Real-Time Weighted Data Fusion Algorithm for Temperature Detection Based on Small-Range Sensor Network
<p>Schematic of biological oxidation pretreatment.</p> "> Figure 2
<p>Temperature distribution of temperature in different wind field positions.</p> "> Figure 3
<p>Single-connected fusion structures.</p> "> Figure 4
<p>Multi-connected fusion structures.</p> "> Figure 5
<p>Layered fusion structure of sharing sensors.</p> "> Figure 6
<p>Sensor array of temperature measurement.</p> "> Figure 7
<p>Procedure of improved extended Kalman filtering (IEKF).</p> "> Figure 8
<p>Distributed multi-sensor data fusion.</p> "> Figure 9
<p>Simulation results of each level of the sensors and the contrast method.</p> "> Figure 10
<p>Absolute error of each level of the sensors and the contrast method.</p> "> Figure 11
<p>Simulation results of each sensor.</p> "> Figure 12
<p>Simulation results of sensor one, level-two sensors, and level-one sensors, and the contrast method.</p> "> Figure 13
<p>Absolute error of sensor one, level-two sensors, and level-one sensors, and the contrast method.</p> "> Figure 14
<p>Simulation results of each sensor.</p> "> Figure 15
<p>Experimental equipment diagram.</p> "> Figure 16
<p>Actual industrial site and data acquisition box of the reactor.</p> "> Figure 17
<p>Processing flow of the experimental data.</p> "> Figure 18
<p>Experimental result of each sensor.</p> "> Figure 19
<p>Experimental fusion result.</p> "> Figure 20
<p>MRE of experimental result.</p> ">
Abstract
:1. Introduction
2. Algorithm Description
2.1. Heat Transfer Model of Oxidation Tank
2.2. Fusion System of Biological Oxidation Pretreatment
2.3. Improvements of Data Processing
2.3.1. Iterative Operation of State Model
2.3.2. Multiple Fading Factors Based on Weighted Fading Memory Index
2.4. Distributed Data Fusion Method Based on Improved EKF
3. Simulation and Experiment
3.1. Simulation Results and Discussion
3.2. Experimental Setup
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Type | Error type | ||
---|---|---|---|
MAE (°C) | MRE (%) | RMSE (°C) | |
Contrast method based on single sensor with EKF | 10.6973 | 1.79 | 16.2528 |
Level-3 sensor fusion | 3.5261 | 0.59 | 4.1562 |
Level-2 sensor fusion | 3.4948 | 0.58 | 4.1297 |
Level-1 sensor fusion | 2.4361 | 0.41 | 3.5869 |
Type | Error type | ||
---|---|---|---|
MAE (°C) | MRE (%) | RMSE (°C) | |
Contrast method based on single sensor with EKF | 5.4830 | 0.92 | 10.6396 |
Sensor one of level-three sensor | 63.0346 | 10.56 | 70.2693 |
Level-two sensor fusion | 4.2876 | 0.72 | 5.8926 |
Level-one sensor fusion | 3.6103 | 0.61 | 4.9857 |
Performance index | Parameter |
---|---|
Brand | HACH (USA) P53A4A1N |
pH Range | −2.00–4.00 pH |
ORP Range | −2100 ± 2100 mV |
Temperature Range | −20.00 ± 200.00 °C |
Temperature Compensation | −10.00 ± 110.00 °C |
Accuracy | 0.2% of range every 24 h, no accumulation |
Precision | 0.1% of range or better |
Output | Two 0/4–20 mA DC RS232 |
Source | 190–60 VAC, 50/60 Hz |
Protection Degree | NEMA 4X (IP65), 1/2 DIN |
Calibration | Standard electrolyte |
Material | Thermal conductivity (W·m−1·°C−1) | Density (kg·m−3) | Specific heat (J·kg−1·°C−1) |
---|---|---|---|
Stainless steel | 16 | 7900 | 450 |
Air | 0.028 | 1.29 | 1030 |
Ore pulp | — | 1150 | 3290 |
Water | 0.64 | 1000 | 4186 |
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Zhang, Z.; Nan, X.; Wang, C. Real-Time Weighted Data Fusion Algorithm for Temperature Detection Based on Small-Range Sensor Network. Sensors 2019, 19, 64. https://doi.org/10.3390/s19010064
Zhang Z, Nan X, Wang C. Real-Time Weighted Data Fusion Algorithm for Temperature Detection Based on Small-Range Sensor Network. Sensors. 2019; 19(1):64. https://doi.org/10.3390/s19010064
Chicago/Turabian StyleZhang, Ziling, Xinyuan Nan, and Cong Wang. 2019. "Real-Time Weighted Data Fusion Algorithm for Temperature Detection Based on Small-Range Sensor Network" Sensors 19, no. 1: 64. https://doi.org/10.3390/s19010064