Analysis of the Time Series of Compressed Air Flow and Pressure and Determining Criteria for Diagnosing Causes of Pressure Drop in Pneumatic Systems
Source abstract
Abstract
This article explores the possibility of diagnosing unwanted pressure drops in pneumatic systems. The proposed algorithm aims to distinguish the causes and location of their occurrence. The diagnostics clearly distinguish pressure drops caused by supply lines from those caused in the main or branch lines of an industrial pneumatic system. Pressure drops in pneumatic systems are one of the main causes of increased energy consumption. For the energy and resource optimization of pneumatic systems, it is essential to detect and locate the causes of pressure drops. This article proposes an approach for using the time diagrams of two measurable variables—flow rate and pressure—at the inlet of the end consumer (machine). Based on constant monitoring and a correlation relationship between the two time series, we determined indicators for detecting and locating unwanted pressure drops. In order to verify the proposed approach and the performed analysis, in general, we made observations of 16 real production machines and lines.
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Research context
OpenAlex topics: Engineering Technology and Methodologies · Engineering Education and Technology · Advanced Sensor Technologies Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Control Systems, Technical University–Sofia, Branch Plovdiv, 4000 Plovdiv, Bulgaria
Center of Competence “Smart Mechatronic, Eco-and Energy-Saving Systems and Technologies”, 4000 Plovdiv, Bulgaria
Vocational Training Center TRAKIYA, 4000 Plovdiv, Bulgaria
Funding
This research was funded by the European Regional Development Fund within the OP “Research, Innovation and Digitalization Programme for Intelligent Transformation 2021–2027”
References (48)
Show source-provided references
1. Blaustein, E., and Radgen, P. (2001). Compressed Air Systems in the European Union, LOG_X Verlag GmbH.
2. System Analysis and Opportunities for Optimization of Pneumatic Systems in Manufacturing Plants10.18421/TEM83-08 ↗
3. Werner, K., Löbbe, S., Büttner, S., and Schneider, C. (2020). Establishing Energy Efficiency—Drivers for Energy Efficiency in German Manufacturing Small- and Medium-Sized Enterprises. Energies, 13.10.3390/en13195144 ↗
4. Implementation of energy efficiency measures in compressed air systems: Barriers, drivers and non-energy benefits10.1007/s12053-018-9647-3 ↗
5. Data Mining and Analytics in the Process Industry: The Role of Machine Learning10.1109/ACCESS.2017.2756872 ↗
6. Review on data-driven modeling and monitoring for plant-wide industrial processes10.1016/j.chemolab.2017.09.021 ↗
7. Cesarotti, V., Orazi, S.D., and Introna, V. (2010, January 11–13). Improve Energy Efficiency in Manufacturing Plants through Consumption Forecasting and Real Time Control: Case Study from the Pharmaceutical Sector. Proceedings of the International Conference on Advances in Production Management Systems (APMS 2010), Cernobbio, Italy.
8. Fault-diagnosis for reciprocating compressors using big data and machine learning10.1016/j.simpat.2017.10.005 ↗
9. Magoulès, F., and Zhao, H.-X. (2016). Data Mining and Machine Learning in Building Energy Analysis, John Wiley & Sons.10.1002/9781118577691 ↗
10. A robust online fault detection and diagnosis strategy of centrifugal chiller systems for building energy efficiency10.1016/j.enbuild.2015.09.044 ↗
11. A fault detection and diagnosis strategy with enhanced sensitivity for centrifugal chillers10.1016/j.applthermaleng.2011.07.047 ↗
12. López, A.J.G., Márquez, A.C., Macchi, M., and Fernández, J.F.G. (2018). Prognostics and Health Management in Advanced Maintenance Systems. Advanced Maintenance Modeling for Asset Management, Springer.10.1007/978-3-319-58045-6_4 ↗
13. Optimization of a network of compressors in parallel: Real Time Optimization (RTO) of compressors in chemical plants—An industrial case study10.1016/j.apenergy.2015.01.010 ↗
14. Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment10.1016/j.procir.2014.02.001 ↗
15. From energy targets setting to energy-aware operations control and back: An advanced methodology for energy efficient manufacturing10.1016/j.jclepro.2016.09.213 ↗
16. Implementing multivariate statistics-based process monitoring: A comparison of basic data modeling approaches10.1016/j.neucom.2018.02.051 ↗
17. Xiao, W. (2016). A probabilistic machine learning approach to detect industrial plant faults. Int. J. Progn. Health Manag., 7.10.36001/ijphm.2016.v7i1.2360 ↗
18. Benedetti, M., Bonfà, F., Introna, V., and Santolamazza, A. (2019). Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications. Energies, 12.10.3390/en12203935 ↗
19. Semitela, Â., Silva, J., Girão, A.F., Verdasca, S., Futre, R., and Lau, N. (2025). Combining Infrared Thermography with Computer Vision Towards Automatic Detection and Localization of Air Leaks. Sensors, 25.10.3390/s25113272 ↗
20. Boyko, V., and Weber, J. (2024). Cycle Time-Based Fault Detection and Localization in Pneumatic Drive Systems. Actuators, 13.10.3390/act13110447 ↗
21. Expert estimation of leakage in the pneumatic systems in the bottling industry10.1088/1757-899X/878/1/012008 ↗
22. Leak detection using cepstrum of cross-correlation of transient pressure wave signals10.1177/0954406217722805 ↗
23. Diagnosis of Pneumatic Systems on the Basis of Time Series and Generalized Feature for Comparison with Standards for Normal Working Condition10.18421/TEM101-23 ↗
