A Novel Chiller Sensors Fault Diagnosis Method Based on Virtual Sensors
Abstract
:1. Introduction
- In order to ensure the performance of virtual sensors, MIC is used to examine potentially interesting relationships between sensors. Chiller sensors with high MIC scores are divided into the same groups. This could dramatically improve the fitting effect of virtual sensors by constructing them in the same group.
- The performance of virtual sensors could be easily impacted by the input sensors. In order to reduce the false alarm rate, two virtual sensors that have different input sensors are constructed for the same physical sensor. When the two deviations between the corresponding physical sensor and the two virtual sensors both exceed the thresholds, the physical sensor is considered as a fault state.
- The LSTM model, which can better extract discriminating features from the sensor data, is used to construct the virtual sensors. It could further improve the fitting effect of virtual sensors.
2. Coupling Characteristic Analysis of the Air-Cooled Chiller System
3. Methods
3.1. Maximal Information Coefficient
3.2. Virtual Sensors
3.3. The Threshold and Procedure of Fault Diagnosis
- Step 1:
- A physical sensor is selected from eleven sensors. Two virtual sensors, which have been constructed in the training period, are used to predict the value of this physical sensor.
- Step 2:
- Deviations between virtual and physical sensors are calculated and compared with the fault diagnosis threshold.
- Step 3:
- Obviously, the physical sensor is not considered as a fault state when no deviations exceed the threshold. On the contrary, a sensor fault occurs when the deviations both exceed the fault diagnosis threshold. Input sensors are considered as a fault state if only one absolute deviation exceeds the threshold, because two virtual sensors have different input sensors. Under this situation, another physical sensor from input sensors is selected and step 2 will be repeated to predict the value of another physical sensor.
4. Results and Discussion
4.1. Experimental Data
4.2. Performance Comparison
4.2.1. Verification of Low False Alarm Rate
4.2.2. Fault Diagnosis Performance
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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No. | Sensors | Descriptions | Unit |
---|---|---|---|
1 | Compressor suction temperature | C | |
2 | Compressor discharge temperature | C | |
3 | Condenser-air temperature at the outlet | C | |
4 | Refrigerant temperature before throttling | C | |
5 | Refrigerant temperature after throttling | C | |
6 | Chilled-water supply temperature | C | |
7 | Chilled-water return temperature | C | |
8 | Compressor suction pressure | MPa | |
9 | Compressor discharge pressure | MPa | |
10 | Inlet pressure of the throttle device | MPa | |
11 | Outlet pressure of the throttle device | MPa |
1 | 0.239 | 0.264 | 0.279 | 0.432 | 0.850 | 0.881 | 0.267 | 0.271 | 0.618 | 0.832 | |
0.239 | 1 | 0.812 | 0.652 | 0.307 | 0.237 | 0.237 | 0.750 | 0.827 | 0.319 | 0.092 | |
0.264 | 0.812 | 1 | 0.836 | 0.249 | 0.305 | 0.299 | 0.922 | 0.898 | 0.255 | 0.160 | |
0.279 | 0.652 | 0.836 | 1 | 0.289 | 0.337 | 0.328 | 0.864 | 0.906 | 0.273 | 0.188 | |
0.432 | 0.307 | 0.249 | 0.289 | 1 | 0.348 | 0.422 | 0.255 | 0.263 | 0.906 | 0.805 | |
