detection in Reciprocating Compressor Valves by Means of Vibration and pV Diagram Analysis," in Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications: Springer, 2019, pp. 167-205. https://doi.org/10.1007/978-3-030-05645-2_6
  27. Ahmed, S. Abdusslam, M. Baqqar, F. Gu, and A. D. Ball, "Fault classification of reciprocating compressor based on neural networks and support vector machines," in The 17th International Conference on Automation and Computing, 2011: IEEE, pp. 213-218.
  28. Ahmed et al., "Feature selection and fault classification of reciprocating compressors using a genetic algorithm and a probabilistic neural network," in Journal of Physics: Conference Series, 2011, vol. 305, no. 1: IOP Publishing, p. 012112. https://doi.org/10.1088/1742-6596/305/1/012112
  29. R. Kolodziej and J. N. Trout, "An image-based pattern recognition approach to condition monitoring of reciprocating compressor valves," Journal of Vibration and Control, vol. 24, no. 19, pp. 4433-4448, 2018. https://doi.org/10.1177/1077546317726453
  30. Kalani et al., "Using artificial neural network models and particle swarm optimization for manner prediction of a photovoltaic thermal nanofluid based collector," Applied Thermal Engineering, vol. 113, pp. 1170-1177, 2017. https://doi.org/10.1016/j.applthermaleng.2016.11.105
  31. Kalani et al., "Forward kinematics solution of stewart-gough using improved hybrid strategy (neural network and 3rd-order Newton-Raphson," Journal of Computational Methods in Engineering, vol. 35, no. 2, pp. 113-129, 2022. https://doi.org/10.18869/acadpub.jcme.35.2.113
  32. Sadollah and J. Riazat, "Parameter Optimization of Various Solar Cell Models by Neural Network Algorithm," Journal Of Applied and Computational Sciences in Mechanics, vol. 36, no. 1, pp. 123-142, 2024. https://doi.org/10.22067/jacsm.2023.81702.1176
  33. Zamani mohiabadi et al., "Flow prediction in two-dimensional asymmetric diffuser by neural network and comparison with three turbulence models and experimental data," Journal Of Applied and Computational Sciences in Mechanics, vol. 34, no. 2, pp. 45-60, 2022. https://doi.org/10.22067/jacsm.2022.74041.1076
  34. J. Salmani et al., "Predicting the performance of organic solvent reverse osmosis membranes using artificial neural network and principal component analysis by considering solvent–solvent and solvent–membrane affinities," Journal of Membrane Science, vol. 687, p. 122025, 2023. https://doi.org/10.1016/j.memsci.2023.122025
  35. Jalaei Salmani et al., "A practical hybrid machine learning method for predicting the flash point of complex ternary alcohol-based mixtures," Petroleum Science and Technology, vol. 42, no. 25, pp. 4390-4404, 2024. https://doi.org/10.1080/10916466.2023.2246489
  36. Kalani et al., "Multi-objective genetic algorithm and Castigliano’s theorem for stiffness optimisation of parallel robots: case study of conventional Stewart platforms," Australian Journal of Mechanical Engineering, vol. 22, no. 4, pp. 797-809, 2024. https://doi.org/10.1080/14484846.2023.2174281
  37. Kalani and A. Akbarzadeh, "Multiple-objective Optimization of Serpentine Locomotion with Snake Robot by Using the NSGA," 2012.
  38. Beynaghi et al., "Analysis of fracture behavior of carbon nitride poly crystalline by genetic algorithm and molecular dynamics methods," Journal Of Applied and Computational Sciences in Mechanics, vol. 36, no. 2, pp. 29-46, 2024. https://doi.org/10.22067/jacsm.2023.83323.1192
  39. sargolzaie, A. tousi, and r. askari, "Optimization of one Stage Axial Compressor by using Quasi-Three Dimensional Design and Hybrid Genetic Algorithm," Journal Of Applied and Computational Sciences in Mechanics, vol. 26, no. 1, pp. 63-78, 2015.
  40. Delkhosh and M. Saadat Foumani, "Geometric Optimization of Half Toroidal Continuously Variable Transmission Using Intelligent Techniques," Journal Of Applied and Computational Sciences in Mechanics, vol. 23, no. 1, pp. 29-40, 2012. https://doi.org/10.22067/FUM-MECH.V23I1.12819
Send comment about this article
Enter Name.
Enter a valid email address.
Enter a vaid affiliation.
Enter comments (At leaset 10 words)
CAPTCHA Image
Enter Security Code Correctly.