Е-Публикации
Технически университет - София

Abstract: Short industrial compressed-air audits can contain thousands of samples while representing only a few dependent operating episodes. Autocorrelation therefore makes nominal sample count an unreliable basis for deciding whether a low-flow regime is sufficiently supported for an energy-efficiency or maintenance action. This paper proposes an audit-to-decision procedure separating three claims: that a low-flow regime exists, that it is measured reliably enough, and that its relative scale justifies action. hv-block selection and minimum-dwell reconstruction are combined with episode repeatability, effective intra-regime size, resampling uncertainty, metrological distinguishability, and relative non-productive consumption in a six-level action scale (D1–D6). The procedure was applied to 29 records from three operating plants. Five PLC-labeled records provided a limited external evaluation of the operational meaning of the identified low-flow state. Nominal BIC and hv-block selection differed in 20 of 24 audit records. Median deviation fell from 32.2–33.9% for nominal-selection baselines to 2.9% when the HMM used the hv-block-selected regime count. Outcomes were 3 × D1, 15 × D2, 7 × D3, and 4 × D4. D-codes were unchanged in 23/24 threshold-tested and 14/16 pressure-augmented records. In the five-record PLC-labeled evaluation, no action unsupported by the labels was triggered. The strongest time-series-only outcome is a mandatory diagnostic work order, not an automatic repair decision.

References

1. Benedetti M. Bonfà F. Introna V. Santolamazza A. Ubertini S. Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications Energies 2019 12 3935 10.3390/en12203935
2. Vyas V. Jeon H.-W. Wang C. An Integrated Energy Simulation Model of a Compressed Air System for Sustainable Manufacturing: A Time-Discretized Approach Sustainability 2021 13 10340 10.3390/su131810340
3. Dindorf R. Takosoglu J. Wos P. Review of Compressed Air Receiver Tanks for Improved Energy Efficiency of Various Pneumatic Systems Energies 2023 16 4153 10.3390/en16104153
4. Zaim A. Industrial Compressed Air System Optimization: Experimental Evaluation of Energy Efficiency and Sustainability Gains Processes 2025 13 3590 10.3390/pr13113590
5. Trianni A. Accordini D. Cagno E. Identification and Categorization of Factors Affecting the Adoption of Energy Efficiency Measures within Compressed Air Systems Energies 2020 13 5116 10.3390/en13195116
6. Hanifi S. Alkali B. Lindsay G. McGlinchey D. Optimizing Energy and Air Consumption in Smart Manufacturing: An Industrial Internet of Things-Based Monitoring and Efficiency Enhancement Solution Appl. Sci. 2025 15 3222 10.3390/app15063222
7. Mousavi S. Kara S. Kornfeld B. Energy Efficiency of Compressed Air Systems Procedia CIRP 2014 15 313 318 10.1016/j.procir.2014.06.026
8. Borg M. Refalo P. Francalanza E. Pneumatic Fault Monitoring and Control for Sustainable Compressed Air Systems Procedia CIRP 2024 122 217 222 10.1016/j.procir.2024.01.032
9. Borg M. Refalo P. Francalanza E. Fault Condition Indicators along the Demand Side of a Sustainable Compressed Air System Procedia CIRP 2024 126 283 288 10.1016/j.procir.2024.08.340
10. Boyko V. Weber J. Cycle Time-Based Fault Detection and Localization in Pneumatic Drive Systems Actuators 2024 13 447 10.3390/act13110447
11. Martin Nascimento G.F. Wurtz F. Kuo-Peng P. Delinchant B. Batistela N.J. Quantifying Compressed Air Leakage through Non-Intrusive Load Monitoring Techniques in the Context of Energy Audits Energies 2022 15 3213 10.3390/en15093213