0.850 | 0.237 | 0.305 | 0.337 | 0.348 | 1 | 0.886 | 0.334 | 0.333 | 0.335 | 0.516 | |
0.881 | 0.237 | 0.299 | 0.328 | 0.422 | 0.886 | 1 | 0.325 | 0.328 | 0.431 | 0.499 | |
0.267 | 0.750 | 0.922 | 0.864 | 0.255 | 0.334 | 0.325 | 1 | 0.940 | 0.260 | 0.180 | |
0.271 | 0.827 | 0.898 | 0.906 | 0.263 | 0.333 | 0.328 | 0.940 | 1 | 0.262 | 0.183 | |
0.618 | 0.319 | 0.255 | 0.273 | 0.906 | 0.335 | 0.431 | 0.260 | 0.262 | 1 | 0.135 | |
0.832 | 0.092 | 0.160 | 0.188 | 0.805 | 0.516 | 0.499 | 0.180 | 0.183 | 0.135 | 1 |
Grouping Threshold | Group |
---|---|
0.8 | {, , , } {, , , } |
0.6 | {, , , , } {, , , , } |
0.3 | {, , , , , } {, , , , , , } |
Sensors | Unit | Biases |
---|---|---|
C | ±0.5, ±0.65, ±0.8, ±0.95 | |
C | ±1.5, ±1.9, ±2.3, ±2.7 | |
C | ±1.0, ±1.3, ±1.6, ±1.9 | |
C | ±1.0, ±1.3, ±1.6, ±1.9 | |
C | ±0.4, ±0.5, ±0.6, ±0.7 | |
C | ±0.4, ±0.5, ±0.6, ±0.7 | |
C | ±0.4, ±0.5, ±0.6, ±0.7 | |
MPa | ±0.035, ±0.04, ±0.045, ±0.05 | |
MPa | ±0.07, ±0.08, ±0.09, ±0.10 | |
MPa | ±0.07, ±0.08, ±0.09, ±0.10 | |
MPa | ±0.035, ±0.04, ±0.045, ±0.05 |
Only one virtual sensor | 35.0% | 18.0% | 26.0% | 52.0% | 44.0% | 27.0% | 39.0% | 11.0% | 23.0% | |
Two virtual sensors | 0.00% | 3.0% | 0.00% | 0.00% | 0.00% | 2.0% | 4.0% | 0.00% | 0.00% |
LR-based, LSTM-based | |||||||||
FC-based, LSTM-based |
LR-based | 1.37 | 0.89 | 0.87 | 0.42 | 0.44 | 0.38 | 0.061 | 0.057 | 0.032 | |
Positive | FC-based | 1.08 | 0.84 | 0.85 | 0.29 | 0.21 | 0.27 | 0.049 | 0.053 | 0.021 |
LSTM-based | 0.85 | 0.68 | 0.69 | 0.17 | 0.19 | 0.20 | 0.034 | 0.038 | 0.016 | |
LR-based | 1.41 | 0.82 | 0.89 | 0.33 | 0.47 | 0.39 | 0.067 | 0.062 | 0.035 | |
Negative | FC-based | 1.12 | 0.89 | 0.81 | 0.33 | 0.27 | 0.31 | 0.052 | 0.058 | 0.018 |
LSTM-based | 0.87 | 0.56 | 0.75 | 0.20 | 0.21 | 0.25 | 0.032 | 0.040 | 0.019 |
LR-based | 90.5% | 92.25% | 91.0% | 94.5% | 93.25% | 89.5% | 90.5% | 92.5% | 93.0% | 95.25% | 94.5% |
FC-based | 93.75% | 93.5% | 96.25% | 95.75% | 95.5% | 91.75% | 97.5% | 96.0% | 95.5% | 96.25% | 97.75% |
LSTM-based | 98% | 98.5% | 98.75% | 97.25% | 97.75% | 96.25% | 100.0% | 97.25% | 99.25% | 97.5% | 99.5% |
LR-based | 90.25% | 92.5% | 89.25% | 94.25% | 92.5% | 86.5% | 86.25% | 89.75% | 94.5% | 95.0% | 94.25% | |
FC-based | 91.25% | 96.75% | 92.25% | 96.0% | 95.5% | 92.25% | 93.5% | 94.0% | 96.75% | 97.5% | 96.75% | |
LSTM-based | 95.25% | 98.75% | 93.0% | 95.5% | 97.5% | 96.75% | 95.25% | 98.5% | 98.5% | 99.25% | 97.25% |
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Share and Cite
Gao, L.; Li, D.; Li, D.; Yao, L.; Liang, L.; Gao, Y. A Novel Chiller Sensors Fault Diagnosis Method Based on Virtual Sensors. Sensors 2019, 19, 3013. https://doi.org/10.3390/s19133013
Gao L, Li D, Li D, Yao L, Liang L, Gao Y. A Novel Chiller Sensors Fault Diagnosis Method Based on Virtual Sensors. Sensors. 2019; 19(13):3013. https://doi.org/10.3390/s19133013
Chicago/Turabian StyleGao, Long, Donghui Li, Ding Li, Lele Yao, Limei Liang, and Yanan Gao. 2019. "A Novel Chiller Sensors Fault Diagnosis Method Based on Virtual Sensors" Sensors 19, no. 13: 3013. https://doi.org/10.3390/s19133013
APA StyleGao, L., Li, D., Li, D., Yao, L., Liang, L., & Gao, Y. (2019). A Novel Chiller Sensors Fault Diagnosis Method Based on Virtual Sensors. Sensors, 19(13), 3013. https://doi.org/10.3390/s19133013