24. Paramonov, A., and Starikov, A. (2011). Air Supply Systems for Enterprises, Lan. (In Russian).
25. Study of the electricity consumption reduction of a compressed air system: The case of a steelmaking company10.1590/0370-44672015700189 ↗
26. Bertoldi, P. (2018). Energy Efficiency in Compressed Air Systems: A review of energy efficiency potentials, technological development, energy policy actions and future importance. Proceedings of the 10th International Conference on Energy Efficiency in Motor Driven Systems (EEMODS’2017), International Con-ference on Energy Efficiency in Motor Driven Systems EEMODS, Rome, Italy, 6–8 September 2017, Publications Office of the European Union.
27. Automatic measurement system for determination of leakage flow rate in compressed air pipeline system
28. A practical approach to investigating energy consumption of industrial compressed air systems10.1177/0957650911423173 ↗
29. Harman, A., Baur, L., Wolf, C., and Sauer, A. (2024, January 20–22). Detection of Anomalies in Compressed Air Systems using Time of Occurrence and KPI Analysis. Proceedings of the 5th International Conference on Communications, Information, Electronic and Energy Systems (CIEES), Veliko Tarnovo, Bulgaria.10.1109/CIEES62939.2024.10811145 ↗
30. Thabet, M., Sanders, D.A., and Bausch, N. (2021). Detection of Patterns in Pressure Signal of Compressed Air System Using Wavelet Transform. Energy and Sustainable Futures, Proceedings of the 2nd ICESF 2020, Hatfield, UK, 10–11 September 2020, Springer.10.1007/978-3-030-63916-7_8 ↗
31. Digital twin for a real-time leakage detection and localization in pressurized piping systems10.2166/hydro.2025.304 ↗
32. Tiboni, M., and Remino, C. (2024). Condition Monitoring of Pneumatic Drive Systems Based on the AI Method Feed-Forward Backpropagation Neural Network. Sensors, 24.10.3390/s24061783 ↗
33. Mueller, J., Boyle, W.C., and Popel, I.H.J. (2002). Aeration: Principles and Practice, CRC Press. [1st ed.].10.1201/9781420010343 ↗
34. Modeling and Simulation of Air Compressor Energy Use
35. Perevoshchikov, S.I. (1996). Design and Operation of Compressor Stations, TyumGNTU.
36. Popov, G., Nikolov, G., Boyadzhiev, M., and Klimentov, K. (2025, January 15). Investigation of errors in calculating friction coefficients in round pipes, Yearbook of the University of Mining and Geology “St. Ivan Rilski”, 2010, Volume 53, is. I. (In Bulgarian).
37. Scaling Laws for Fully Developed Turbulent Flow in Pipes: Discussion of Experimental Data10.1073/pnas.94.3.773 ↗
38. Marcus, R.D., Leung, L.S., Klinzing, G.E., and Rizk, F. (1990). Single Phase Flow in Pneumatic Conveying Systems of Solids, Springer.10.1007/978-94-009-0405-7 ↗
39. SMC Corporation (2013). Energy Saving Manual. SMC Corporation.
40. (2020, November 01). Engineering ToolBox, Compressed Air—Pressure Loss in Pipe Lines—Online Calculator with Metric and Imperial Units. Available online: https://www.engineeringtoolbox.com/pressure-drop-compressed-air-pipes-d_852.html.
41. Atlas Copco (1975). Atlas Copco Air Compendium, Atlas Sopco AB.
42. Rodgers, J.L., and Nicewander, W.A. (1988). Thirteen Ways to Look at the Correlation Coefficient. The American Statistician, American Statistical Association.10.2307/2685263 ↗
43. Hahs-Vaughn, D. (2023). Foundational methods: Descriptive statistics: Bivariate and multivariate data (correlations, associations). International Encyclopedia of Education, Elsevier.10.1016/B978-0-12-818630-5.10084-3 ↗
44. Process simulation of energy behaviour of pneumatic drives10.1016/j.proeng.2015.06.018 ↗
45. Taylor, B. (2025, June 10). What Is Compressed Air Pressure Drop and How Do You Minimize It. Fluid-Aire Dynamics Ltd. 21 September 2021. Available online: https://fluidairedynamics.com/blogs/articles/what-is-pressure-drop-and-how-its-costing-you-money#:~:text=An%20efficient%20compressed%20air%20system%20should%20lose,not%20receive%20enough%20pressure%20to%20operate%20correctly.
46. Energy Savings Measures in Compressed Air Systems10.32479/ijeep.9059 ↗
47. Oster, C. (2025, June 10). Detecting and Minimizing Air Pressure Drop in Compressed Air Systems. JHFOSTER Ltd. 2 April 2025. Available online: https://jhfoster.com/automation-blogs/what-is-a-pressure-drop-and-how-do-you-prevent-it/.
48. Taylor, B. (2025, June 10). Air Compressor Troubleshooting: Pressure Problems. Fluid-Aire Dynamics, 4 July 2023. Available online: https://fluidairedynamics.com/blogs/articles/air-compressor-troubleshooting-pressure-problems.