12. Borg M. Refalo P. Francalanza E. Failure Detection Techniques on the Demand Side of Smart and Sustainable Compressed Air Systems: A Systematic Review Energies 2023 16 3188 10.3390/en16073188
13. Mallia J. Francalanza E. Xuereb P. Refalo P. Intelligent Approaches for Anomaly Detection in Compressed Air Systems: A Systematic Review Machines 2023 11 750 10.3390/machines11070750
14. Abela K. Refalo P. Francalanza E. Analysis of Pneumatic Parameters to Identify Leakages and Faults on the Demand Side of a Compressed Air System Clean. Eng. Technol. 2022 6 100355 10.1016/j.clet.2021.100355
15. Titova T. Kosturkov R. Analysis of the Time Series of Compressed Air Flow and Pressure and Determining Criteria for Diagnosing Causes of Pressure Drop in Pneumatic Systems Appl. Sci. 2025 15 9536 10.3390/app15179536
16. Titova T. Kosturkov R. Nachev V. Diagnosis and Localization of Leaks in Industrial Compressed Air Systems Using the Dynamic Time Warping (DTW) Time Series Analysis Method Appl. Sci. 2026 16 4913 10.3390/app16104913
17. Kapan S. Energy Saving Potential and Machine Learning-Based Prediction of Compressed Air Leakages in Sustainable Manufacturing Sustainability 2026 18 904 10.3390/su18020904
18. Jeon S.H. Yoo S. Yoo Y.-S. Lee I.-W. ML- and LSTM-Based Radiator Predictive Maintenance for Energy Saving in Compressed Air Systems Energies 2024 17 1428 10.3390/en17061428
19. Barakat N. Hajeir L. Alattal S. Hussein Z. Awad M.M. Air Leaks Fault Detection in Maintenance Using Machine Learning J. Qual. Maint. Eng. 2024 30 391 408 10.1108/JQME-02-2023-0016
20. Borg M. Refalo P. Francalanza E. Multi-System and Multi-Fault Evaluation towards Generalisable Fault Monitoring for Efficient Pneumatic Systems: A Measurement-Based Approach Measurement 2026 258 118992 10.1016/j.measurement.2025.118992
21. Huang C. Bu S. Lee H.H. Chan C.H. Kong S.W. Yung W.K.C. Prognostics and Health Management for Predictive Maintenance: A Review J. Manuf. Syst. 2024 75 78 101 10.1016/j.jmsy.2024.05.021
22. Mallioris P. Aivazidou E. Bechtsis D. Predictive Maintenance in Industry 4.0: A Systematic Multi-Sector Mapping CIRP J. Manuf. Sci. Technol. 2024 50 80 103 10.1016/j.cirpj.2024.02.003
23. Neupane D. Bouadjenek M.R. Dazeley R. Aryal S. Data-Driven Machinery Fault Diagnosis: A Comprehensive Review Neurocomputing 2025 627 129588 10.1016/j.neucom.2025.129588
24. Benhanifia A. Ben Cheikh Z. Oliveira P.M. Valente A. Lima J. Systematic Review of Predictive Maintenance Practices in the Manufacturing Sector Intell. Syst. Appl. 2025 26 200501 10.1016/j.iswa.2025.200501
25. McLachlan G.J. Peel D. Finite Mixture Models Wiley Series in Probability and Statistics John Wiley & Sons New York, NY, USA 2000 10.1002/0471721182
26. Schwarz G. Estimating the Dimension of a Model Ann. Stat. 1978 6 461 464 10.1214/aos/1176344136
27. Ruiz-Suarez S. Leos-Barajas V. Morales J.M. Hidden Markov and Semi-Markov Models: When and Why Are These Models Useful for Classifying States in Time Series Data? J. Agric. Biol. Environ. Stat. 2022 27 339 363 10.1007/s13253-021-00483-x
28. Racine J. Consistent Cross-Validatory Model-Selection for Dependent Data: hv-Block Cross-Validation J. Econom. 2000 99 39 61 10.1016/S0304-4076(00)00030-0
29. Cerqueira V. Torgo L. Mozetič I. Evaluating Time Series Forecasting Models: An Empirical Study on Performance Estimation Methods Mach. Learn. 2020 109 1997 2028 10.1007/s10994-020-05910-7
30. Bergmeir C. Benítez J.M. On the Use of Cross-Validation for Time Series Predictor Evaluation Inf. Sci. 2012 191 192 213 10.1016/j.ins.2011.12.028
31. Varma S. Simon R. Bias in Error Estimation When Using Cross-Validation for Model Selection BMC Bioinform. 2006 7 91 10.1186/1471-2105-7-91 16504092
32. Hastie T. Tibshirani R. Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd ed. Springer New York, NY, USA 2009 10.1007/978-0-387-84858-7
