Analysis of the Time Series of Compressed Air Flow and Pressure and Determining Criteria for Diagnosing Causes of Pressure Drop in Pneumatic Systems
Abstract
1. Introduction
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- Reducing the speed and power of actuators;
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- Unpredictable production line shutdowns.
2. Materials and Methods
2.1. Correlation Analysis
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- Correlations between pairs of variables;
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- Correlations within and between sets of variables.
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- Whether there is a statistically significant relationship between two continuous variables;
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- The strength of the linear relationship (i.e., how close the relationship is to a perfectly straight line);
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- The direction of a linear relationship (increasing or decreasing).
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- Two or more continuous variables;
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- It is not permissible to have missing values for both variables;
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- Linear relationship between variables within the measurement range;
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- Independence of observations. The Pearson bivariate correlation coefficient and the corresponding significance test are not robust when independence is violated.
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- Bivariate normality:
- Each pair of variables is bivariately normally distributed;
- Each pair of variables is bivariately normally distributed at all levels of the other variable.
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- —the dependent variable Y is perfectly positively correlated with the independent variable X;
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- —this indicates a strong positive correlation of the dependent variable Y with the independent variable X;
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- —significant positive correlation of the dependent variable Y with the independent variable X;
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- —this indicates a moderate positive correlation of the dependent variable Y with the independent variable X;
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- —this indicates a weak positive correlation of the dependent variable Y with the independent variable X;
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- —what is considered the dependent variable Y does not have any kind of linear correlation with what is considered the independent variable X;
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- —this indicates a weak negative correlation of the dependent variable Y with the independent variable X;
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- —this indicates a moderate negative correlation of the dependent variable Y with the independent variable X;
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- —there is a significant negative correlation of the dependent variable Y with the independent variable X;
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- —this indicates a strong negative correlation of the dependent variable Y with the independent variable X;
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- —the dependent variable Y is perfectly negatively correlated with the independent variable X.
2.2. Diagnostic Criteria
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- A sign of a problem.
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- Indication of the problem being related to the supply line:
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- A sign of a structural or systemic problem in the design or implementation of the supply line—excessively large maximum theoretical pressure drop:
- 2.
- ;
- 3.
- ; ;.
- 4.
- ; ; .
3. Experimental Part
4. Results and Analyses
5. Discussion
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- The Pearson correlation coefficient between flow rate and end-user inlet pressure is a promising indicator for distinguishing whether pressure drop is due to supply line problems. This was confirmed both by actual troubleshooting of supply line problems on Machines 11 to 13 and by visual analysis of time diagrams on machines falling into Group 2. More real-world data are needed to empirically determine its threshold value more definitively;
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- Using the theoretical maximum pressure drop to separate the causes of problems in the supply line has practical application, but it requires additional knowledge of the supply line itself, and its parameters must be included in the calculations. From a practical point of view, it turns out that it is better for machines classified in Groups 3 and 4 to inspect the entire supply line and, if there are kinks, blockages, or leaks, to eliminate them. And, if after that, the pressure drop is still above the permissible level, optimize the internal diameter and length of the supply line;
