Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors
Abstract
:1. Introduction
2. Experimental Facilities
2.1. Test Rig
2.2. Fault Simulation
3. Analysis of Temperature Change of Reciprocating Compressor
3.1. Temperature Change Characteristics of Reciprocating Compressor Based on Pseudo-Color Analysis
3.2. Fault Type Classification Using Support Vector Machine (SVM)
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Fault Modes | Frame Rate | Duration | Number of Images | Ambient Temperature (°C) |
---|---|---|---|---|
BL | 4.04 frame/s | 10′22″ | 2515 | 23.5 |
AFB | 4.09 frame/s | 10′04″ | 2393 | 23.1 |
ASW | 3.92 frame/s | 10′10″ | 2469 | 20.5 |
DVL | 3.02 frame/s | 10′31″ | 1907 | 24.7 |
Region | Hue Range | Saturation Range | Value Range |
---|---|---|---|
moto | —— | —— | >0.8 |
1st inlet | <0.1 | —— | —— |
1st outlet | >0.95 | —— | —— |
Cooling inlet | <0.1 | —— | —— |
Cooling outlet | <0.2 | —— | >0.7 |
2nd outlet | <0.09 | <0.6 | —— |
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Deng, R.; Lin, Y.; Tang, W.; Gu, F.; Ball, A. Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors. Sensors 2020, 20, 3436. https://doi.org/10.3390/s20123436
Deng R, Lin Y, Tang W, Gu F, Ball A. Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors. Sensors. 2020; 20(12):3436. https://doi.org/10.3390/s20123436
Chicago/Turabian StyleDeng, Rongfeng, Yubin Lin, Weijie Tang, Fengshou Gu, and Andrew Ball. 2020. "Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors" Sensors 20, no. 12: 3436. https://doi.org/10.3390/s20123436
APA StyleDeng, R., Lin, Y., Tang, W., Gu, F., & Ball, A. (2020). Object-Based Thermal Image Segmentation for Fault Diagnosis of Reciprocating Compressors. Sensors, 20(12), 3436. https://doi.org/10.3390/s20123436