33. Künsch H.R. The Jackknife and the Bootstrap for General Stationary Observations Ann. Stat. 1989 17 1217 1241 10.1214/aos/1176347265
34. Hall P. Horowitz J.L. Jing B.-Y. On Blocking Rules for the Bootstrap with Dependent Data Biometrika 1995 82 561 574 10.1093/biomet/82.3.561
35. Politis D.N. White H. Automatic Block-Length Selection for the Dependent Bootstrap Econom. Rev. 2004 23 53 70 10.1081/ETC-120028836
36. Kacker R. Sommer K.-D. Kessel R. Evolution of Modern Approaches to Express Uncertainty in Measurement Metrologia 2007 44 513 529 10.1088/0026-1394/44/6/011
37. JCGM 100:2008 Evaluation of Measurement Data—Guide to the Expression of Uncertainty in Measurement Joint Committee for Guides in Metrology Sèvres, France 2008 10.59161/JCGM100-2008E
38. ISO 11011:2013 Compressed Air—Energy Efficiency—Assessment International Organization for Standardization Geneva, Switzerland 2013
39. ISO 50002-1:2025 Energy Audits—Requirements with Guidance for Use—Part 1: General Requirements International Organization for Standardization Geneva, Switzerland 2025
40. CS Instruments GmbH & Co. KG VA 520—Compact Inline Flow Meter for Compressed Air and Gas: Technical Data Available online: https://www.cs-instruments.com/cs-data/Datenblaetter/Data%20sheets%20-%20EN/Data_sheet_VA520_EN.pdf (accessed on 27 July 2026)
41. CS Instruments GmbH & Co. KG VA 500—Flow Sensor for Compressed Air and Gases: Technical Data Available online: https://www.cs-instruments.com/cs-data/Datenblaetter/Data%20sheets%20-%20EN/Data_sheet_VA500_EN.pdf (accessed on 27 July 2026)
42. SMC Corporation ISE20/ISE20A Digital Pressure Sensor: Operation Manual Available online: https://static.smc.eu/binaries/content/assets/smc_global/product-documentation/installationmaintenance-manuals/en/im_z_ise20a_tf2z059-a.pdf (accessed on 28 July 2026)
43. CS Instruments GmbH & Co. KG DS 400 Mobile—Portable Chart Recorder with Integrated Data Logger: Technical Data Available online: https://www.cs-instruments.com/cs-data/Datenblaetter/Data%20sheets%20-%20EN/Data_sheet_DS400_mobile_EN.pdf (accessed on 31 July 2026)
44. Geyer C.J. Practical Markov Chain Monte Carlo Stat. Sci. 1992 7 473 483 10.1214/ss/1177011137
45. U.S. Department of Energy Improving Compressed Air System Performance: A Sourcebook for Industry 3rd ed. Office of Energy Efficiency and Renewable Energy Washington, DC, USA 2016 Available online: https://www.energy.gov/sites/default/files/2016/03/f30/Improving%20Compressed%20Air%20Sourcebook%20version%203.pdf (accessed on 31 July 2026)
46. da Cunha I.F. Compressed Air: Energy Efficiency Reference Guide CEA Technologies Inc. (CEATI) Montréal, QC, Canada 2007 Available online: https://natural-resources.canada.ca/energy-efficiency/energy-star/energy-efficiency-reference-guide-compressed-air (accessed on 15 September 2026)
47. Çerci Y. Çengel Y.A. Turner R.H. Reducing the Cost of Compressed Air in Industrial Facilities Thermodynamics and the Design, Analysis, and Improvement of Energy Systems ASME New York, NY, USA 1995 Volume 35 175 186
48. Çengel Y.A. Shiva Prasad B.G. Turner R.H. Çerci Y. Reduce Compressed Air Costs Hydrocarb. Process. 2000 79 57 58
49. Saidur R. Rahim N.A. Hasanuzzaman M. A Review on Compressed-Air Energy Use and Energy Savings Renew. Sustain. Energy Rev. 2010 14 1135 1153 10.1016/j.rser.2009.11.013
50. Çağman S. Soylu E. Ünver Ü. A Research on the Easy-to-Use Energy Efficiency Performance Indicators for Energy Audit and Energy Monitoring of Industrial Compressed Air Systems J. Clean. Prod. 2022 365 132698 10.1016/j.jclepro.2022.132698

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