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- The higher correlation coefficient above the specified threshold does not mean that the problem is only in the supply line. For example, in Machines 11 and 12, after eliminating the problems in the supply line, the pressure drop is still high, although within the specified permissible limits. However, searching for the causes of pressure drops in the main and branch lines is a significantly more complex task, often associated with stopping entire production sections. The proposed method can solve the problem to some extent without the need for unnecessary pressure increase in the compressor and the generation of artificial consumption.
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Model | SMC ISE20A | |
---|---|---|
Fluid | Applicable fluid | Compressed air, non-flammable gases, non-corrosive gases |
Temperature range | 0 to 50 °C | |
Pressure | Measurement range | −0.100 to 1.000 MPa |
Accuracy | Repeatability | ±0.2% FS |
Analog output | ±2.5% FS (ambient temperature: 25 ± 3 °C) | |
Analog output linearity | ±1% FS | |
Analog Output | Type | Current output: 4 to 20 mA |
Response time | 1.5 ms or less |
Model | CS VA 520 Standard | CS VA520 Max | |
---|---|---|---|
Fluid | Applicable fluid | Compressed air, N2; quality: ISO 8573-1 1.1.2 to 1.6.2. | |
Temperature range | −30 to 80 °C | ||
Measurement method | Thermal, in-line | ||
Fluid velocity | 92.7 m/s | 185 m/s | |
Pressure | Nominal working pressure | −0.1 to 1.6 MPa | |
Accuracy | Repeatability | ±1.5% of the measured value ± 0.3% FS | |
Analog output | Type | Current output: 4–20 mA | |
Response time | Minimum: 0.05 s. | ||
Digital output | RS 485 interface (Modbus-RTU), Ethernet, M-Bus |
Model | CS Instruments DS 500 Mobile | ||
---|---|---|---|
Sensor inputs | Up to 12 sensor inputs—digital or analog. Programmable Digital CS sensors for dew-point measurement with SDI interface. Flow sensors on CS. Other sensors with RS 485/Modbus RTU. Analog CS sensors for pressure, flow, temperature, etc. Other analog sensors with outputs 0/4–20 mA, 0–1/10/30 V, pulse outputs Pt100/Pt1000, and KTY. | ||
Input signals | Current signal (0–20 mA/4–20 mA) and internal or external power supply | Measurement range | 0–20 mA/4–20 mA |
Resolution | 0.0001 mA | ||
Accuracy | ±0.03 mA ±0.05% | ||
Input resistance | 50 Ω | ||
Sampling frequency | 10 ms, 100 Hz | ||
Non-volatile memory | 4 GB |
Machine | Pressure | Equivalent Length of the Supply Line | Inner Diameter of the Supply Line d | |
---|---|---|---|---|
[bar] | [m] | [mm] | ||
Flexible water connection plant | Machine 1- Sealant application | 8.4 | 9 | 6 |
Machine 2—Sealant application | 8.4 | 8 | 10 | |
Machine 3—Automatic shear | 8.5 | 6 | 10 | |
Machine 4—Pressing and molding | 8.4 | 7 | 16 | |
Machine 5—Cutting and sorting | 8.5 | 2.5 | 8 | |
Electronic components factory | Machine 6—Automatic soldering | 8.3 | 10 | 6 |
Machine 7—Assembly unit | 8.4 | 12 | 10 | |
Machine 8—Robotic assembly | 8.5 | 5 | 12 | |
Machine 9—Robotic assembly | 8.4 | 5 | 12 | |
Automobile hydraulic pump plant | Machine 10—Press | 6.7 | 3.5 | 8 |
Machine 11—Press | 6.8 | 4.5 | 8 | |
Machine 12—Automatic assembly | 6.8 | 10 | 10 | |
Machine 13—Automatic assembly and test in clean room | 5.7 | 5 | 10 | |
Household plastic products factory | Machine 14—Blast line | 7.3 | 5 | 50 |
Machine 15—Sorting and arranging | 7.3 | 15 | 20 | |
Machine 16—Packaging machine | 7.3 | 3 | 8 |
Machine | [bar] | |||
---|---|---|---|---|
[%] | [%] | |||
Machine 1 | 8.4 | 2.02% | 8.33% | −0.0431 |
Machine 2 | 8.4 | 1.19% | 4.76% | 0.3532 |
Machine 3 | 8.5 | 2.12% | 16.00% | −0.3900 |
Machine 4 | 8.4 | 1.43% | 4.76% | −0.2768 |
Machine 5 | 8.5 | 3.60% | 16.59% | −0.9508 |
Machine 6 | 8.3 | 31.58% | 36.88% | −0.8778 |
Machine 7 | 8.4 | 3.38% | 9.40% | −0.7046 |
Machine 8 | 8.5 | 2.92% | 11.41% | −0.4175 |
Machine 9 | 8.4 | 2.98% | 11.90% | 0.1652 |
Machine 10 | 6.7 | 16.17% | 17.91% | −0.9046 |
Machine 11 | 6.8 | 14.19% | 23.09% | −0.8759 |
Machine 12 | 6.8 | 4.42% | 21.76% | −0.7293 |
Machine 13 | 5.7 | 5.11% | 5.79% | −0.9321 |
Machine 14 | 7.3 | 0.34% | 19.81% | 0.3742 |
Machine 15 | 7.3 | 2.35% | 23.56% | −0.3939 |
Machine 16 | 7.3 | 4.18% | 29.59% | −0.8461 |
Initial State | After Correction | ||||||||
---|---|---|---|---|---|---|---|---|---|
PC | Equivalent Length of the Supply Line L | Inner Diameter of the Supply Line d | ∆Pmmax | ∆Pmmax/Pc | Equivalent Length of the Supply Line L | Inner Diameter of the Supply Line d | ∆Pmmax | ∆Pmmax/Pc | |
[bar] | [m] | [mm] | [bar] | [%] | [m] | [mm] | [bar] | [%] | |
Machine 10 | 6.7 | 5 | 8 | 1.2 | 17.91% | 5 | 13 | 0.2 | 2.99% |
Machine 11 | 6.8 | 4.5 | 8 | 1.57 | 23.09% | 5 | 13 | 0.3 | 4.41% |
Machine 12 | 6.8 | 10 | 10 | 1.48 | 21.76% | 10 | 13 | 0.3 | 4.41% |
Machine 13 | 5.7 | 5 | 10 | 0.33 | 5.79% | 5 | 13 | 0.1 | 1.75% |
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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. https://doi.org/10.3390/app15179536
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. Applied Sciences. 2025; 15(17):9536. https://doi.org/10.3390/app15179536
Chicago/Turabian StyleTitova, Tanya, and Rosen Kosturkov. 2025. "Analysis of the Time Series of Compressed Air Flow and Pressure and Determining Criteria for Diagnosing Causes of Pressure Drop in Pneumatic Systems" Applied Sciences 15, no. 17: 9536. https://doi.org/10.3390/app15179536
APA StyleTitova, T., & Kosturkov, R. (2025). Analysis of the Time Series of Compressed Air Flow and Pressure and Determining Criteria for Diagnosing Causes of Pressure Drop in Pneumatic Systems. Applied Sciences, 15(17), 9536. https://doi.org/10.3390/app15179536