AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection
Abstract
:1. Introduction
2. Related Works
2.1. The Diagnostic Methods Based on Traditional Probability Theory
2.2. Distributed Fault Detection Methods
2.3. The Diagnosis Method Based on Artificial Intelligence
2.4. Other Diagnostic Methods
3. The Method Based on AF-DHNN
3.1. Selection of Two Features for Detected Signals
3.1.1. FRMS
3.1.2. NSDS
3.2. Fuzzy Inference Operator
3.2.1. Basic Design
Output State | Flue Gas Dimming Extent | Ambient Temperature | Communication Module | Node State |
---|---|---|---|---|
normal | A1 | B1 | C1 | D1 |
abnormal | A2 | B2 | C2 | D2, D3, D4 |
- A1——Smoke sensing module is normal;
- A2——Smoke sensing module is abnormal;
- B1——Temperature sensing module is normal;
- B2——Temperature sensing module is abnormal;
- C1——Enable the main communication module;
- C2——Enable the standby communication module;
- D1——Overall state of node is normal;
- D2——Smoke sensing module fault;
- D3——Temperature sensing module fault;
- D4——Node main communication module fault, switching to standby communication module.
3.2.2. Membership Function and Rules
- R1: IF x is A1 and y is B1 and z is C1 THEN s is D1
- R2: IF x is A1 and y is B1 and z is C1 THEN s is D2
- R3: IF x is A2 and y is B1 and z is C1 THEN s is D3
- R4: IF z is C2 THEN s is D4
3.2.3. Normalization
3.2.4. Output Membership Function
3.3. FCMA Operator
Algorithm 1. Sorting and classification algorithm. | |
Inputs: Si, j, i = 1, 2, … n, j = 1, 2, … ccl E = {eγi} Outputs: Refresh Si, j, O1 Initialize: i, j | |
1. | Si, j = , i = 1, 2, … n, j = 1, 2, … ccl |
2. | Establish grades standard: |
3. | |
4. | |
5. | |
6. | Classified the data: |
7. | For 1 ≤ j ≤ ccl; For 1 ≤ i ≤ n |
8. | |
9. |
3.4. Discrete Hopfield Network
3.5. Output and Feedback
3.6. Implementation of the Algorithm
4. Simulation and Analysis Results
4.1. Network Structure
4.2. Sample Parameters
4.3. Conclusion Analysis
4.3.1. Fuzzy Input
- ;
- ;
- ;
- .
4.3.2. Diagnostic Method Performance Comparison
Grade | Flue Gas Dimming Extent Grade | Flue Gas Dimming Extent | Temperature Grade | Temperature | Main Communication Grade | Main Communication |
---|---|---|---|---|---|---|
L1 | 0.9482 | 0.9482 | 0.9845 | |||
L2 | 0.7898 | 0.7897 | 0.9018 | |||
L3 | 0.5516 | 0.3192 | 0.5973 | |||
L4 | 0.1680 | 0.3143 | 0.0607 | |||
L5 | 0 | 0 | 0 |
Grade | Flue Gas Dimming Extent Grade | Flue Gas Dimming Extent | Temperature Grade | Temperature | Main Communication Grade | Main Communication |
---|---|---|---|---|---|---|
L1 | 15 | 15 | 20 | |||
L2 | 29 | 29 | 20 | |||
L3 | 34 | 31 | 31 | |||
L4 | 10 | 6 | 13 | |||
L5 | 12 | 19 | 16 |
Project | The Actual Number of Failure | AF-DHNN | Data Change Rate | PSO | ||||||
---|---|---|---|---|---|---|---|---|---|---|
The Accuracy Rate of Diagnosis | Maintenance Number (Piece) | The Time of Diagnosis (s) | The Accuracy Rate of Diagnosis | Maintenance Number (Piece) | The Time of Diagnosis (s) | The Accuracy Rate of Diagnosis | Maintenance Number (Piece) | The Time of Diagnosis (s) | ||
Photoelectric smoke module | 20 | 100% | 22 | 10 | 80% | 28 | 100 | 85% | 32 | 10 |
Temperature sensing module | 23 | 95.65% | 25 | 10 | 73.91% | 44 | 100 | 86.95% | 47 | 10 |
The main communication module | 26 | 100% | 29 | 10 | 76.92% | 32 | 100 | 76.92% | 35 | 10 |
Node | 35 | 97.14% | 40 | 13.3 | 77.14% | 53 | 124.2 | 82.85% | 55 | 15.7 |
4.3.3. Performance Comparison in a Fire
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Jin, S.; Cui, W.; Jin, Z.; Wang, Y. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection. Sensors 2015, 15, 17366-17396. https://doi.org/10.3390/s150717366
Jin S, Cui W, Jin Z, Wang Y. AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection. Sensors. 2015; 15(7):17366-17396. https://doi.org/10.3390/s150717366
Chicago/Turabian StyleJin, Shan, Wen Cui, Zhigang Jin, and Ying Wang. 2015. "AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection" Sensors 15, no. 7: 17366-17396. https://doi.org/10.3390/s150717366
APA StyleJin, S., Cui, W., Jin, Z., & Wang, Y. (2015). AF-DHNN: Fuzzy Clustering and Inference-Based Node Fault Diagnosis Method for Fire Detection. Sensors, 15(7), 17366-17396. https://doi.org/10.3390/s150717366