Fault Diagnosis of Reciprocating Compressor Valve Based on Triplet Siamese Neural Network
Abstract
:1. Introduction
2. Methodology
2.1. Establishment of Standard Reference for Logarithmic p-V Diagram
2.2. Preprocessing Method of Logarithmic p-V Diagram Difference
2.3. Feature Extraction for Logarithmic p-V Diagram Difference Sequence
2.4. Classification
3. Experimental Setup and Data Acquisition
4. Results and Discussion
4.1. Fault Classification Effect
4.2. Comparisons with Other Methods
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Possible Cases | Characteristic | Schematic Diagram |
---|---|---|
Easy triplet | parameters cannot update | |
Hard triplet | parameters can update | |
Semi-hard triplet | parameters can update |
Layer | Kernel Number | Kernel Size | Activation Function |
---|---|---|---|
convolutional layer | 128 | 5 | PReLU |
convolutional layer | 64 | 3 | PReLU |
pooling layer | - | 2 | - |
dropout layer | - | - | - |
convolutional layer | 32 | 3 | PReLU |
convolutional layer | 32 | 3 | PReLU |
pooling layer | - | 2 | - |
dropout layer | - | - | - |
linear layer | - | 1024 | PReLU |
linear layer | - | 512 | PReLU |
linear layer | - | 11 | - |
Name | Parameter |
---|---|
volume flow (m3/min) | 0.1 |
speed (rpm) | 688 |
inlet/outlet pressure (MPa) | 0/0.6 |
volume flow (m3/min) | 0.1 |
speed (rpm) | 688 |
inlet/outlet pressure (MPa) | 0/0.6 |
Failure Type | Simulation Measure | Detail | |
---|---|---|---|
leakage | valve plate trepanning | suction valve | rA * = 0.05% |
rA = 0.17% | |||
rA = 0.22% | |||
exhaust valve | rA = 0.1% | ||
rA = 0.15% | |||
rA = 0.20% | |||
blockage | catching adding | suction valve | add trepanning catching on valve seat |
exhaust valve | add trepanning catching on lift limiter | ||
spring failure | length changing | low elastic force | 10 mm spring truncated to 5 mm |
high elastic force | 10 mm spring is replaced with an 18 mm spring of the same material and diameter |
Valve Condition | Tag |
---|---|
normal | 1 |
severe leaking suction valve | 2 |
moderate leaking suction valve | 3 |
slight leaking suction valve | 4 |
blocked suction valve | 5 |
spring failure suction valve | 6 |
severe leaking exhaust valve | 7 |
moderate leaking exhaust valve | 8 |
slight leaking exhaust valve | 9 |
blocked exhaust valve | 10 |
spring failure exhaust valve | 11 |
Number | Logarithmic p-V Diagram Difference Sequence | p-V Diagram | ||||
---|---|---|---|---|---|---|
SNN | CNN | SVM | SNN | CNN | SVM | |
1 | 100 | 100 | 99.86 | 95.81 | 85.59 | 74.27 |
2 | 100 | 100 | 99.86 | 99.40 | 79.68 | 74.27 |
3 | 100 | 100 | 99.86 | 99.72 | 88.95 | 74.27 |
4 | 100 | 100 | 99.86 | 95.72 | 83.23 | 74.27 |
5 | 100 | 100 | 99.86 | 94.00 | 88.09 | 74.27 |
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Zhang, Z.; Wang, W.; Chen, W.; Xiao, Q.; Xu, W.; Li, Q.; Wang, J.; Liu, Z. Fault Diagnosis of Reciprocating Compressor Valve Based on Triplet Siamese Neural Network. Machines 2025, 13, 263. https://doi.org/10.3390/machines13040263
Zhang Z, Wang W, Chen W, Xiao Q, Xu W, Li Q, Wang J, Liu Z. Fault Diagnosis of Reciprocating Compressor Valve Based on Triplet Siamese Neural Network. Machines. 2025; 13(4):263. https://doi.org/10.3390/machines13040263
Chicago/Turabian StyleZhang, Zixuan, Wenbo Wang, Wenzheng Chen, Qiang Xiao, Weiwei Xu, Qiang Li, Jie Wang, and Zhaozeng Liu. 2025. "Fault Diagnosis of Reciprocating Compressor Valve Based on Triplet Siamese Neural Network" Machines 13, no. 4: 263. https://doi.org/10.3390/machines13040263
APA StyleZhang, Z., Wang, W., Chen, W., Xiao, Q., Xu, W., Li, Q., Wang, J., & Liu, Z. (2025). Fault Diagnosis of Reciprocating Compressor Valve Based on Triplet Siamese Neural Network. Machines, 13(4), 263. https://doi.org/10.3390/machines13040263